Sensors & Transducers
Volume 144, Issue 9
September 2012
www.sensorsportal.com
ISSN 1726-5479
Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, e-mail: editor@sensorsportal.com
Editors for Western Europe
Meijer, Gerard C.M., Delft University of Technology, The Netherlands
Ferrari, Vittorio, Universitá di Brescia, Italy
Editors for North America
Datskos, Panos G., Oak Ridge National Laboratory, USA
Fabien, J. Josse, Marquette University, USA
Katz, Evgeny, Clarkson University, USA
Editor for Eastern Europe
Sachenko, Anatoly, Ternopil State Economic University, Ukraine
Editor for Asia
Ohyama, Shinji, Tokyo Institute of Technology, Japan
Editor for Africa
Maki K.Habib, American University in Cairo, Egypt
Editor for Asia-Pacific
Mukhopadhyay, Subhas, Massey University, New Zealand
Editor South America
Costa-Felix, Rodrigo, Inmetro, Brazil
Editorial Advisory Board
Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia
Ahmad, Mohd Noor, Nothern University of Engineering, Malaysia
Annamalai, Karthigeyan, National Institute of Advanced Industrial Science
and Technology, Japan
Arcega, Francisco, University of Zaragoza, Spain
Arguel, Philippe, CNRS, France
Ahn, Jae-Pyoung, Korea Institute of Science and Technology, Korea
Arndt, Michael, Robert Bosch GmbH, Germany
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Azamimi, Azian binti Abdullah, Universiti Malaysia Perlis, Malaysia
Bahreyni, Behraad, University of Manitoba, Canada
Baliga, Shankar, B., General Monitors Transnational, USA
Baoxian, Ye, Zhengzhou University, China
Barford, Lee, Agilent Laboratories, USA
Barlingay, Ravindra, RF Arrays Systems, India
Basu, Sukumar, Jadavpur University, India
Beck, Stephen, University of Sheffield, UK
Ben Bouzid, Sihem, Institut National de Recherche Scientifique, Tunisia
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Binnie, T. David, Napier University, UK
Bischoff, Gerlinde, Inst. Analytical Chemistry, Germany
Bodas, Dhananjay, IMTEK, Germany
Borges Carval, Nuno, Universidade de Aveiro, Portugal
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Bouvet, Marcel, CNRS – UPMC, France
Brudzewski, Kazimierz, Warsaw University of Technology, Poland
Cai, Chenxin, Nanjing Normal University, China
Cai, Qingyun, Hunan University, China
Calvo-Gallego, Jaime, Universidad de Salamanca, Spain
Campanella, Luigi, University La Sapienza, Italy
Carvalho, Vitor, Minho University, Portugal
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Chakrabarty, Chandan Kumar, Universiti Tenaga Nasional, Malaysia
Chakravorty, Dipankar, Association for the Cultivation of Science, India
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Chaudhari, Gajanan, Shri Shivaji Science College, India
Chavali, Murthy, N.I. Center for Higher Education, (N.I. University), India
Chen, Jiming, Zhejiang University, China
Chen, Rongshun, National Tsing Hua University, Taiwan
Cheng, Kuo-Sheng, National Cheng Kung University, Taiwan
Chiang, Jeffrey (Cheng-Ta), Industrial Technol. Research Institute, Taiwan
Chiriac, Horia, National Institute of Research and Development, Romania
Chowdhuri, Arijit, University of Delhi, India
Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan
Corres, Jesus, Universidad Publica de Navarra, Spain
Cortes, Camilo A., Universidad Nacional de Colombia, Colombia
Courtois, Christian, Universite de Valenciennes, France
Cusano, Andrea, University of Sannio, Italy
D'Amico, Arnaldo, Università di Tor Vergata, Italy
De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy
Deshmukh, Kiran, Shri Shivaji Mahavidyalaya, Barshi, India
Dickert, Franz L., Vienna University, Austria
Dieguez, Angel, University of Barcelona, Spain
Dighavkar, C. G., M.G. Vidyamandir’s L. V.H. College, India
Dimitropoulos, Panos, University of Thessaly, Greece
Ding, Jianning, Jiangsu Polytechnic University, China
Djordjevich, Alexandar, City University of Hong Kong, Hong Kong
Donato, Nicola, University of Messina, Italy
Donato, Patricio, Universidad de Mar del Plata, Argentina
Dong, Feng, Tianjin University, China
Drljaca, Predrag, Instersema Sensoric SA, Switzerland
Dubey, Venketesh, Bournemouth University, UK
Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, Germany
Erdem, Gursan K. Arzum, Ege University, Turkey
Erkmen, Aydan M., Middle East Technical University, Turkey
Estelle, Patrice, Insa Rennes, France
Estrada, Horacio, University of North Carolina, USA
Faiz, Adil, INSA Lyon, France
Fericean, Sorin, Balluff GmbH, Germany
Fernandes, Joana M., University of Porto, Portugal
Francioso, Luca, CNR-IMM Institute for Microelectronics and Microsystems, Italy
Francis, Laurent, University Catholique de Louvain, Belgium
Fu, Weiling, South-Western Hospital, Chongqing, China
Gaura, Elena, Coventry University, UK
Geng, Yanfeng, China University of Petroleum, China
Gole, James, Georgia Institute of Technology, USA
Gong, Hao, National University of Singapore, Singapore
Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain
Granel, Annette, Goteborg University, Sweden
Graff, Mason, The University of Texas at Arlington, USA
Guan, Shan, Eastman Kodak, USA
Guillet, Bruno, University of Caen, France
Guo, Zhen, New Jersey Institute of Technology, USA
Gupta, Narendra Kumar, Napier University, UK
Hadjiloucas, Sillas, The University of Reading, UK
Haider, Mohammad R., Sonoma State University, USA
Hashsham, Syed, Michigan State University, USA
Hasni, Abdelhafid, Bechar University, Algeria
Hernandez, Alvaro, University of Alcala, Spain
Hernandez, Wilmar, Universidad Politecnica de Madrid, Spain
Homentcovschi, Dorel, SUNY Binghamton, USA
Horstman, Tom, U.S. Automation Group, LLC, USA
Hsiai, Tzung (John), University of Southern California, USA
Huang, Jeng-Sheng, Chung Yuan Christian University, Taiwan
Huang, Star, National Tsing Hua University, Taiwan
Huang, Wei, PSG Design Center, USA
Hui, David, University of New Orleans, USA
Jaffrezic-Renault, Nicole, Ecole Centrale de Lyon, France
James, Daniel, Griffith University, Australia
Janting, Jakob, DELTA Danish Electronics, Denmark
Jiang, Liudi, University of Southampton, UK
Jiang, Wei, University of Virginia, USA
Jiao, Zheng, Shanghai University, China
John, Joachim, IMEC, Belgium
Kalach, Andrew, Voronezh Institute of Ministry of Interior, Russia
Kang, Moonho, Sunmoon University, Korea South
Kaniusas, Eugenijus, Vienna University of Technology, Austria
Katake, Anup, Texas A&M University, USA
Kausel, Wilfried, University of Music, Vienna, Austria
Kavasoglu, Nese, Mugla University, Turkey
Ke, Cathy, Tyndall National Institute, Ireland
Khelfaoui, Rachid, Université de Bechar, Algeria
Khan, Asif, Aligarh Muslim University, Aligarh, India
Kim, Min Young, Kyungpook National University, Korea South
Ko, Sang Choon, Electronics. and Telecom. Research Inst., Korea South
Kotulska, Malgorzata, Wroclaw University of Technology, Poland
Kockar, Hakan, Balikesir University, Turkey
Kong, Ing, RMIT University, Australia
Kratz, Henrik, Uppsala University, Sweden
Krishnamoorthy, Ganesh, University of Texas at Austin, USA
Kumar, Arun, University of Delaware, Newark, USA
Kumar, Subodh, National Physical Laboratory, India
Kung, Chih-Hsien, Chang-Jung Christian University, Taiwan
Lacnjevac, Caslav, University of Belgrade, Serbia
Lay-Ekuakille, Aime, University of Lecce, Italy
Lee, Jang Myung, Pusan National University, Korea South
Lee, Jun Su, Amkor Technology, Inc. South Korea
Lei, Hua, National Starch and Chemical Company, USA
Li, Fengyuan (Thomas), Purdue University, USA
Li, Genxi, Nanjing University, China
Li, Hui, Shanghai Jiaotong University, China
Li, Sihua, Agiltron, Inc., USA
Li, Xian-Fang, Central South University, China
Li, Yuefa, Wayne State University, USA
Liang, Yuanchang, University of Washington, USA
Liawruangrath, Saisunee, Chiang Mai University, Thailand
Liew, Kim Meow, City University of Hong Kong, Hong Kong
Lin, Hermann, National Kaohsiung University, Taiwan
Lin, Paul, Cleveland State University, USA
Linderholm, Pontus, EPFL - Microsystems Laboratory, Switzerland
Liu, Aihua, University of Oklahoma, USA
Liu Changgeng, Louisiana State University, USA
Liu, Cheng-Hsien, National Tsing Hua University, Taiwan
Liu, Songqin, Southeast University, China
Lodeiro, Carlos, University of Vigo, Spain
Lorenzo, Maria Encarnacio, Universidad Autonoma de Madrid, Spain
Lukaszewicz, Jerzy Pawel, Nicholas Copernicus University, Poland
Ma, Zhanfang, Northeast Normal University, China
Majstorovic, Vidosav, University of Belgrade, Serbia
Malyshev, V.V., National Research Centre ‘Kurchatov Institute’, Russia
Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados, Mexico
Matay, Ladislav, Slovak Academy of Sciences, Slovakia
Mathur, Prafull, National Physical Laboratory, India
Maurya, D.K., Institute of Materials Research and Engineering, Singapore
Mekid, Samir, University of Manchester, UK
Melnyk, Ivan, Photon Control Inc., Canada
Mendes, Paulo, University of Minho, Portugal
Mennell, Julie, Northumbria University, UK
Mi, Bin, Boston Scientific Corporation, USA
Minas, Graca, University of Minho, Portugal
Mishra, Vivekanand, National Institute of Technology, India
Moghavvemi, Mahmoud, University of Malaya, Malaysia
Mohammadi, Mohammad-Reza, University of Cambridge, UK
Molina Flores, Esteban, Benemérita Universidad Autónoma de Puebla,
Mexico
Moradi, Majid, University of Kerman, Iran
Morello, Rosario, University "Mediterranea" of Reggio Calabria, Italy
Mounir, Ben Ali, University of Sousse, Tunisia
Mrad, Nezih, Defence R&D, Canada
Mulla, Imtiaz Sirajuddin, National Chemical Laboratory, Pune, India
Nabok, Aleksey, Sheffield Hallam University, UK
Neelamegam, Periasamy, Sastra Deemed University, India
Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria
Oberhammer, Joachim, Royal Institute of Technology, Sweden
Ould Lahoucine, Cherif, University of Guelma, Algeria
Pamidighanta, Sayanu, Bharat Electronics Limited (BEL), India
Pan, Jisheng, Institute of Materials Research & Engineering, Singapore
Park, Joon-Shik, Korea Electronics Technology Institute, Korea South
Passaro, Vittorio M. N., Politecnico di Bari, Italy
Penza, Michele, ENEA C.R., Italy
Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal
Petsev, Dimiter, University of New Mexico, USA
Pogacnik, Lea, University of Ljubljana, Slovenia
Post, Michael, National Research Council, Canada
Prance, Robert, University of Sussex, UK
Prasad, Ambika, Gulbarga University, India
Prateepasen, Asa, Kingmoungut's University of Technology, Thailand
Pugno, Nicola M., Politecnico di Torino, Italy
Pullini, Daniele, Centro Ricerche FIAT, Italy
Pumera, Martin, National Institute for Materials Science, Japan
Radhakrishnan, S. National Chemical Laboratory, Pune, India
Rajanna, K., Indian Institute of Science, India
Ramadan, Qasem, Institute of Microelectronics, Singapore
Rao, Basuthkar, Tata Inst. of Fundamental Research, India
Raoof, Kosai, Joseph Fourier University of Grenoble, France
Rastogi Shiva, K. University of Idaho, USA
Reig, Candid, University of Valencia, Spain
Restivo, Maria Teresa, University of Porto, Portugal
Robert, Michel, University Henri Poincare, France
Rezazadeh, Ghader, Urmia University, Iran
Royo, Santiago, Universitat Politecnica de Catalunya, Spain
Rodriguez, Angel, Universidad Politecnica de Cataluna, Spain
Rothberg, Steve, Loughborough University, UK
Sadana, Ajit, University of Mississippi, USA
Sadeghian Marnani, Hamed, TU Delft, The Netherlands
Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia
Sandacci, Serghei, Sensor Technology Ltd., UK
Saxena, Vibha, Bhbha Atomic Research Centre, Mumbai, India
Schneider, John K., Ultra-Scan Corporation, USA
Sengupta, Deepak, Advance Bio-Photonics, India
Seif, Selemani, Alabama A & M University, USA
Seifter, Achim, Los Alamos National Laboratory, USA
Shah, Kriyang, La Trobe University, Australia
Sankarraj, Anand, Detector Electronics Corp., USA
Silva Girao, Pedro, Technical University of Lisbon, Portugal
Singh, V. R., National Physical Laboratory, India
Slomovitz, Daniel, UTE, Uruguay
Smith, Martin, Open University, UK
Soleimanpour, Amir Masoud, University of Toledo, USA
Soleymanpour, Ahmad, University of Toledo, USA
Somani, Prakash R., Centre for Materials for Electronics Technol., India
Sridharan, M., Sastra University, India
Srinivas, Talabattula, Indian Institute of Science, Bangalore, India
Srivastava, Arvind K., NanoSonix Inc., USA
Stefan-van Staden, Raluca-Ioana, University of Pretoria, South Africa
Stefanescu, Dan Mihai, Romanian Measurement Society, Romania
Sumriddetchka, Sarun, National Electronics and Comp. Technol. Center, Thailand
Sun, Chengliang, Polytechnic University, Hong-Kong
Sun, Dongming, Jilin University, China
Sun, Junhua, Beijing University of Aeronautics and Astronautics, China
Sun, Zhiqiang, Central South University, China
Suri, C. Raman, Institute of Microbial Technology, India
Sysoev, Victor, Saratov State Technical University, Russia
Szewczyk, Roman, Industr. Research Inst. for Automation and Measurement, Poland
Tan, Ooi Kiang, Nanyang Technological University, Singapore,
Tang, Dianping, Southwest University, China
Tang, Jaw-Luen, National Chung Cheng University, Taiwan
Teker, Kasif, Frostburg State University, USA
Thirunavukkarasu, I., Manipal University Karnataka, India
Thumbavanam Pad, Kartik, Carnegie Mellon University, USA
Tian, Gui Yun, University of Newcastle, UK
Tsiantos, Vassilios, Technological Educational Institute of Kaval, Greece
Tsigara, Anna, National Hellenic Research Foundation, Greece
Twomey, Karen, University College Cork, Ireland
Valente, Antonio, University, Vila Real, - U.T.A.D., Portugal
Vanga, Raghav Rao, Summit Technology Services, Inc., USA
Vaseashta, Ashok, Marshall University, USA
Vazquez, Carmen, Carlos III University in Madrid, Spain
Vieira, Manuela, Instituto Superior de Engenharia de Lisboa, Portugal
Vigna, Benedetto, STMicroelectronics, Italy
Vrba, Radimir, Brno University of Technology, Czech Republic
Wandelt, Barbara, Technical University of Lodz, Poland
Wang, Jiangping, Xi'an Shiyou University, China
Wang, Kedong, Beihang University, China
Wang, Liang, Pacific Northwest National Laboratory, USA
Wang, Mi, University of Leeds, UK
Wang, Shinn-Fwu, Ching Yun University, Taiwan
Wang, Wei-Chih, University of Washington, USA
Wang, Wensheng, University of Pennsylvania, USA
Watson, Steven, Center for NanoSpace Technologies Inc., USA
Weiping, Yan, Dalian University of Technology, China
Wells, Stephen, Southern Company Services, USA
Wolkenberg, Andrzej, Institute of Electron Technology, Poland
Woods, R. Clive, Louisiana State University, USA
Wu, DerHo, National Pingtung Univ. of Science and Technology, Taiwan
Wu, Zhaoyang, Hunan University, China
Xiu Tao, Ge, Chuzhou University, China
Xu, Lisheng, The Chinese University of Hong Kong, Hong Kong
Xu, Sen, Drexel University, USA
Xu, Tao, University of California, Irvine, USA
Yang, Dongfang, National Research Council, Canada
Yang, Shuang-Hua, Loughborough University, UK
Yang, Wuqiang, The University of Manchester, UK
Yang, Xiaoling, University of Georgia, Athens, GA, USA
Yaping Dan, Harvard University, USA
Ymeti, Aurel, University of Twente, Netherland
Yong Zhao, Northeastern University, China
Yu, Haihu, Wuhan University of Technology, China
Yuan, Yong, Massey University, New Zealand
Yufera Garcia, Alberto, Seville University, Spain
Zakaria, Zulkarnay, University Malaysia Perlis, Malaysia
Zagnoni, Michele, University of Southampton, UK
Zamani, Cyrus, Universitat de Barcelona, Spain
Zeni, Luigi, Second University of Naples, Italy
Zhang, Minglong, Shanghai University, China
Zhang, Qintao, University of California at Berkeley, USA
Zhang, Weiping, Shanghai Jiao Tong University, China
Zhang, Wenming, Shanghai Jiao Tong University, China
Zhang, Xueji, World Precision Instruments, Inc., USA
Zhong, Haoxiang, Henan Normal University, China
Zhu, Qing, Fujifilm Dimatix, Inc., USA
Zorzano, Luis, Universidad de La Rioja, Spain
Zourob, Mohammed, University of Cambridge, UK
Sensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA).
Available in electronic and on CD. Copyright © 2012 by International Frequency Sensor Association. All rights reserved.
Sensors & Transducers Journal
Contents
Volume 144
Issue 9
September 2012
www.sensorsportal.com
ISSN 1726-5479
Research Articles
Research in Nanothermometry. Part 8. Summary
Svyatoslav Yatsyshyn, Bohdan Stadnyk, Yaroslav Lutsyk, Olena Basalkevych ...............................
1
Temperature Measurement and Control Based on LabVIEW and SMS
D. Mercy, Ashok M., Karthick N., Rajamanickam M...........................................................................
16
Theoretical Considerations of Fiber Optic Sensors for Thermal Sensing Under Low and
High Temperatures Effects
Ahmed Nabih Zaki Rashed.................................................................................................................
27
Effect of Firing Temperature on the Micro Structural Parameters of Synthesized Zinc Oxide
Thick Film Resistors Deposited by Screen Printing Method
Ratan Y. Borse, Vaishali. T. Salunke and Jalinder Ambekar .............................................................
45
Design and Analysis of Bulk Micromachined Piezoresistive MEMS Accelerometer for
Concrete SHM Applications
S. Kavitha, R. Joseph Daniel, K.Sumangala ......................................................................................
62
Lumped Parameter Modeling of Absolute and Differential Micro Pressure Sensors
S. Meenatchisundaram, Ashwin Simha, Mukund Kumar Menon, S. M. Kulkarni
and Somashekara Bhat ......................................................................................................................
76
Geometrical Amplification of SMA Actuator Displacement Using Externally Actuated Beam
Elwaleed Awad Khidir, Nik Abdullah Mohamed, Sallehuddin Mohamed Haris..................................
92
High Accuracy Resolver to Digital Converter Based on Modified Angle Tracking Observer
Method
Chandra Mohan Reddy Sivappagari, Nagabhushan Raju Konduru...................................................
101
Development of Single Place Multiple Obstacle Avoidable System for Guarded Teleoperated Trolley, a Service Robot Using Single Ultrasonic Sensor
Subrata Chottopadhaya and Soumendra Nath Kundu.......................................................................
113
A Real Time Radio Frequency Field Imaging for Detection of Impurities in Liquids
Mohammad Mezaael. .........................................................................................................................
123
Design and Simulation of a Microgripper with the Ability of Releasing Nano Particles by
Vibrating End-Effectors
Hamed Demaghsi, Hadi Mirzajani, Ehsan Atashzaban, Habib Badri Ghavifekr ................................
131
Linear Resistivity Response with Relative Humidity of Gd Doped Magnesium Ferrite
Jyoti Shah, Amish G. Joshi and R. K. Kotnala ...................................................................................
143
Quartz Crystal Microbalance DNA Based Biosensor for the Detection of Brugia malayi
Thongchai Kaewphinit, Somchai Santiwatanakul, Supatra Areekit and Kosum Chansiri..................
153
161
Recent Advance in Antibody or Hapten Immobilization Protocols of Electrochemical
Immunosensor for Detetion of Pesticide Residues
Ying Zhu, Xia Sun, Xiangyou Wang ...................................................................................................
PSoC Based Blood Coagulation Instrument for the Analysis of PT & APTT
Raghunathan R., Neelamegam P. and Murugananthan K.................................................................
182
L-Asparaginase Extracted From Capsicum annum L and Development of Asparagine
Biosensor for Leukemia
Kuldeep Kumar and Shefali Walia......................................................................................................
192
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International Frequency Sensor Association (IFSA).
Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 1-15
Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Research in Nanothermometry. Part 8. Summary
Svyatoslav YATSYSHYN, Bohdan STADNYK, Yaroslav LUTSYK,
Olena BASALKEVYCH
National University 'Lviv Polytechnic', Institute of Computer Technologies,
Automatics and Metrology, Bandera str.12, Lviv, 79013, Ukraine
Tel.: +38-0322-37-50-89
E-mail: slav.yat@gmail.com
Received: 28 August 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Recent advances in nanotechnology are expressed by the atom-scale insights successes
related to development in nanometrology. Its main integral part is supposed to be nanothermometry.
The latter rests on nanothermodynamics while determining its advancement as well as the overall
progress of nanotechnology. This mutual feedback, described by the examples of certain types of
thermotransducers in a series of articles published in Sensors & Transducers journal earlier, is under
consideration in this paper. Copyright © 2012 IFSA.
Keywords: Nanothermometry, Nanometrology, Nanothermodynamics, Fluctuations, Noise
1. Introduction
Succession of investigations in reference to the study of processes in thermodynamic materials for the
purpose of creating the high-precision thermometers adapted for extreme temperatures and other
exploitation conditions has been made through the last years [1]. The required experience, encouraged
by the endeavor to cognize the nature of measuring instrument drifting which is related to the
processes in a thermometric substance, proved to be precious, since being concerned with the profound
processes in the substance, namely at micro- and nano-levels. Besides, with the development in
nanotechnology, nanometrology [2] accompanied by nanothermodynamics has arisen [3].
Metrology is inseparably related to thermometry. The same concerns nanothermometry as an integral
part of nanometrology. Its development implies both a direct task, to measure temperatures and other
substance thermo-properties in nanoworld, and an opposite one, to ascertain the reasons for particular
1
Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 1-15
behavior of metrological characteristics, linking them with fluctuation-dissipation processes in
thermometric substance during manufacturing and exploiting at a micro- and nano-level, on the basis
of high experimental experience and metrological culture due to the measurement results with the use
of nanosized and nanostructured sensitive elements of thermometers.
In nanotechnology the term “Temperature” acquires ambiguous statistical thermodynamic sense [4].
Hereby, the absence of knowledge about the temperature of the researched micro- and nano-objects
does not allow to assure the reproducibility of technologies, and measurement leads simultaneously to
the shift of a temperature field, appearance of methodical error of a measurement instrument and
sometimes to the destruction of a nanopattern despite of applying contact and/or remote method [5].
Keeping the deep insights of metrological approaches into the essence of processes within
thermosensitive substance, and mastering the achievements of nanotechnology, we have widened the
spectrum of thermometric methods: assimilating Raman thermometry [6-7] of micro- and
nanopatterns; conducting the research in thermometry based on solid- and liquid-phase sensitive
elements at decreasing their sizes down to a nanoarea; and studying the role of a thermosensitive
substance superficial tension gradient as a main thermodynamic force of nanothermodynamics in the
forming of metrological characteristics [8] besides the already ascertained role of a mechanical
microtension gradient [9] in the forming of thermometer transformation function drift.
The branches of thermometry were singled out in order to study the nanostructured thermosensitive
substances for the purpose of improving durability or increasing the temperature limits of sensors.
Those are the branches of ultrasonic thermometry [10], noise thermometry [11] and resistive
thermometry [12]. Owing to the means of engineering maintenance including the traditional equipment
for calibrating, thoroughly conducted resource tests, and row of structural methods for characterizing a
structure and its changes during exploitation, we managed to associate the effect of nanostructuring
with quite precisely controlled micro- and macro-characteristics.
The widest investigations were made in traditional thermoelectric thermometry [13], where
thermodynamics of irreversible processes, having proved mutual independency of the chosen
thermodynamic coordinates (forces and flows) with regard to physical phenomena spectrum, is
combined with metrology (its integral part - thermometry) and the relevant apparatus of
“correlativeness / incorrelativeness” of the gained measurement results with the row of influence
factors.
All the papers on the subject are published in the series of articles including Research in
Nanothermometry, Part 1-7 [4, 5, 7, 8, 10-12] and [6, 9, 13, 14] being completed by this work where
theoretical aspects and possibilities of the further progress in nanotechnology that follow from the
conducted research are considered.
2. Task Definition
The fundamental issues in this domain of nanomaterials are [3]: ... ability to obtain the required
composition, not just the average composition but details such as defects, concentration gradients, etc.,
and to control the modulation dimensionality … More specifically the following issues have to be
considered for the future development of nanomaterials: … better understanding of the influence of the
size of building blocks in nanostructured materials as well as the influence of microstructure on
physical, chemical and mechanical properties of this material; better understanding of the influence of
interfaces on properties of nanostructured material; development of concepts for nanostructured
materials and in particular their elaboration, etc.
2
Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 1-15
Nowadays, there is a great deal of interest and activity steered towards extending macroscopic
thermodynamics and statistical physics to the nanometer scale consisting of countable particles below
the thermodynamic limit due to recent developments in nanoscience and nanotechnology. To
generalize thermodynamics on a nanoscale, we should understand well the unique properties of
nanosystems. One of the characteristic features of nanosystems is their high surface-to-volume ratio.
As the results of surface effects becoming increasingly important with decreasing size, the Gibbs free
energy relatively increases for some thermodynamic equilibrium systems. Therefore, the behavior of
such nanoscopic clusters differs significantly from the usual thermodynamic limit. It is clearly known
that when the system size decreases, one has to consider the fluctuations. Based on the nucleation
reactions, the first considerations are given to the temperature fluctuations. The quantitative
measurements of temperature fluctuations are realized by superconducting magnetometers.
Remarkably that an important role of fluctuations is well underlined in the following statement by the
US National Initiative on Nanotechnology: ‘‘There are also many different types of time scales,
ranging from 10-15 s to several seconds, so consideration must be given to the fact that the particles are
actually undergoing fluctuations in time and to the fact that there are uneven size distributions. To
provide reliable results, researchers should also consider the relative accuracy appropriate for the space
and time scales that are required; however, the cost of accuracy can be high. The temporal scale goes
linearly with the number of particles N, the spatial scale goes as O (N log N), yet the accuracy scale
can go as high as N7 to N! with a significant prefactor’’. Therefore, these valuable hints motivate
researchers to pursue the thermodynamic description at the nanometer size for the nucleation of a
metastable phase.
For instance, with the help of Raman thermometer the measurements of carbon nanotube temperature
within the range 30 ... 250 ºС are made [7]. Those tubes are treated to be standard nanopatterns for
testing and calibrating the nanotechnological means. The gained results of experimental research give
possibility of realizing the metrologically correct evaluation of temperature measurement results with
considering the peculiarities of both measuring instrument and standard pattern. Hereby, to study the
action of seven and more possible influence factors (angle of light bunch incidence, distance to a
photo-receiver, exposure time, duration of spectrum passing, power and mode of laser functioning,
drift characteristics and so on), 28000 gauges have been performed, enabling us to ascertain the
following indices of the measurement accuracy. The approach of errors is applied to processing results,
consequently of which one of the gained results (with the introduced correction to a systematic error
component) looks as Тreal =287.27 К ± 1.72 К (0.6 %). At the same time, due to the uncertainty
approach, the gained result makes Тreal =287.27 К with the expanded error 0.58 % and combined
standard uncertainty 0.3 % at the credence level Р = 0.95, the expanded coefficient 1.96 and the
efficient value of freedom degrees 130.6 [11].
However, while applying the thermodynamics to the choice of uncorrelated influence factors in the
form of thermodynamic coordinates (forces and flows) acting on the thermometric substance of a
thermoelectric thermometer, the quantity of necessary measurements is decreased for 1-2 orders,
assuring the similar accuracy indices [13]. As a result, the summary influence function in the presence
of external thermodynamic fields is determined. Temperature, density, strain and etc. gradients created
by the external effect in thermosensitive substance are subordinated to the same statistical regularities
as the gradients appearing consequently of a fluctuations effect in the mentioned substance (according
to the sense of a fluctuation-dissipation theorem of thermodynamics). At the availability of
fluctuations, additional influence functions applying multiplicatively on influence functions related by
the fluctuation effect of external environment are formed. The given approach is valuable due to the
possibilities of considering the thermodynamic system of thermosensitive substance with regard to
external environment, and taking into account the essentiality of fluctuation-dissipation processes that
occur in substance itself.
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The following nanotechnology development including nanomaterial-lore does not seem to be possible
without this in nanometrology [2], whose highly important component is nanothermometry. There are
two main issues to consider in nanometrology: precise measurement of sizes within the nanoscope
range, and assimilation of existing methods or development of new ones to characterize properties as a
function of size and of the certain temperature, pressure and other thermodynamical parameters.
Progress in nanothermometry is inseparably related to the nanometrological investigations first of all
of nanostructured materials, consequently, to the identification of standard nanopatterns, the
performance of their multipurpose and volumetric research and finally to the further development of
nanotechnology theoretical principles at electron, quasi-particle, particle and nanothermodynamic
decisive levels.
3. Objectives
The study of instrumental facilities of different methods for substance profound structure research on
the pattern of thermosensitive thermometer substance in comparison with theoretical developments
including the achievements in nanothermodynamics, and so-grounded stipulation of both perspective
research methods and theoretical approaches to the further nanothermometry and nanotechnology
development (technology+measurement).
4. Theoretical Approaches to Temperature Measurement and Determination
in Nanotechnology
Among the considerable number of temperature definitions, one of the fittest for nanothermometry is
the following [4]. Temperature is the statistically formed value of quantity, determined by the inner
energy of a body of sufficient sizes for the purpose of applying a thermodynamic consideration to this
body. So far temperature remains the last and only value among seven main units of International unit
system that is still not regulated at the nuclear / molecular and hence much higher level in terms of
accuracy. In the chain of leading world centers, the intensive endeavors of elaborating and assuring the
unit of temperature scale in the form of a quantum energetic unit at the methodological level of State
certifying services are carried out for several years. Applying the direct methods of temperature
determination (gas, acoustic, optical, magnetic and noise), and having determined Boltzmann constant
with a very small error, we could regulate the unit of a temperature scale due to the energetic units,
also endued with a certain determination error. Afterwards, according to the development in
nanotechnology, it is supposed to appear the temperature energetic quantum, similar to the quantum of
electric resistance, quantum of an electric charge unit, capacitance quantum and so on.
4.1. Principles of Thermodynamic Approach
Dealing with metrology (measurement and result processing), authors have determined the core
question - how due to the results of measuring the macro-, micro or just noise-characteristics of the
highest level characteristics it is possible to estimate changes at the nanolevel of nanostructured or 1d-,
2d- nanomaterials. To solve it, we should develop the methodology of nanomeasurements as well as
principles of interpreting the gained results. Moreover, the approach of I. Prigogine, following which
any microscopic process is a result of more or less coherent microscopic processes, should be taken as
a basis [15]. Microscopic freedom degrees are revealed as fluctuations describable in terms of the
introduced additive members in the equation for microscopic quantities. Exactly in this way we have
advanced in the study of drift of a thermoelectric thermometer transformation function. To avoid the
correlation effects of different influence factors which are of importance assuring the accuracy in
metrology, the metrologically stipulated system of selecting the non-related factors that are
thermodynamic quantities is applied [13].
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4.2. Nanothermometry, Nanothermodynamics and Temperature of Nanoobjects
Nanothermometry as a branch of nanometrology implies measuring the temperature of nanoobjects
with the given accuracy (error, uncertainty), repeatability, discreteness and under the preliminary
guaranteed instability of a thermometer transformation function. Moreover, rare works [16] are
dedicated to experiencing the term of nanoobject temperature. Almost all known works concern with
temperature as one of the main thermodynamic parameters, successfully characterizing substance at
the macrolevel. In the light of the stable development of nanotechnology and nanomeasuring, the
determination of this task acquires increasing significance, since:
 Single unrepeated gauging is applied whereas there is no place for the classical approach of error
theory, evolved in information-measuring systems [17];
 Measuring instrument (sensor) of primary transducer of a measuring means whose interference into
energetic exchange disturbs the dimension of the controlled quantity acquires weight;
 Previous selection of certain research tools among the totality of possible instruments capable of
retaining the reproducibility of the much needed kind of direct or indirect measurements of a
concrete quantity is demanded. Since, when the definition uncertainty is considerable as compared
to other uncertainty components, its inclusion into general balance leads to extension of a covering
interval [18].
Finally, there appears the task to describe the gained results so that they would satisfy the experiment
reproducibility and repeatability as well as the possibility of comparison and verification of the
received values of the measured quantity for the purpose of working out its dimension. So in order to
determine the temperature of nanoscaled objects, we should:
 Base on the (nano)thermodynamic interpretation of temperature;
 Follow from the existing methods of thermodynamic temperature determination;
 Apply methods connecting the values “temperature” and “controlled parameter”, avoiding the
additional coefficients determined with the insufficient accuracy (following other methods), to
research the concrete temperature;
 Stipulate the limits and possibilities of applying the notion “thermodynamic temperature” as to the
mentioned objects considering their linear sizes;
 Represent the errors of single gauging of “thermodynamic temperature” in hybrid [19] or hybridthermodynamic [13] interpretation.
The latter explains the form of systematic component of an instrumental error as an additive totality of
multiplicative pairs of influence functions with appropriate coefficients. Forming pairs, where one of
the coefficients is determined by fluctuations of thermosensitive substance properties, and another – by
fluctuations of parameters of the applied outer fields, corresponds to the sense of fluctuationdissipation theorem of thermodynamics. Hybrid-thermodynamic approach is used in [18] to evaluate
the results of measurement, and its basis concerns with the study of origin sources of errors and
particular influence functions. The research of the processes of energy-transmission, based on
thermodynamics, enables us to determine a methodical error component as well as cognizable part of
systematic component of an instrumental error component, and thus to decrease substantially the
guaranteed by the manufacturer of thermometric means a total error of measuring the temperature in
exploitation conditions.
4.3. Methodical Errors of Temperature Measurement in Micro- and Nano-world
Avoiding the questions of the gas-medium measurement where owing to the pyrometry means
employment there are possibilities of enduing some temperature value through radiation intensity or
spectrum to the certain selected atom or molecule, let us consider the methodically more complicated
questions of thermometring the solids – micro- and nanoobjects, first of all, their surfaces. The
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interactive response of object practically is not figured anywhere consequently of both its own change
caused by thermometring and this of thermometer transformation functions during measuring under
the effect of energetic-entropy selection. In the conditions of rising significance of energy-transmission
processes in the medium “thermometer – controlled object”, with decreasing sizes of an object, the
intervention of a thermometer into an energetic exchange affects the temperature values, hence causing
the occurrence of systematic component of a methodical error component. The more considerable the
latter is, the less object we deal with. Therefore, it is proposed for a short term: (a) to create a thermal
contact between sensor and object; (b) to conduct the measurements simultaneously; and (c) to
determine the temperature and methodical error due to the nature of signal drift.
In the macroworld by default is assumed that linear sensor size does not exceed 0.1-linear size of the
controlled object, and the ratio of their volumes – 0.001. This defines a relative methodical error of
measurement no higher than 0.1 %. In total, this value loses other components of the measurement
error, including the instrumental one. So there are grounds not to consider and not to take into account
the methodical error of temperature measurement. For micro-, nano- sized sensors and controlled
T
objects with comparable thermophysical properties, a relative methodical error is  Tmet  sen  1 . For
Tx
example, while gauging microobject with Tx = 270 K by means of commensurate-sized sensor of the
initial temperature Tsen = 300 K, it is received  Tmet  11% [5].
4.4 Fluctuation-dissipation Theorem of Thermodynamics and Noise Measurement Methods
Fluctuation-dissipation theorem, relating reversible and irreversible thermodynamics, could be applied
both to nanomaterials and sensitive thermometer substance. Nyquist rate, combining the spectral
density of energy (SDE) of electric noises within certain frequency band and electric resistance, is
treated as a particular case of this theorem. In experimental research, the special attention could be
paid exactly to the method of noise investigation both in electric and mechanical treatment. The
research in noise- and ultrasonic-thermometry made at the enterprise-designer has assured the high
level of metrological reliability of the gained results, and fostered the specification of theoretical states.
The main scientific results of electric noise investigation undertaken on the noise thermometers within
the range 4.2 ... 500 К are the following [11]. The known Nyquist formula is equitable for thermal
electrical noise at frequencies higher than 1000 Hz and for sensitive elements, made from alloys and
composites. Their calibration characteristics are linear, with no recorded deviations. The consideration
is important for such an ordinary factor of influence on calibration characteristics as a deformation
factor that tends to increase at the combined action of temperature and strains (structural, resilient,
ductile or others) in thermosensitive substance during measuring. This factor is present in the local
distortions of temperature in the mentioned materials as a result of tensile micro concentrator actions,
which are considered as quasi local nanosized defects of thermo-fluctuation origin. The given
distortions have lowered substantially the real durability of thermometric substance through
electromechanical noises, which at the same time stipulates the deviations of noise thermometer
calibration characteristics, worsening its precision. The intensity of electrical noise considerably
depends on different transport processes in the thermosensitive substance. For instance, at 77 K, the
most significant are the heat and charge transfer processes. At the certain temperatures and
deformations, the importance of other processes that may become crucial is growing.
The similar results of characteristics’ calibration research in the case of ultrasonic thermometers with
tungsten-based half-wave sensitive elements but within the highest temperature range 2000 ... 3000 К
are proposed in [10]. Hereby, the transformation function, expressed in a frequency characteristic
measured with a minimal error depending on temperature, is caused by direct temperature changes of
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Young module. Let us notice that the maximal stability of indices is inherent in sensitive elements
made from nanostructured tungsten. The essential role of micro concentrators of mechanical tensions
is revealed both in assuring the high-temperature shape-stability with resulting stability of indices, and
in appearing 1/f noise which becomes the pivotal causer of gained bandwidth quality of a thermometer
resonator.
Getting into a tungsten matrix, admixture and addition atoms reveal a deformation influence depending
on the ratio of their atom volumes and volume of a main component. For instance, the ratio of atom
volumes of potassium – main admixture of silicon-lixivium addition – and tungsten makes near 5 : 1.
Consequently, mechanical tension fields, made by addition atoms, influence upon the state of
substances and finally affect an USTh transformation function. Admixtures of potassium, indissoluble
at the room temperatures in a tungsten matrix, are concentrated in the form of liquid phase bubbles.
With rising the temperature till 1750-1850 К, they evaporate forming micropores inside of which the
pressure reaches 30-40 МPа. As a result, the field of mechanical tensile nanoconcentrators is being
formed in a matrix, stabilizing a structure. Their influence could be taken into consideration in terms of
consolidation theory of porous metal ceramic materials. Hereby, mechanical tensions are applied
exactly to pores without affecting monolith. Therefore the changes of an USTh transformation function
could be related through Young module to the alteration in porosity, and correspondently through the
f (T ) 1   EU (T ) 
prefactor  0 – to that in substance density from  0 to  :
 
.
f
2   0 EU (T ) 
5. Following Development of Theoretical Approaches and Experimental Methods
Up to date, there are two kinds of fundamental approaches to open out in thermodynamics on
nanoscale, based on the microscopic and macroscopic viewpoints, respectively. One would go back to
the fundamental theorem of macroscopic thermodynamics and establish the new formalism of
nanothermodynamics by introducing the new function(s) presenting the fluctuations or surface effects
of nanosystems. Another one could directly modify the equations of the macroscopic thermodynamics
and establish the new model of thermodynamics on nanoscale by incorporating the Laplace–Young or
Gibbs–Thomson relations, representing the density fluctuation of nanosystems in the corresponding
thermodynamic expressions [3, p.89]. Let us apply both of mentioned approaches to the description of
thermometer behavior and metrological characteristics incl. sensitive elements made from nanoscaled
and nanostructured materials.
5.1. Thermodynamic
Quantities
and
New
Formalism
of
while Describing the Peculiarities of Thermometer Exploitation
Nanothermodynamics
5.1.1. Nanothermometers with Solid- and Liquid-phase Sensitive Elements
Thermometers with liquid- and solid-phase sensitive elements are already well-known in thermometry
[9]. Their construction implies the availability of a narrow tube with movable thermosensitive
substance inside. The consideration given to a liquid thermometer in macroworld under the condition
of neglecting superficial tension, when the diameter of a thermometric tube is quite large and
thermosensitive substance is hardly contractile, could bring in the analysis of the equation describing
the interrelation of the volume of the mentioned substance and the changes of column sizes h of a
thermometer with its temperature: h  c d T , mm (d - the inner diameter of a thermometric tube; c –
constant). Hereby, the sensitivity of a thermometer is reducing with the decrease in the diameter of a
thermometric tube. Here the effect of superficial tension forces is revealed only through the distortion
of meniscus causing a readout error.
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The similar equation is deduced also for micro- and nanothermometers filled with solid-phase
thermosensitive substance. These sensitive elements that function due to the phenomenon of thermo
expanding are principally similar to the sensitive elements of liquid thermometers of macrosizes. The
size of the employed micro- and nanotube is being reduced while bringing thermometer dimensions
onto micro- and afterwards nanoscale. Modern investigations cover the application of single-wall
carbon nanotubes with the diameter approximately 10 ... 40 nm.
In the case of a nanothermometer with a liquid-phase sensitive element, the processes of transposition
related to the superficial freedom degree prevail. The behavior of liquid in a capillary tube depends on
the phenomenon of moistening. The temperature-dependence of superficial tension could be depicted
for all liquids so that the data are placed along one common curve:  V 2 3  k Tc  6  T  . Here,
k = 2.1×10-7 Joule/K mole-2/3 – Eötvös constant that is equal for all liquids. The equation of calibration
characteristic of a nanothermometer with a liquid-phase sensitive element could be found as
h  b Tc  6  T  / d (b – constant).
Evidently, the constant of calibration characteristic of a nanothermometer with a liquid-phase sensitive
element as well as the constant of this of a nanothermometer with a solid-phase sensitive element
comprises the size of a determinant constructive element, namely the inner diameter of the
thermometric tube d. However, the main difference between them is that the sensitivity of a
nanothermometer with a liquid thermosensitive element rises relatively to the decrease in a tube
diameter, whereas the sensitivity of a nanothermometer with a solid-phase sensitive element falls [9].
Therefore, in the latter case, we should orient not to readings through solid-phase sensitive substance,
but due to the dimension of an air column above.
5.1.2. Resistance Thermometers with Nanostructured Thermosensitive Substance
Metal glasses (MG) with an amorphous structure undergo our investigations as the new-gained
materials with the high specific resistance at the small value of its temperature coefficient . The
particular efforts are made in the endeavour to bind the electrical MG properties with the peculiarities
of their manufacturing technology which could be profitable in evolving the special electrotechnical
materials, spintronics [20andetc. The study of nanostructured materials confirms the existence of
fields of considerable mechanical microtensions whose influence is equal to the doping with a number
of admixtures. In this case, we have applied nanothermodynamics in order to explain MG
electrokinetics and other properties. The latter stipulates the introduction of two additional freedom
degrees into the main equation of thermodynamics: dM (M- the surface area), caused by the
superficial tension and ƔdV, caused by the expenses of the specific energy Ɣ for the formation of
precipitations of the second phase with the volume V in the matrix of output substance. Considering
the two-phase MG model, i.e., assuming that there are precipitations of other phases in a matrix, e.g.
pseudo-phases which could be represented by the microvolumes with different densities, to obtain
experimental results, we evolve the equation of the third order for the specific electrical resistance of
2
2
3
two-phase material:  a     1  q  1         q 12 . Here q  S S - an efficient
intersection area of precipitations, 1  a  bT - specific electrical resistance of a matrix, Δρ – changes
of the specific electrical resistance due to precipitations. Explanation roots in the dependence of the
specific volume on the speed of chilling, determined due to the mentioned temperature. The increment
in the MG volume reaching some percent and considerably influencing the transmission processes,
occurs with temperature rising [12].
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5.1.3. Thermoelectric Thermometers, Thermometric Substance and its Structure
Phenomenology [9] of thermo-e.m.f. stability of mechanically strained (σ - tension) thermometric
material has ascertained the fact of Gibbs energy  2 EU amplification and enabled us to stipulate the
introduction of additional thermodynamic power X , caused by the drop in this
energy: X    2 2 EU    EU  . For better understanding of processes that happen in materials,
thermometer drift is related to the temperature change in small substance volumes (with or without
deformation). In thermosensitive substance, the notified below hierarchy of structural levels with
regard to the deformation/distraction processes is observed.
The structural levels, utmost by their scales, are treated to be stiffly given. The first of them is a
macroscopic level, describing the chain of defects with the help of a distribution function, smoothly
changing due to volume. In thermometry it is realized by introduction of dependable on temperature,
distributed physical properties of thermometric substance, or parameters of classical thermodynamics
which helps to reach the change in (thermo)electric properties. In [21] (Fig. 1), the temperature
changes of solid bodies, depending on the applied efforts (an adiabatic case) in a tense-deformation
state, have been thoroughly studied. The temperature fall is fixed in the resilient deformation area.
With crossing into the area of ductile deformation, temperature changes in local volumes of the
deformed substance get the tendency to change a sign.
Intermediate area of a mezoscopic level (10-8 – 10-5 m) is placed between macroscopic and nanolevel
which concerns with the certain lattice defects (main physical processes – kinematics of dot defects,
diffusion ductility and correspondent changes of thermoelectric properties). In this area, the processes
of precritical evolution of the deformed material take place. Hereby, the considerable tension gradients
are being formed. In the opinion of [22] under the conditions of relative pattern prolongation 10-4 as an
issue of residual viscous deformation, a deformation potential makes 0,5 mV which affects
thermoelectric properties, particularly, thermoelectric heterogeneity. When the sizes of certain crystals
become commensurable with the characteristic size (wire diameter) of recrystallized material, the level
of mechanical tensions on the border of non-recrystallized and recrystallized areas reaches 103 MPа,
which leads to linear changes in thermo-e.m.f.
Fig. 1. Correlation of mechanical tensions and deformations in resilient area with pattern temperature change,
fixed by means of a thermoelectric thermometer [21].
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The second determinable level is a nanoscopic level. The decrease in the real limit of material strength
concerning the theoretical values is explained by the presence of tensile defects [23], [24]. The latter
are considered as micro- or nano- concentrators of mechanical tensions, according to the results of
analyzing the spectrum of combinational light in the transparent polyethylene terephthalate. The
availability of low- and high-frequency satellites with reference to the main maximum confirms the
existence of micro- and nanoareas of extension and contraction, respectively. The displacement of
frequencies for 8 cm-1 is explained by the deformation of inter-atomic bonds of tensile defects for the
value   
1 
. Here
G 
G - the mode parameter of Gruneisen,  - the alteration of light frequency,
due to multi-phonon dispersion.
While capturing the phonons by a tensile defect, its local temperature is rising. Considerable thermoextension takes place and hence at some moment could cause the rapture of inter-atomic bonds and
thus microcrack appearance. Using the intensity of fluctuation bands in Raman spectrum of
combinational dispersion (Stokes and Anti-Stokes bands), the phonon value or the capture by tensile
defects was ascertained. Local temperature of the above mentioned defects (with linear size till 10
atoms) is determined as Td 
h
k ln  n  1  ln n 
. It is obvious how essential the dot temperature
increasing is on these defects, being especially important for the thermosensitive substance.
Microconcentrators serve as a trigger that launches the transformation of the energy applied to the
substance into microcracks. The appearance of the latter is indissolubly related to the emission and
redistribution of energy, considerably exceeding the energy capacitance of elementary deformation
acts. The indication of tensile defects as physically elementary nanosized structure subsystems enables
us to motivate the introduction of thermodynamic values i.e. the application of nanothermodynamics
of irreversible processes to the researched substance.
Since metal glasses of Fe-(Ni)-B system, whose structure resembles unordered liquid, and a
deformation nature is viscous, are referred to nanostructured materials, we have studied temperaturemechanical factors’ effect on the changes in calibration characteristics of thermoelectric thermometers.
The high reproducibility of characteristics and their negligible drift (till 11 nV) at high-temperature
maturing under the substantial extension efforts (Fig. 2) is noted. It proves [12] the determinable role
in the drift appearance of both a mechanical tension gradient and concentrators of mechanical tensions.
5.2. New Model of Thermodynamics on Nanoscale with Incorporating the Real Defect Structure
In the concrete case of thermometric substance, thermodynamic methods do not allow ascertaining the
form of an equation of a thermodynamic system state, required to endow the equation of
thermodynamics with necessary physical sense. On contrary, this determination takes place owing to
statistic physics as an indisputable thermodynamic component predicting the necessity of fluctuation
presence [25]. Therefore considering the problems of noise inherent in a solid state and originated by
thermo- or 1 f low-frequency, let us plunge into the detailed peculiarities of electron-phonon
interaction, applying different approximations on the subject of possible ways of their adequate
description by statistic physics. For a solid body in the case of possible Hooke’s law applying, the
normal lattice fluctuations are treated to be independent. The energy of these fluctuations is
determined by their frequency  , and depends on the quantum number n of phonon states. In thermoequilibrium, the mean value of the quantum number n is found by Planck law, representing the
function of phonon distribution through the frequencies [26]. Energy (of acoustic or optical modes) of
phonons could be approximated by the Debye model based on Planck’s law at   kT .
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The Einstein model applies the same formula while assuming that   kT . However, no model
describes intermediate, in our opinion, the real state of a solid body when phonon energy becomes
commensurable with kT, and in the body structure, heterogeneities of nano-scaled tensile quasi-defects
capable of modifying the very process of electron-phonon interaction appear. The presence of the latter
[23] implies, at first, that the consideration is not made at the level of linear oscillators but physically
elementary substance volumes in whose case thermodynamic consideration already has a sense; at
second, that there exists ambiguity in a substance structuring nature at the atom-electron level as well
as the appropriate ways of its study during electron-phonon interaction (through SDE while studying
electric noise, or the spectral distribution of an inner friction parameter while studying the latter).
Fig. 2. Temperature dependence of changes in integral thermo-e.m.f. of metal glasses
at the different extension efforts.
Let us accept that phonon energy is insignificant:   kT . It means that the situation could be liable
kT
to the description through the Debye model with a distribution function that is reduced to  n 

(the symbol  means a mean value in thermo-equilibrium). To wit, at the attenuated process
energetics when the speed of energy incoming cedes the speed of its removal at any current moment of
time, the given defect acts as a virtual trap of phonons. Having absorbed a phonon and received an
energy quantum, quasi-effect immediately gets rid of the latter. However, the similar methods are not
capable of describing the system behavior revealed in a certain set of researched physical properties.
Thus, at n synchronously acquired phonons on one tensile defect (multi-phonon dispersion, fixed e.g.
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by Raman method [27]), Planck law describes no longer the state of thermo-equilibrium in the whole
volume of substance, but that of physically elementary part of the mentioned volume relevant to the
concrete defect:
1
 nd  
e
n
kT
(1)
1
Defects with temperatures over a thousand degrees are fixed in [24] at spectroscopic study of
transparent polymers during deformation at room temperature. Then at n  kT , i. e. at multiphonon dispersion, the total energy of phonons essentially exceeds kT, causing the passing to the
Einstein model with the distribution:
 n 
 nd   exp  

 kT 
(2)
It does not affect the previously determined limits of the mentioned models. As we discuss not so the
ordinary oscillators but rather the fluctuation oscillators of a nanolevel, whose usage helps to explain
the lowering of a material robustness limit in comparison with theoretical values. At rising the total
energy of phonons, accumulated on a defect, the latter finishes its role of an energetic concentrator
with its following conversion into a microcrack. It happens when the speed of energy incoming
exceeds that of its removal [28].
On the other hand, in the thermometric substance of an ultrasonic thermometer, the possibilities of
such physically elementary volumes to absorb the energy of an elastic-plastic wave, are characterized
W
by e. g. the absorption factor  
, here W is a part of the common wave energy W absorbed
2 W
by the body. In the case of the Debye model, the absorption factor is expressed through the frequency:

f


. If it is reduced to the single frequency range f , then it corresponds to the spectral
2 2 f
absorption factor:
  f  

1

f 2 f
(3)
In the case of the signals of smaller energy (lower frequency), we could treat the medium response to
be linear. Then fluctuation SDE is proportional to the spectral absorption factor:
S  f   k p   f  
kp
2 f
,
(4)
here k p - the coefficient of power transmission of a measuring system. To wit, consequently of the
justified Debye model application, the frequency SDE dependence inherent in 1/f noise is gained.
In the case of the Einstein model usage, at the concentrating of energy phonons on the physically
elementary volumes, tensile quasi-defects, we come to the determination of an absorption factor as:

n nf

kT
2 kT
(5)
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and a spectral absorption factor being proportional to S  f  :
  f  
 n

f kT
(6)
It is frequency independent, and fitted for the case of thermal noises. The experimentally fixed
quadratic SDE nature of 1/f noise could be stipulated by a methodical measurement error, and a higher
order of degree dependence, till cubic, obviously, is related to the restriction of frequency-time
analysis range and to the integration of the gained signal whilst gauging the substance remains in nonstationary non-equilibrium thermodynamic state [29].
6. Conclusions
Development of research methodology with advancing in the theory and practice of both measurement
error and uncertainty approaches enables us, at first, to describe micro- and nanoobjects characteristics
in a more precise and reproductive manner and, at second, to deepen the microworld insight, assuring
the development of nanotechnology. Nanometrologically and nanothermometrically provided
reproducibility of loop turns to be of exceptional significance in nanopattern production. The
combination of metrological and especially thermometric methodologies with theoretical research
while using and further unifying the gained data requires the proper understanding of nanostructures in
order to reinforce our conviction that the revealed “artifacts” are not created in the very informationmeasurement system. Hereby, the optimization of measurement quantity, providing the proper
metrological characteristics, should comprise the elimination of correlation effects. This is carried out
by the qualified selection and estimation of influence factors whose independence is conditioned by
means of nanothermodynamics.
Since the sizes play a crucial role in ascertaining the properties of nanomaterials, and new phenomena
[2] are observed in nanopatterns, some of them being important for nanothermometry are considered in
detail:
 The intensive atom diffusion across the division surfaces of thermosensitive substance, involved in
the Laplace–Young or Gibbs–Thomson relations;
 The decrease in a temperature coefficient of electric resistance in metal glasses of resistance
thermometers till zero, caused by the precipitations of the second phase;
 Immediate action of superficial tension forces (the main component of the state equation of
nanothermodynamics), regarded in forming the calibration characteristics of capillary type
thermometers at decreasing their sizes into a nanoarea and etc.
Hereby, the issues of influence of the change in a phonon spectrum, so-called effect of phonon
confinement, are of primary significance in forming the calibration characteristics of Raman,
ultrasonic and noise thermotransducers. Particularly, this effect explains the appearance of 1/f noise
and its transformation into the thermal noise that has a special sense in nanothermometry and
eventually in nanometrology.
Thermodynamically stipulated phase equilibrium is replaced due to the contribution of division
surfaces or superficially predetermined mechanical tensions to free energy of thermosensitive
substance system which enables us to produce new quasi-nonequilibrium materials with a high
stability of calibrating characteristics for thermoelectric thermotransducers, and also to create
functionally gradient thermocouples that are a bright example of structures, quasi-distributed in space
[14].
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Acknowledgements
Acknowledgements to the staffs of Information-Measuring Technology Department and Metrology,
Standardization and Certification Department of Computer Technology, Automatics and Metrology
Institute of “Lviv Polytechnic” National University as well as the enterprise “Thermodevice” in Lviv
that politely offers a laboratory base for the realization and implementation of scientific research. We
also appreciate the assistance of “Sensors & Transducers” journal and, particularly, its editor-in-chief,
prof. S. Y. Yurish, in high-quality and efficient representation of the given series of papers.
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__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Temperature Measurement and Control Based
on LabVIEW and SMS
D. Mercy, Ashok M., Karthick N., Rajamanickam M.
Department of Electronics and Instrumentation Engineering, M.A.M College of Engineering,
Siruganur, Tiruchirappalli-621105, TamilNadu, India
Tel.: +91 9842270067
E-mail: mercyprabhu06@gmail.com
Received: 16 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Remote applications are becoming widely used in various fields such as industry, education
and security. This paper presents a low cost system to monitor and control the temperature remotely by
Short Message Service (SMS). The system has been designed using LabVIEW. In this process there is
no need of manual operation, the temperature can be controlled automatically from anywhere using
Bluetooth mobile phones. The system was successfully tested locally and remotely in a temperature
measurement procedure. Copyright © 2012 IFSA.
Keywords: LabVIEW, SMS, Bluetooth.
1. Introduction
The advancement of remote monitoring and control systems in recent years is closely related to the
outstanding advance in electronics and instrumentation techniques. In every distinct area it is possible
to evince different advantages and applications when using remote systems. To exemplify the
industrial field, a remote data acquisition may be used to monitor and control the temperature where it
is difficult to access or to use wired data acquisition systems. Addressing now the main focus of this
project, the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) is a graphical
programming environment from National Instruments that deserves attention not only on remote
systems, but in a wide range of applications.
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In this project Temperature is monitor and controlled using Virtual Instrumentation. Virtual
Instrumentation is the combination of Data acquisition hardware and LabVIEW software. The DAQ
hardware used here is the universal DAQ and the LabVIEW software is installed in the PC. The DAQ
hardware is connected to the PC through Universal Serial Bus. First the temperature is measured using
thermocouple and it is connected to the DAQ. The DAQ hardware consists of Analog to Digital
converter and the analog input acquire is converted to a digital form that is compatible with PC. In PC
LabVIEW based temperature monitoring coding is performed. By using the Front Panel of the
LabVIEW temperature can be monitored & based on the set points temperature can be controlled [1].
2. Block Diagram
The block diagram describes the working of complete temperature process (Fig. 1). First the
temperature is measured using thermocouple and it is connected to the DAQ. The DAQ hardware
consists of Analog to Digital converter and the analog input temperature is converted to a digital form
that is compatible with PC. In PC LabVIEW software is installed and in the LabVIEW block diagram
temperature monitor coding is performed. By using the Front Panel of the LabVIEW temperature can
be monitored and controlled based on the coding. The set point is set in the front panel when the
temperature reaches the set point “SET POINT REACHED” command is send as a SMS to the mobile
phone or PC using Bluetooth [5]. When the set point reaches the desire level the temperature process
will be stopped.
Fig. 1. Block Diagram.
3. Hardware Description
3.1. Temperature Sensor
Thermocouples are the most popular temperature sensors. A thermocouple is a device consisting of
two different conductors (usually metal alloys) that produce a voltage, proportional to a temperature
difference, between either end of the two conductors. Thermocouples are a widely used type of
temperature sensor for measurement and control and can also be used to convert a temperature
gradient into electricity. They are inexpensive, interchangeable, are supplied with standard connectors,
and can measure a wide range of temperatures. In contrast to most other methods of temperature
measurement, thermocouples are self-powered and require no external form of excitation. The main
limitation with thermocouples is accuracy and system errors of less than one degree Celsius (C) can be
difficult to achieve. Any junction of dissimilar metals will produce an electric potential related to
temperature. Thermocouples for practical measurement of temperature are junctions of specific alloys
which have a predictable and repeatable relationship between temperature and voltage [2].
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Thermocouples are widely used in science and industry; applications include temperature
measurement for kilns, gas turbine exhaust, diesel engines, and other industrial processes.
Thermocouples are among the easiest temperature sensors to use and obtain and are widely used in
science and industry. They are based on the Seeback Effect or Thermoelectric Effect that occurs in
electrical conductors that experience a temperature gradient along their length. They are simple,
rugged; need no batteries, measure over very wide temperature ranges and more. Thermocouples are
available in different combinations of metals or calibrations.
3.1.1. Thermocouple Types
Thermocouples are available in different combinations of metals or calibrations. The four most
common calibrations are J, K, T and E. Each calibration has a different temperature range and
environment, although the maximum temperature varies with the diameter of the wire used in the
thermocouple. Some of the thermocouple types have standardized with calibration tables, color codes
and assigned letter-designations [2].
There are four "classes" of thermocouples:
 The home body class (called base metal);
 The upper crust class (called rare metal or precious metal);
 The rarified class (refractory metals);
 The exotic class (standards and developmental devices).
The home bodies are the Types E, J, K, N and T. The upper crust is types B, S, and R, platinum all to
varying percentages. The exotic class includes several tungsten alloy thermocouples usually designated
as Type W. In this project, we use J-Type thermocouple as temperature sensor (Fig. 2). Type J (iron–
constantan) has a more restricted range than type K (−40 to +750 °C), but higher sensitivity of about
55 µV/°C. The Curie point of the iron (770 °C) causes an abrupt change in the characteristic, which
determines the upper temperature limit [2].
Fig. 2. J-Type thermocouple.
3.1.2. Principle of Working
The thermoelectric effect is the direct conversion of temperature differences to electric voltage and
vice-versa. A thermoelectric device creates a voltage when there is a different temperature on each
side. Conversely, when a voltage is applied to it, it creates a temperature difference. At the atomic
scale, an applied temperature gradient causes charge carriers in the material to diffuse from the hot
side to the cold side, similar to a classical gas that expands when heated; hence inducing a thermal
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current. This effect can be used to generate electricity, measure temperature or change the temperature
of objects. Because the direction of heating and cooling is determined by the polarity of the applied
voltage, thermoelectric devices are efficient temperature controllers.
The term "thermoelectric effect" encompasses three separately identified effects: the Seebeck effect,
Peltier effect and Thomson effect. Textbooks may refer to it as the Peltier–Seebeck effect. This
separation derives from the independent discoveries of French physicist Jean Charles Athanase Peltier
and Estonian-German physicist Thomas Johann Seebeck. Joule heating, the heat that is generated
whenever a voltage is applied across a resistive material, is related though it is not generally termed a
thermoelectric effect. The Peltier–Seebeck and Thomson effects are thermodynamically reversible,
whereas Joule heating is not.
Fig. 3. Thermocouple measuring circuit.
The working principle of thermocouple is based on three effects, discovered by Seebeck, Peltier and
Thomson.
Seebeck effect: The Seebeck effect states that when two different or unlike metals are joined together
at two junctions, an electromotive force (emf) is generated at the two junctions. The amount of emf
generated is different for different combinations of the metals (Figure 4).
The Seebeck effect is the conversion of temperature differences directly into electricity and is named
for German physicist Thomas Johann Seebeck, who, in 1821 discovered that a compass needle would
be deflected by a closed loop formed by two metals joined in two places, with a temperature difference
between the junctions. This was because the metals responded differently to the temperature
difference, creating a current loop and a magnetic field. Seebeck did not recognize there was an
electric current involved, so he called the phenomenon the thermomagnetic effect. Danish physicist
Hans Christian rectified the mistake and coined the term "thermoelectricity". The voltage created by
this effect is of the order of several microvolts per Kelvin difference. One such combination, copperconstantan, has a Seebeck coefficient of 41 microvolts per Kelvin at room temperature [2].
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Fig. 4. Seebeck Effect.
The voltage V developed can be derived from:
T2
V   S B T   S A T  dT ,
T1
Where SA and SB are the thermopowers (Seebeck coefficient) of metals A and B as a function of
temperature and T1 and T2 are the temperatures of the two junctions. The Seebeck coefficients are nonlinear as a function of temperature, and depend on the conductors' absolute temperature, material, and
molecular structure. If the Seebeck coefficients are effectively constant for the measured temperature
range, the above formula can be approximated as:
V  S B  S A   T2  T1 
The Seebeck effect is used in the thermocouple to measure a temperature difference; absolute
temperature may be found by setting one end to a known temperature. A metal of unknown
composition can be classified by its thermoelectric effect if a metallic probe of known composition,
kept at a constant temperature, is held in contact with it. Industrial quality control instruments use this
as thermoelectric alloy sorting to identify metal alloys. Thermocouples in series form a thermopile,
sometimes constructed in order to increase the output voltage, since the voltage induced over each
individual couple is small. Thermoelectric generators are used for creating power from heat
differentials and exploit this effect [2]. Based on the Seebeck effect the water bath temperature is
measured & it is given to the DAQ.
3.1.3. Thermocouple Applications
Thermocouples are suitable for measuring over a large temperature range, up to 2300 °C. They are less
suitable for applications where smaller temperature differences need to be measured with high
accuracy, for example the range 0–100 °C with 0.1 °C accuracy. For such applications thermistors,
silicon band gap temperature sensors and resistance temperature detectors are more suitable.
Applications include temperature measurement for kilns, gas turbine exhaust, diesel engines, and other
industrial processes. The Seebeck effect is used in the thermoelectric generator, which functions like a
heat engine, but is less bulky, has no moving parts, and is typically more expensive and less efficient.
These have a use in power plants for converting waste heat into additional power (a form of energy
recycling), and in automobiles as automotive thermoelectric generators (ATGs) for increasing fuel
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efficiency. Space probes often use radioisotope thermoelectric generators with the same mechanism
but using radioisotopes to generate the required heat difference [2].
3.2. DAQ Hardware
A data acquisition system is devices designed to measure and log some parameters. The purpose of the
data acquisition system is generally the analysis of the logged data and the improvement of the object
of measurements. The data acquisition system is normally electronics based, and it is made of
hardware and software. The hardware part is made of sensors, cables and electronics components. The
software part is made of the data acquisition logic and the analysis software. An example: Data
logging, carried out by a data acquisition system (DAS), can be used to measure parameters such as
temperature and humidity in storage facilities with perishable products; the measurement data is then
stored for analysis to improve quality assurance.
A data acquisition system (DAQ) is a collection of sensors and communication links to sample or
collect and then return data to a central location for further processing, display, or archiving. Data
acquisition is the process of extracting, transforming, and transporting data from the source systems
and external data sources to the host processing system to be displayed, analyzed, and stored. A data
acquisition system (DAQ) typically consist of transducers for asserting and measuring electrical
signals, signal conditioning logic to perform amplification, isolation, and filtering, and other hardware
for receiving analog signals and providing them to a processing system, such as a personal computer.
A data acquisition system may be used to obtain, and possibly record, information about an
environment. Information obtained from the environment by the data acquisition system may be used
to adjust a system operating in or controlling that environment. Digital data processing systems are
employed in many applications, including a variety of laboratory process control, real time data
analysis, and real time data reduction operations, process monitoring and control, data logging,
analytical chemistry, tests and analysis of physical phenomena, and control of mechanical or electrical
machinery. Data recorders are used in a wide variety of applications for imprinting various types of
forms, and documents. Data collection systems or data loggers generally include memory chips or strip
charts for electronic recording, probes or sensors which measure product environmental parameters
and are connected to the data logger. Hand-held portable data collection systems permit in field data
collection for up-to-date information processing [1, 2].
A data acquisition system (DAQ) is a combination of computer hardware and software that gathers,
stores or processes data in order to control or monitor some sort of physical process. A typical data
acquisition system comprises a computer system with DAQ hardware, wherein the DAQ hardware is
typically plugged into one of the I/O slots of the computer system. The DAQ hardware is configured
and controlled by DAQ software executing on the computer system. A data acquisition system mostly
includes transducers, sensors, amplifiers and other means for provision of the signal representation by
their measurement and/or monitoring. These components provide field electrical signals representing a
process, physical phenomena, equipment being monitored or measured, etc. The transducers or other
detecting means convert the physical phenomena being measured into electrical signals, such as
voltage or current, measurable by the DAQ hardware. A data collection system for providing a
controller with data typically comprises a sensor, including a sensing element and an electronic circuit
for converting the output of the sensing element into electric signal, and a readout device for analyzing
the output of the electronic circuit of the sensor. In data acquisition systems, information about a
plurality of parameters is often obtained by simultaneously deploying numerous sensors. Data
acquisition involves interfacing an analog sensor with a recording or display device to measure and
record some value of interest over a period of time. A signal generated by the one or more sensors may
need to be amplified and/or filtered by the data acquisition system for proper operation. Sensors are
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commercially available which can produce environmental information in the form of an electrical or
optical signal about the local area in which the sensor is situated.
The advent of the information age has been made possible by computer technology. Information
processing and handling had been performed by hand on paper. Computers provide an effective and
efficient way for humans to manage, locate, peruse and manipulate data or objects. A personal
computer may be configured by software programs and by plug-in peripheral equipment to perform a
wide variety of special purpose tasks, including data reduction or computation, data acquisition and
control. In the particular area of data acquisition, peripheral devices for performing measurements of
physical phenomena and converting such measurements to digital signals conventionally are attached
to a personal computer through an expansion bus. Messages are transmitted through the expansion bus
to issue commands to instruments and to receive data back in return. Computer systems have been
indispensable in reducing the amount of menial labor surrounding data acquisition and record keeping.
Computer systems can maintain large databases associated with a particular organizations operation.
Typical commercially-available data acquisition systems sample the voltage signal from a sensor in
discrete time interval. Generally, this analog voltage must be converted to a digital signal that the
computer can process and store. This analog-to-digital conversion is typically done with specialized
data acquisition hardware and software which must be installed in a user's computer.
All data acquisition systems generally operate in a similar fashion. They receive an external input from
some type of sensing device, condition and/or convert the input to a format suitable for transmission,
and transmit it a computer. In data acquisition systems, it is necessary to convert one or several analog
signals into one or several digital signals capable of being stored in a digital memory and processed by
a digital processor. Analog signals must be digitized before they can be used by a computer as a basis
for supporting computations. An analog to digital converter is an electrical device that converts an
analog signal to a digital signal. When the analog signal has been converted to a digital signal it can be
processed and stored by computer systems. An analog to digital converter is often fabricated on a
single integrated circuit. Data acquisition systems for generating digital data for the purposes of
computation may receive analog input signals from a plurality of sensors. There are numerous
applications where digital data from analog to digital converters is gathered, stored, and analyzed. Data
and information are constantly being transferred from one location to another.
DAQ act as an interfacing between thermocouple and PC. In thermocouple two leads are there. One is
positive lead and another lead is negative lead. Both that positive and negative leads are connected to
channel-0 at port number 4, 5 in DAQ. The data acquisition hardware is connected to pc through USB
port. The NI cDAQ-9174 is a four-slot NI Compact DAQ chassis designed for small, portable, mixedmeasurement test systems. Combine the cDAQ-9174with up to four NI C Series I/O modules for a
custom analog input, analog output, digital I/O, and counter/timer measurement system [1, 2].
3.3. Bluetooth Connectivity
Bluetooth is a connectivity device used to transfer the data from one device to another. We can transfer
images, audio, files etc., Main goal of our work was to learn things around Bluetooth and to learn to
work with Bluetooth application. In our day to day life we are widely make use of Bluetooth device.
Bluetooth technology was designed primarily to support simple wireless networking of personal
consumer devices and peripherals, including cell phones, PDAs, note books, PCs, printers and wireless
headsets. Wireless signals transmitted with Bluetooth cover short distances, typically up to 30 feet
(10 meters). Bluetooth devices generally communicate at less than 1 Mbps. Here we use Bluetooth as a
main device for both server and client. The PC act as a server and mobile phone as a client. To transfer
the data from PC to mobile both the devices should be in pair [5]. For that we have to know the
Bluetooth address of the both devices.
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For PC:
Bluetooth name : ASHOK-PC
Bluetooth address:90:4c:e5:d1:82:ab
For mobile:
Bluetooth name: Nokia5233
Bluetooth address:10:f9:ee:9d:04:19
4. Software Details
4.1. LabVIEW
Virtual instrumentation is defined as combining hardware and software with industry-standard
computer technologies to create user-defined instrumentation solutions. Virtual Instrumentation is the
use of customizable software and modular measurement hardware to create user-defined measurement
systems, called virtual instruments. A computer-based instrument using digital data acquisition or
generation combined with software algorithms to create the functionality of an instrument. LabVIEW
is a Laboratory Virtual Instrument Engineering Workbench and is a development environment based
on graphical programming. A LabVIEW virtual instrument (VI) roughly equivalent to a subroutine. A
VI has both a diagram (and program) and a front panel (user interface). LabVIEW is a graphical
programming language that uses icons instead of lines of text to create applications. In contrast to textbased programming languages, where instructions determine program execution, LabVIEW uses
dataflow programming, where the flow of data determines execution [2, 3].
LabVIEW is an integral part of virtual instrumentation because it provides an easy-to-use application
development environment designed specifically for engineers and scientists. LabVIEW offers
powerful features that make is easy to connect to a wide variety of hardware and other software. This
ease of use and these features deliver the required flexibility for a virtual instrumentation software
development environment. The result is a user-defined interface and user-defined application
functionality. One of the most powerful features that LabVIEW offers is its graphical programming
paradigm [2, 3].
LabVIEW is programmed with set of icons that represents controls and functions, available in the
menu of the software. The user interface which is called a VI consists of two parts- a front panel and a
block diagram. This is similar to that of an instrument where a front panel is used for an input, output
controls, and to display the data whereas the circuit resides on the circuit board. Similarly you can
bring the buttons, indicators and graphing and display functions on the front panel. Here we use
LabVIEW 2009 software [2, 3].
One benefit of LabVIEW over other development environments is the extensive support for accessing
instrumentation hardware. Drivers and abstraction layers for many different types of instruments and
buses are included or are available for inclusion. These present themselves as graphical nodes. The
abstraction layers offer standard software interfaces to communicate with hardware devices. The
provided driver interfaces save program development time. The sales pitch of National Instruments is,
therefore, that even people with limited coding experience can write programs and deploy test
solutions in a reduced time frame when compared to more conventional or competing systems.
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5. Results and Discussion
The LabVIEW coding is performed for measuring the temperature using block diagram window and
the measured temperature is monitored using the front panel window. The block diagram and front
panel are shown in Fig. 5 and Fig. 6 respectively.
5.1. Block Diagram
Block diagram contains the graphical source code that defines the functionality of the VI Block
diagram is build using the graphical programming language. Nodes are objects on the block diagram
that have inputs and/or outputs and perform operations when a VI runs. They are analogous to
statements, operators, functions, and subroutines in text-based programming languages. The block
diagram of temperature process is shown in Fig. 5.
Fig. 5. Block Diagram Window.
5.2. Front Panel
The front panel is the user interface of a VI. Generally, the front panel is designed first, and then the
block diagram is designed to perform tasks on the inputs and outputs. It is used to set the input value
and viewing the output from the Virtual Instrumentation block diagram. The front panel is built with
controls and indicators, which are the interactive input and output terminals of the VI, respectively.
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Inputs are called controls and the outputs are called indicators. Controls are knobs, push buttons, dials,
and other input devices. Indicators are graphs, LEDs, and other displays. Controls simulate instrument
input devices and supply data to the block diagram of the VI. Indicators simulate instrument output
devices and display data the block diagram acquires or generates. The front panel of temperature
process is shown in Fig. 6.
Fig. 6. Front Panel Window.
5.3. Discussion
J-type thermocouple is inserted into the water bath. The two leads of thermocouple are connected to
the DAQ hardware at 4&5 port of DAQ channel-0. By heating the water the temperature is raised. The
thermocouple is placed inside the water bath when the water gets heated and the temperature value of
the water is sensed by the thermocouple. Among the various types of DAQ hardware we are using is
NI cDAQ-9174. It has four slots, where 3 slots act as an input and output slots and the remaining one
acts as a processor. Each slot having 4 channels a0, a1, a2, a3 and each channel has 6 ports. The
thermocouple leads are connected to the I/O slot in a0 channel. The positive lead is connected to the
port number 4 and the negative lead is connected to the port number 5. The DAQ card is interfaced
with PC using USB port. so that the temperature value is send to LabVIEW by means of DAQ
hardware.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 16-26
PC with LabVIEW software receives the temperature value through DAQ assistant. In the block
diagram window of the LabVIEW temperature measuring coding is performed. In PC DAQ assistant
collects the data from the hardware and waveform chart in the front panel displays the temperature
data. The set point is manually set in the front panel and in the block diagram comparator is used to
compare the actual temperature and the set point. The data from the comparator is given to the case
structure where two conditions will be performed that is “TRUE” or “FLASE”. When the condition is
true “SET POINT IS REACHED” command and the condition is false “SET POINT NOT
REACHED” commands are displayed in the front panel. This command is also send as a SMS to the
mobile phone or PC through the Bluetooth connectivity using LabVIEW. This data is sent with the use
of Bluetooth commands like Bluetooth discover which detects the Bluetooth devices. Then the
Bluetooth read records the data which has been to send and Bluetooth write will send the data to the
mobile phones. When the condition is true the temperature process will be stopped automatically.
6. Conclusion
Here we conclude our real time process “Temperature Measurement Based on LabVIEW and SMS”
which replaces the manual operation of controlling the temperature. The main advantage of our project
is there is no need of human near the temperature system to control the temperature. We are using
LabVIEW software, Data Acquisition hardware and J-type thermocouple to measure and control the
temperature value. The block diagram and front panel diagram in the LabVIEW used to simulate and
execute the program. The temperature of the water is taken and compared with set point which we
given manually inside the front panel diagram. Likewise the waveform will vary according to the
increase or decrease of the temperature. If the temperature exceeds the set point then the command
“SET POINT IS REACHED” is received to the PC through Bluetooth. If the temperature is below the
set point then the command “SET POINT NOT REACHED” is received to the PC. Then by interface
the PC Bluetooth device with the mobile Bluetooth device through Bluetooth address we can send the
report or command from PC to mobile phone which is placed near the Bluetooth discoverable area.
This project can be extended for various types of measurements and control of pressure, level,
humidity etc.
References
[1]. Figueiredo, R. C., Ribeiro, A. M. O., Arthur, R. & Conforti, E., Remote instrumentation control and
monitoring based on LabVIEW and SMS, in Proceedings of the 35th Annual Conference of the IEEE
Industrial Electronics (IECON' 2009), Porto, Portugal, 3-5 November 2009.
[2]. National Instruments website, LabVIEW Run-Time Engine, July 28, 2009. Available online at:
http://joule.ni.com/nidu/cds/view/p/id/1244
[3]. Alsaialy, S. D., Tawy, D. M & Lord, S. M., Introduction to LabVIEW two-part exercise, in Proceedings of
the 33rd Annual Frontiers in Education (FIE), Vol. 1, Nov. 5-8, 2003, pp. T4E-1-6.
[4]. Brown, J., Shipman, B. & Vetter, R., SMS: The Short Message Service, Computer, Vol. 40, No. 12,
Dec. 2007, pp. 106-110.
[5]. J. Campos, E. Jantunen, O. Prakash, Modern maintenance system based on web and mobile technologies,
in Proceedings of the 6th IMA International Conference on Modeling in Industrial Maintenance and
Reliability (MIMAR' 2007), 10-11 September 2007, The Lowry Centre, Salford Quays, Manchester, UK,
pp. 91-95.
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Theoretical Considerations of Fiber Optic Sensors for Thermal
Sensing Under Low and High Temperatures Effects
Ahmed Nabih Zaki Rashed
Electronics and Electrical Communications Engineering Department
Faculty of Electronic Engineering, Menouf 32951, Menoufia University, Egypt
E-mail: ahmed_733@yahoo.com
Received: 19 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Fiber optic sensors were first developed few decades ago for markets where no other
sensing solutions existed, such as applications where high electromagnetic interferences (EMI) could
be present. Typical applications were for instance temperature measurements in microwave ovens or in
high power transformers, strain measurements in electrical welding jaws, pressure measurements for
medical applications. If insensitivity to EMI is probably the most interesting advantage of such
sensors, other interesting advantages are now being considered: since optical technologies proved to be
reliable and accessible, new applications are emerging where reduced size or geometry of such sensors
could be the most interesting features. This paper has presented the important transmission
characteristics of thermal sensors over wide range of the affecting parameters. The free spectral range
(FSR), sensor accuracy, sensor resistance and capacitance, thermal sensing signal quality, sensor
thermal sensitivity and response time are the major interesting design parameters in our current
research under low and high temperature effects. Copyright © 2012 IFSA.
Keywords: Intrinsic sensor, Fiber optic sensors, Thermal sensors, Free spectrum range, Response time
and Signal quality.
1. Introduction
The world of fiber optic sensors lies at the intersection of fiber optic communication and
optoelectronics. Fiber optic sensors offer many advantages over conventional electrical or
electromechanical sensors [1]. First, optical fiber is a dielectric, so it is not subject to interference from
electromagnetic waves that might be present in the sensing environment. Secondly fiber optic sensors
can function under harsh environment, such as high temperature, toxic or corrosive atmospheres where
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
metals or other materials can be corroded. In addition, semiconductor based photodectctors and laser
diode sources are usually small and light, so fiber-optic sensors are useful as sensing devices for wider
range of physical and chemical phenomena that include temperature, pressure, acoustic field, position,
rotation, electrical current [2], liquid level, biochemical composition, and chemical concentration.
Indeed, fiber-optic sensors can perform the functions of virtually any conventional sensor and even
faster and with greater sensitivity. Particularly, they can perform measurement tasks that would be
impracticable with conventional sensors. For instance, they can be embedded in critical structures,
such as airplanes and bridges, reporting continuously on structural integrity, and possibly averting a
catastrophic failure [3]. The numerous advantages of fiber optic sensors will ensure that they continue
to attract research funding for their further development. Even more noteworthy is the fact that
commercially available fiber optic sensors are increasing. It is a promising field with clear advantages
over conventional sensors in certain applications [4].
Fiber optic sensors have many advantages such as ease of embedding, flexible sensor size, wide
temperature range, high sensitivity and etc [5]. Thus, fiber optic sensors have been introduced into
many composite structures. Especially, FBG (Fiber Bragg Grating) strain sensors have noticeable
attractions due to multiplexing capability, linear response and absolute measurement [6]. However, the
use of FBG sensors is limited by their simultaneous dependence on strain and temperature, directional
sensitivity variation, weakened sensor head due to fabrication process, etc [7]. In case of detecting high
frequency signals, multiplexing capability is worse and conversely, most multiplexed FBG systems
have low frequency ranges. And the sensitivity fadeout problem in the intensity demodulation method
is another issue for FBG vibration sensor system. To overcome sensitivity fadeout problems, some
passively controlled systems for a single head FBG sensors system were suggested but do not
guarantee uniform sensitivity [8]. And to measure internal and external strains of the composite
pressure vessels in real time, the mechanical failure of FBG sensors or optical fiber and the spectral
distortion in reflected signals have to be overcome [9]. Thus, in order to implement FBG sensors to
real structures, much attention has been paid to overcome these limitations.
In the present study, fiber optic sensor technology has been and is being increasingly exploited by the
research community because of its relatively simple design, low power consumption, low cost,
relatively low maintenance cost, and the flexibility it offers for both commercial and military
applications. In particular, fiber optic thermal sensors have been recognized as promising technologies
for numerous applications, which include intruder detection and perimeter multiplexing systems for
commercial applications.
2. Intrinsic Fiber Optic Sensors
Fiber optic sensors have advantages over other sensors. They have a further range, lower cost, and
generally smaller in size. These sensors can be intrinsic or extrinsic [10]. Fig. 1 represents a basic
intrinsic sensor with length Ls, sensor diameter (Ds=Dclad) is equal to fiber cladding diameter and its
length, Lf, sensor refractive index (ns=n2) is equal to cladding refractive index and based on polymer
fiber cladding as a guidance of temperature sensing technology. This sensor uses the optical fiber that
is carrying the light, and detects an environmental effect which forces information on the light inside
the fiber.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Fig. 1. Schematic diagram of the core based fiber optic sensor.
Optical fibers can be used as sensors to measure strain, temperature, pressure and other quantities by
modifying a fiber so that the quantity to be measured modulates the intensity, phase, polarization,
wavelength or transit time of light in the fiber. Sensors that vary the intensity of light are the simplest,
since only a simple source and detector are required. A particularly useful feature of intrinsic fiber
optic sensors is that they can, if required, provide distributed sensing over very large distances [11].
3. Theoretical Model Analysis
The investigation of both the thermal and spectral variations of the refractive index require empirical
equation. The set of parameters required to completely characterize the temperature dependence of the
refractive index of both fiber core and sensor are given below, Sellmeier equation is under the form
[12]:
nc 
ns 
A12
2  A22
B12
2  B22


A3 2
2  A42
B32
2  B42


A5 2
2  A62
B52
2  B62
,
(1)
,
(2)
The thermo-optic effect and spectral variations are present in all transparent materials and describes
the dependence of the material index of both fiber core and sensor can be expressed as [13]:
A2
A
A 

A3 A4 4
A5 A6 6 
2   A1 A2

dnc   
T 
T 
T
 


dT  nc   2  A2 2 2  A2 2 2  A2 2 
2
4
6


(3)
B2
B
B 

BB
B3 B4 4
B5 B6 6 
dns  2   1 2 T
T 
T
 



2
2


2
2
2
2 2
dT  ns   2  B 2



B

B
2
4
6


(4)


 
 
 
 


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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
dnc
  A1 A22
 
d
nc  2  A2
2


dns
  B1B22
 
d
ns  2  B 2
2



A3 A42
 
2
2

 
2
 A42
B3 B42
2
 B42



2
2 2
  A6 
(5)



2
2 2
  B6 
(6)
 
2
 
2
A5 A62
B5 B62


where the set of the parameters of empirical equation coefficients for different polymeric materials
based both sensor and fiber core as a function of ambient temperature T, and room temperature T0 are
listed in Table 1.
Table 1. Sellemier coefficients for polymeric materials based both fiber core and sensor [12, 17, 19].
Coefficients
Material based fiber core
Polystyrene (PS)
Coefficients
A1
A2
A3
A4
A5
A6
0.08432
12.07654 (T/T0)
2.06543
0.976542 (T/T0)
0.007431
47.20652 (T/T0)
B1
B2
B3
B4
B5
B6
Material based sensor
Polymethylmethacrylate
(PMMA)
0.4963
0.6965 (T/T0)
0.3223
0.718 (T/T0)
0.1174
9.237 (T/T0)
The index of refraction for the polymer fiber from which the optical fibers are made is temperature
dependent, causing the center wavelength of the sensor to be temperature dependent as well. The
effective refractive index of the fiber core and sensor materials is given by [14]:
neff 
n
2
c

 ns2 b  ns2
,
(7)
where b is the normalized propagation constant and is given by [15]:
2
0.9660 

b  V   1.1428 
 ,
V 

(8)
where V is the normalized frequency. For single mode step index optical fiber waveguide, the cut-off
normalized is approximately V= Vc= 2.405, and by substituting in Eq. (8), we can get the normalized
propagation constant b at the cut-off normalized frequency approximately b ≈ 0.5, and then by
substituting in Eq. (7), then the deuced expression:

neff  0.5 nc2  ns2

,
(9)
The effective refractive index neff is dependent on the refractive indices of the fiber and sensor
materials, then by selecting proper materials of the sensor and fiber core to satisfy Eq. (9), an a thermal
sensor can be designed. Differentiation of Eq. (7) with respect to both optical signal wavelength λ and
ambient temperature T, which yields:
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
 0 .5

 neff
d

  dn c
dn 
 nc
 ns s 
  d 
d 

,
(10)
 0 .5

 neff

  dn c
dn 
 nc
 ns s 
  dT
dT 

,
(11)
dn eff
dn eff
dT
By solving the coupled mode equations, the transmission property of light propagating along the fiber
can be obtained. The free spectrum range (FSR) of fiber optic sensor can be given as follow [16]:
FSR 
2
,
 neff Ls
(12)
where Ls is the sensor length in mm, neff is the effective index of the mode propagating in the fiber
and λ is the optical signal wavelength in μm. The thermal sensing quality factor (Qs) of the sensor can
be calculated as [17]:
Qs 

FWHM
,
(13)
where FWHM is the full width at half maximum which is applied to such phenomena as the duration
of pulse waveforms and the spectral width of sources used for optical communications and the
resolution of spectrometers and can be estimated as the following formula [18]:
FWHM  2.35482 BWsig .
(14)
where BWsig is the transmitted signal bandwidth for single mode fiber, which is given by [19]:
BWsig . 
0.44

(15)
where τ is the total pulse broadening through fiber core and is given by:
  L f Dt s
(16)
where Lf is the fiber length, Δλs is the spectral linewidth of the optical source in nm, and Dt is the total
dispersion coefficient based standard multi mode fiber (MMF) which is given by [20]:
Dt  Dmat.  DP ,
(17)
where Dmat and DP are the material and profile dispersion respectively, which they can be estimated as
[21]:
Dmat.
2
 d neff

,
c d2
(18)
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
  N n  2  g  2  
DP    1  
  c   g  2

2

2 g 
 
 3 g  2 
0.5
(19)
where N1 is the group index for the mode which is given by:
N1  neff  
dneff
,
d
(20)
where C1 is a constant related to index exponent and profile dispersion and is given by:
g  2
g2
C1 
,
(21)
where g is the index exponent, and  is the profile dispersion parameter and is given by:
 
2 neff 
N1 n
,
(22)
Δn is the relative refractive index difference and is defined as:
n 
nc  ns
nc
,
(23)
When the temperature is changed, the length and index of the fiber will be varied, which shifts the
wavelength correspondingly. In order to obtain the wavelength in a dynamic temperature field, we
make a derivation calculus to Eq. (12) on temperature, thus the relationship can be evaluated as below
[22]:


shift   ps   ps  T
(24)
where ΔT is the temperature variations above room temperature (T-T0), αps is the coefficient of thermal
expansion of the polystyrene fiber, βps=1/nc (dnc/dT) is the thermal optical coefficient of the
polystyrene fiber. Generally, the wavelength shift Δλ is small compared with wavelength λ. The
response time in heating and cooling processes of these sensors can be described by the lumped system
equation [23]. For these cylindrical polymers fiber with radius rf and sensor with radius rs, the thermal
sensing response time equation can be described as:
TR 
c p  r f  rs 
2h
(25)
where ρ is the density of the fiber material, cp is its the specific heat, and h is the convection
coefficient. it is indicated that the higher thermal sensing response time, the lower thermal sensing
process. The intensity transmission coefficient Ts, representing the ratio of the transmission intensity
to the input intensity, can be obtained according to general principle of fiber optic sensor [16, 23] and
has the expression:
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Ts 


exp   ps Ls  (sin( K ) 2

1  exp   ps Ls
 sin K 2

exp  c p / h

(26)
where K=0.5(2m+1)π is the coupling parameter, and m is an integer. Following the sensing
mechanism the sensitivity can be defined as the following formula [24]:
SS 
T
neff
 dneff

 dT

 ,

(27)
The fiber optic sensor thermal resistance Rs, for both low and high temperatures can be [25]:
 1
1  
Rs  RRe f . exp   
,
  T TRe f .  

 
(28)
where Ref. is the reference resistance and is equal to 50 Ω and 20 Ω at low and high temperature
respectively, γ is a coefficient and is equal to 0.81365103/°C, and TRef is the reference temperature
and is equal to 75 °C and 825 °C at low and high temperatures respectively. As well as the fiber optical
sensor capacitance Cs can be given by [26]:
Cs 
2   0  r Ls
,
ln rs / rc 
(29)
where ε0 is the permittivity of free space, εr is the relative permittivity and is equal to 2.453 for PMMA
material based fiber optic sensor. Therefore the fiber optic sensor operating frequency fos, is given by:
f os 
0.263
,
Rs Cs
(30)
The total temperature error (TTE) and is related to the sensor accuracy (SA) percentage at both low
and high temperatures respectively can be given by the following formulas [27, 28]:
1
1

TTE L h1 (75 C  T ) (T  25 C )  h2 (T  25 C )  TE25 C
(31)
1
1

TTE H h3 (125 C  T ) (T  125 C )  h4 (T  825 C )  TE125 C
(32)
S AL (%) 
S AH (%) 
where h1=15010-6/°C, h2=710-3, TE25 °C =0.5 °C, h3=-20010-6/°C, h4=-110-3, and TE125 °C=0.6 °C.
4. Simulation Results and Performance Analysis
We have investigated the core based intrinsic fiber optic absorption sensor over wide range of the
affecting operating parameters as shown in Table 2. The Fiber optic sensors have developed fir thermal
sensing over wide temperature range variations to be tested its high thermal sensitivity and sensor
accuracy.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Table 2. Proposed operating parameters for electro-absorption modulators [2, 5, 7, 12, 15].
Parameter
Tlow
Thigh
αps
Ls
T0
ΔTlow
ΔThigh
λ
rs=0.5 Ds
rf=0.5 Df
Δλs
βps
ρ
cp
h
Lf
g
m
ε0
Definition
Low ambient temperature
High ambient temperature
Thermal expansion coefficient
Sensor length
Room temperature
Low temperature variations
High temperature variations
Optical signal wavelength
Sensor radius
Fiber core radius
Spectral linewidth of optical source
Thermal optical coefficient
Fiber material density
Specific heat
Convection coefficient
Fiber length
Index exponent
Integer
Permittivity of free space
Value and unit
25 °C - 75 °C
125 °C - 825 °C
-110-5/°C
5 mm - 10 mm
25 °C
0 °C - 50 °C
100 °C - 800 °C
1.3 μm - 1.65 μm
210 - 250 μm
200 μm
0.1 nm
510-5/°C
1.102 g/cm3
0.00874 J/g/T, T in °C
0.04 Watt/cm2.T, T in °C
50 mm
2
1
8.85410-14 f/cm
Based on the model equations analysis, assumed set of the operating parameters, and the set of the
series of the Figs. (2-22), the following facts are assured:
i) Figs. (2, 3) have indicated that FSR increases with increasing both ambient temperatures with its
low and high values and operating optical signal wavelength. It is indicated that FSR has presented
its higher values under high temperatures effects compared to lower values under low temperatures
effects.
ii) As shown in Figs. (4, 5) have assured that FSR decreases with increasing sensor length under low
and high temperature effects. While FSR has presented its higher values under high temperatures
effects compared to lower values under low temperatures effects.
iii)Figs. (6, 7) have demonstrated that as both operating optical signal wavelength and ambient
temperatures with its low and high values increase, this leads to increase in thermal sensing quality
factor. It is observed that thermal sensing quality factor has shown its higher values under high
temperatures effects compared to lower values under low temperatures effects.
iv) As shown in Figs. (8, 9) have assured that wavelength shift increases with increasing both ambient
temperatures and operating optical signal wavelength under low and high temperature effects.
While wavelength shift has shown its higher values under high temperatures effects compared to
lower values under low temperatures effects.
v) Figs. (10, 11) have demonstrated that as both sensor radius and ambient temperatures with its low
and high values increase, this results in increasing thermal sensing response time. It is observed that
thermal sensing response time has shown its higher values under high temperatures effects
compared to lower values under low temperatures effects.
vi) As shown in Figs. (12, 13) have assured that fiber optic sensor transmission decreases with
increasing sensor length under low and high temperature effects. Where fiber optic sensor
transmission has presented its higher values under low temperatures effects compared to lower
values under high temperatures effects.
vii) Figs. (14, 15) have demonstrated that as ambient temperatures with its low and high values
increase, and operating optical signal wavelength decreases, this results in increasing sensor
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
thermal sensitivity. It is observed that sensor thermal sensitivity has shown its higher values under
high temperatures effects compared to lower values under low temperatures effects.
viii) As shown in Figs. (16, 17) have assured that fiber optic sensor resistance decreases with
increasing temperature effects. Where fiber optic sensor resistance has presented its higher values
under low temperatures effects compared to lower values under high temperatures effects.
ix) Fig. 18 has demonstrated that as sensor length increases and sensor radius decreases, this leads to
increase in sensor capacitance.
x) Figs. (19, 20) have indicated that as sensor length decreases and ambient temperatures with its low
and high values increase, this results in increasing sensor operation frequency. It is observed that
sensor operation frequency has shown its higher values under high temperatures effects compared
to lower values under low temperatures effects.
xi) Figs. (21, 22) have assured that total temperature error decreases and then sensor accuracy
increases under low temperature effects. While total temperature error increases and then sensor
accuracy decreases under high temperature effects.
0,006
Optical signal wavelength λ=1.3
μm
Optical signal wavelength λ=1.45
μm
Optical signal wavelength λ=1.65
Free spectrum range, FSR, μm
0,0055
0,005
0,0045
0,004
0,0035
0,003
0,0025
0,002
0,0015
0,001
25
30
35
40
45
50
55
60
Low ambient temperature, T, °C
65
70
75
Fig. 2. Free spectrum range of fiber optic sensor in relation to low ambient temperatures and operating optical
signal wavelength at the assumed set of the operating parameters.
0,8
Optical signal wavelength λ=1.3 μm
Free spectrum range, FSR, μm
0,7
0,6
Optical signal wavelength λ=1.45
μm
Optical signal wavelength λ=1.65
0,5
0,4
0,3
0,2
0,1
125
225
325
425
525
625
High ambient temperature, T, °C
725
825
Fig. 3. Free spectrum range of fiber optic sensor in relation to high ambient temperatures and operating optical
signal wavelength at the assumed set of the operating parameters.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
0,006
Room temperature T0=25 °C
Free spectrum range, FSR, μm
0,0055
Low ambient temperatures
0,005
Ambient temperature T=50 °C
Ambient temperature T=75 °C
0,0045
0,004
0,0035
0,003
0,0025
0,002
0,0015
0,001
0,0005
0
5
6
7
8
9
Sensor length, Ls, mm
10
Fig. 4. Free spectrum range of fiber optic sensor in relation to sensor length and low ambient temperatures
and at the assumed set of the operating parameters.
0,7
Ambient temperature T=125 °C
Free spectrum range, FSR, μm
0,65
0,6
Ambient temperature T=500 °C
High ambient temperatures
0,55
Ambient temperature T=825 °C
0,5
0,45
0,4
0,35
0,3
0,25
0,2
0,15
0,1
0,05
0
5
6
7
8
9
10
Sensor length, Ls, mm
Fig. 5. Free spectrum range of fiber optic sensor in relation to sensor length and high ambient temperatures
and at the assumed set of the operating parameters.
Thermal sensing quality factor, Qs
100000
Room temperature T0=25 °C
Ambient temperature T=50 °C
Ambient temperature T=75 °C
10000
1000
100
1,3
1,35
1,4
1,45
1,5
Low ambient temperatures
1,55
1,6
1,65
Operating optical signal wavelength, λ, μm
Fig. 6. Thermal sensing quality factor of fiber optic sensor in relation to operating optical signal wavelength
and ambient temperature at the assumed set of the operating parameters.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Thermal sensing quality factor, Qs
1000000
Ambient temperature T=125 °C
Ambient temperature T=500 °C
Ambient temperature T=825 °C
100000
High ambient temperatures
10000
1,3
1,35
1,4
1,45
1,5
1,55
1,6
1,65
Operating optical signal wavelength, λ, μm
Fig. 7. Thermal sensing quality factor of fiber optic sensor in relation to operating optical signal wavelength
and ambient temperature at the assumed set of the operating parameters.
Wavelength shift, Δλshift, nm/°C
0,007
Optical signal wav elength λ=1.3 μm
0,006
Optical signal wav elength λ=1.65 μm
0,005
0,004
0,003
0,002
0,001
0
0
5
10
15
20
25
30
35
40
45
50
Low temperature variations, ΔT, °C
Fig. 8. Wavelength shift in relation to low temperature variations and operating optical signal wavelength
at the assumed set of the operating parameters.
Wavelength shift, Δλshift, nm/°C
1,2
1,1
Optical signal wav elength λ=1.3 μm
Optical signal wav elength λ=1.65
μm
1
0,9
0,8
0,7
0,6
100
200
300
400
500
600
700
800
High temperature variations, ΔT, °C
Fig. 9. Wavelength shift in relation to high temperature variations and operating optical signal wavelength
at the assumed set of the operating parameters.
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Thermal sensing response time, TR, msec
0,14
Sensor radius, rs=210 μm
Sensor radius, rs=225 μm
0,12
Sensor radius, rs=250 μm
0,1
0,08
0,06
0,04
0,02
25
30
35
40
45
50
55
60
65
70
75
Low ambient temperature, T, °C
Fig. 10. Thermal sensing response time of fiber optic sensor in relation to low ambient temperatures
and sensor radius at the assumed set of the operating parameters.
Thermal sensing response time, TR, msec
2
Sensor radius, rs=210 μm
Sensor radius, rs=225 μm
Sensor radius, rs=250 μm
1,75
1,5
1,25
1
0,75
0,5
125
225
325
425
525
625
725
825
High ambient temperature, T, °C
Fig. 11. Thermal sensing response time of fiber optic sensor in relation to high ambient temperatures
and sensor radius at the assumed set of the operating parameters.
Fiber optic sensor transmission, Ts
80%
Ambient temperature T=25 °C
Ambient temperature T=50 °C
Ambient temperature T=75 °C
70%
60%
50%
40%
30%
20%
10%
5
Low ambient temperatures
6
7
8
Sensor length, Ls, mm
9
10
Fig. 12. Intensity transmission of fiber optic sensor in relation to sensor length and low ambient
temperatures and at the assumed set of the operating parameters.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Fiber optic sensor transmission, Ts
31%
29%
Ambient temperature T=125 °C
27%
Ambient temperature T=500 °C
25%
Ambient temperature T=825 °C
23%
21%
19%
17%
15%
13%
11%
9%
7%
5%
3%
5
6
7
8
9
10
Sensor length, Ls, mm
Fig. 13. Intensity transmission of fiber optic sensor in relation to sensor length and high ambient
temperatures and at the assumed set of the operating parameters.
Sensor thermal sensitivity, SSx10-5/°C
3
Optical signal wavelength λ=1.3 μm
2,75
Optical signal wavelength λ=1.45
μm
Optical signal wavelength λ=1.65
μm
2,5
2,25
2
1,75
1,5
1,25
1
0,75
25
30
35
40
45
50
55
60
Low ambient temperature, T, °C
65
70
75
Fig. 14. Thermal sensitivity of fiber optic sensor in relation to low ambient temperatures and operating
optical signal wavelength at the assumed set of the operating parameters.
0,12
Sensor thermal sensitivity, SS/°C
0,11
0,1
0,09
0,08
Optical signal wavelength λ=1.3 μm
Optical signal wavelength λ=1.45
μm
Optical signal wavelength λ=1.65
μm
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0
125
225
325
425
525
High ambient temperature, T, °C
625
725
825
Fig. 15. Thermal sensitivity of fiber optic sensor in relation to high ambient temperatures and operating
optical signal wavelength at the assumed set of the operating parameters.
39
Sensor thermal resistance, Rs, Ω
Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
1000
950
900
850
800
750
700
650
600
550
500
450
400
350
300
250
200
150
100
50
Sensor resistance Rs at low temperatures
25
35
45
55
65
Low ambient temperature, T, °C
75
Fig. 16. Thermal resistance of fiber optic sensor in relation to low ambient temperatures
at the assumed set of the operating parameters.
140
Sensor thermal resistance, Rs, Ω
Sensor resistance Rs at high temperatures
120
100
80
60
40
20
125
225
325
425
525
High ambient temperature, T, °C
625
725
825
Fig. 17. Thermal resistance of fiber optic sensor in relation to high ambient temperatures
at the assumed set of the operating parameters.
5,5
Sensor radius, rs=210 μm
Sensor capacitance, Cs, nF
5
Sensor radius, rs=225 μm
Sensor radius, rs=250 μm
4,5
4
3,5
3
2,5
2
1,5
1
0,5
0
5
6
7
8
9
10
Sensor length, Ls, mm
Fig. 18. Capacitance of fiber optic sensor in relation to both sensor length and radius and
at the assumed set of the operating parameters.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 27-44
Sensor operation frequency, fos, MHz
600
Sensor length Ls=5 mm
Sensor length Ls=7.5 mm
Sensor length Ls=10 mm
550
500
450
400
350
300
250
200
150
25
35
45
55
65
75
Low ambient temperature, T, °C
Fig. 19. Operation frequency of fiber optic sensor in relation to low ambient temperatures
and sensor length at the assumed set of the operating parameters.
Sensor operation frequency, fos, MHz
750
700
650
Sensor length Ls=5 mm
Sensor length Ls=7.5 mm
Sensor length Ls=10 mm
600
550
500
450
400
350
300
250
125
225
325
425
525
625
725
825
High ambient temperature, T, °C
Fig. 20. Operation frequency of fiber optic sensor in relation to high ambient temperatures
and sensor length at the assumed set of the operating parameters.
16
TTE at low temperatures
14
SA at low temperatures
0,4
12
0,35
0,3
10
0,25
8
0,2
0,15
6
0,1
4
0,05
Sensor accuracy, SAL(%), /°C
Total temperature error, TTEL, °C
0,5
0,45
2
0
25
35
45
55
65
75
Low ambient temperature, T, °C
Fig. 21. Total temperature error and sensor accuracy in relation to low ambient temperatures
at the assumed set of the operating parameters.
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Total temperature error, TTEH, °C
TTE at high temperatures
1,2
1,8
SA at high temperatures
1,1
1,6
1
1,4
0,9
0,8
1,2
0,7
1
0,6
0,5
Sensor accuracy, SAH(%), /°C
2
1,3
0,8
125
225
325
425
525
625
725
825
High ambient temperature, T, °C
Fig. 22. Total temperature error and sensor accuracy in relation to high ambient temperatures
at the assumed set of the operating parameters.
5. Conclusions
In a summary, we have deeply presented the fiber optic sensor for thermal sensing under low and high
temperatures over wide range of the affecting parameters. It is theoretically found that the increased
low and high temperature effects, this result in increasing in free spectrum range, thermal sensing
quality factor, sensor wavelength shift, thermal sensing response time, sensor thermal sensitivity, and
sensor operation frequency, and decreasing in sensor resistance and fiber optic sensor transmission. As
well as it is indicated that the increased operating optical signal wavelength, this lead to the increased
FSR, sensor wavelength shift, and the decreased sensor thermal sensitivity. Moreover it is observed
that the increased sensor length, this result in the increased sensor capacitance and the decreased FSR,
sensor operation frequency and fiber optic sensor transmission. It is also found that the increased
sensor radius, this lead to the increased thermal sensing response time, and the decreased sensor
capacitance. Finally it is theoretically observed that at low temperatures effects, the total temperature
error decreases, and therefore the sensor accuracy increases. While at low temperatures effects, the
total temperature error increases, and therefore the sensor accuracy decreases.
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___________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 45-61
Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Effect of Firing Temperature on the Micro Structural
Parameters of Synthesized Zinc Oxide Thick Film Resistors
Deposited by Screen Printing Method
a*
Ratan Y. BORSE, b Vaishali. T. SALUNKE and c Jalinder AMBEKAR
a,b
Thin and Thick Film Laboratory, Department of Electronic Science, M. S. G. College,
Malegaon Camp - 423105 (Nasik), India
c
Centre for Materials for Electronics Technology (CMET) Pune, India
Tel.: (02554) 252077, fax: (02554) 251705
a*
E-mail: ratanborse@yahoo.co.in
Received: 26 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Zinc-oxide (ZnO) powder was prepared by synthesis of zinc nitrate using self propagation
solution combustion method in air with the purpose of modifying the powders physico-chemical
properties. Textured ZnO thick film resistors (TFRs) have been deposited on alumina using standard
screen printing method. The films were fired at 500, 600 and 700 °C for 2h firing cycle in air. The
influence of firing temperature on the structural and morphological properties of the TFRs was
investigated. X-ray diffraction (XRD) studies indicate the formation of polycrystalline hexagonal
(wurtzite) crystal structure with preferential orientation along (101) plane. X-ray line broadening
technique is adopted to study the effect of firing temperature on microstructural parameters such as
interplaner spacing, average grain size, microstrain, dislocation density due to microstrain, stacking
fault probability and texture coefficient. The dependence of these values on the firing temperature was
established, which enabled analysis of the evolution of the defect structure of zinc-oxide TFRs during
the firing. Scanning electron microscopic (SEM) images of the films fired at 500 and 600 oC are more
porous as compared to TFR fired at 700 oC. The grain size is little bit larger as the increase of firing
temperature although the increase is not significant. Copyright © 2012 IFSA.
Keywords: ZnO, TFRs, Firing temperature, SEM, Texture coefficient.
1. Introduction
Zinc oxide has proven itself as one of the competitive and promising candidates to replace expensive
materials like CdS, TiO2, GaN, SnO2, and In2O3 for applications such as solar cells [1], photocatalysis
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 45-61
[2], ultraviolet laser [3, 4], transparent conductive oxides [5], spintronics [6], and gas sensors [7]. Zinc
oxide (ZnO) is an II–VI group n-type semiconductor material with a hexagonal Wurtzite crystal
structure [8, 9], a strong cohesive energy of 1.89 eV [10], a high optical gain (300 cm-1) [11], high
mechanical and thermal stabilities [12, 13], and radiation hardness [14-18]. ZnO possesses a Wurtzite
structure similar to GaN [19, 20], which is widely used in high-performance optoelectronic devices.
Zinc oxide is a direct wide-band-gap semiconductor with a band gap of 3.37 eV at room temperature
and has been investigated extensively for its electrical, optical and photocatalytic properties. It has
been used in applications such as laser diodes, solar cells, gas sensors, field effect transistors,
transparent conductive films, hydrogen production by mean to water photolysis and degradation of
organic pollutants [21-25]. Their absorbing optical, electronic, and mechanical properties are highly
creditable for promising nano devices. For example, epitaxial ZnO films have demonstrated enormous
potential for developing blue lasers and light emitting diodes [26-28]. The films have been prepared by
various dry processes such as pulsed laser deposition (PLD) [29], metal organic chemical vapor
deposition (MOCVD) [30], chemical vapor deposition (CVD) [31], molecular beam epitaxy (MBE)
[32], magnetron sputtering [33], and electron beam evaporation [34]. Wet processes such as
electrochemical deposition [35], spray pyrolysis [36, 37], sol–gel [38, 39], and hydrothermal method
[40-42] are also valuable for the preparation of the oxide film.
Various chemical synthesis methods have been employed by several workers to synthesize nano/micro
crystals such as solvothermal, hydrothermal, self assembly and sol-gel, etc [43-47]. Various workers
have been working on synthesis and characterization of different nanostructures of pure and doped
zinc oxide phosphors. M. Jayalakshmi et al have explained the synthesis of nano crystals of zinc oxide
using self propagation combustion method starting with products of zinc nitrate and dextrose [48].
Zinc Oxide nanostructures could be synthesized by several techniques such as vapor deposition,
oxidation, sputtering, and pulse laser deposition. The self propagation combustion method was used
for the preparation of ZnO powder, though there are several methods of preparation, as this method is
easy with compared to other methods and the chemicals required for these methods are easily available
and cheap. In this work, ZnO films were prepared by standard screen printing method on alumina
substrates. The effect of firing temperature on crystalline structure of the film was investigated.
The aim of this work is to produce high-quality synthesized ZnO thick film resistors for structural
properties. Special attention was paid to the influence of the firing temperature on the structure and
morphology of the thick film resistors.
2. Materials and Experimental Methods
2.1. Synthesis of ZnO Powder
Zinc oxide nano structured powder was prepared by self propagating solution combustion method [48].
The starting materials are Zinc nitrate and Dextrose. Proper amount of zinc nitrate and dextrose are
dissolved in water contained beaker and placed on a hot plate for 15 minutes as the solution dehydrates
to form a deposition like a gel. Then the beaker was placed in a preheated muffle furnace at 500 oC
temperatures. The solution boils, ignites with a flame and the entire reaction was completed within
5 minutes. The powder is amorphous in nature. Then the powder was calcined at 650 oC to get
nanocrystalline ZnO powder. The XRD pattern of this confirms the formation of ZnO.
2.2. Preparation of Thick Film Resistors
For ZnO thick film resistors, the inorganic to organic materials ratio was maintained as 70:30 %. In
inorganic materials, the synthesized ZnO powder was used as a functional material. The ZnO powder was
weighed and calcined in air atmosphere at 600 oC for 2 h. The ratio of active ZnO powder to permanent
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 45-61
binder was kept as 95:5 % in 70 % part. Glass frit (70 wt. % PbO, 18 wt. % Al2O3, 9 wt. % SiO2 and 3
wt. % B2O3) was used as a permanent binder [49, 50]. Organic part consist of 8 % ethyl cellulose
(EC, Loba Chemicals) as a temporary binder and 92 % butyl carbitol acetate (BCA liquid, Merck,
Munchen, C10H20O4, BP 245 oC) as a vehicle to make the paste. Butyl carbitol acetate was added
drop by drop to obtain the proper viscosity and thixotropic properties of the paste.
The calcined ZnO powder was mixed and crushed thoroughly with glass frit and ethyl cellulose in an
acetone medium in mortar and pestle. During this mixing process, BCA was added drop by drop to
obtain the proper viscosity of the paste. This paste should have the thixotropic property for
printing through the screen on the substrate. The paste was used to prepare thick film resistors on
alumina substrate by using standard screen printing method using 140 s mesh no. 355. After screen
printing, the films were dried under IR-lamp for 60 minutes [51] and then fired at temperatures of 500,
600 and 700oC for 2 h firing cycle in muffle furnace.
2.3. X-ray Diffraction Analysis
To study the microstructural detail of the thick film resistors, X-ray diffractometer [Miniflex Model,
Rigaku Japan] using CuKα, radiation (λ=0.1542 nm) with a 0.1o/step (2θ) at the rate of 2 s/step was
employed. A range of 2θ from 20–80° was scanned from a fixed slit type, so that all possible
diffraction peaks could be detected. X-ray diffraction technique was used to determine the crystalline
structure and preferential orientation of the crystallite materials and also to calculate the crystallite
size. The average grain size of ZnO was calculated by using Scherrer’s formula [52].
D
0 .9 
 cos 
(1)
where D is the crystallite size, β is the full width half maxima of the (101) peak of the XRD pattern, λ
is the wavelength of X-ray radiation (1.542 Ǻ) and θ is the diffraction angle.
XRD-data is adopted to determine microstructural parameters such as texture coefficient, microstrain,
dislocation density and stacking fault probability at different firing temperatures.
2.4. Surface Morphology by Scanning Electron Microscopy (SEM)
A scanning electron microscopy (Model SEM-JOEL JED-2300) was employed to characterize the
surface morphology of s fired at 500, 600 and 700 oC. The composition of ZnO thick film samples
were analyzed by an energy dispersive X-ray spectrometer (EDX) (JOEL-JED 6360 LA). For SEM all
the ZnO thick film samples were coated with a very thin conducting gold layer (few100Å) using
vacuum evaporation/sputtering technique to avoid charging of the samples. The thickness of the ZnO
thick film resistors was measured using a Taylor-Hobson (Taly-step UK) system.
3. Results and Discussion
3.1. Characterization of Synthesized ZnO Powder
XRD profile of the synthesized ZnO powder prepared by using self propagation combustion method is
shown in Fig. 1. It is clearly seen that powder has hexagonal wurtzite crystal structure (JCPDS
36- 1451). Grain size of the powder is 30 nm as calculated using Debye Scherrer method.
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Fig. 1. XRD pattern of synthesized ZnO powder.
3.2. Fabrication of ZnO TFRs
ZnO Thick Film Resistors were prepared by standard screen printing method. The thick film resistor
samples were good without crack. Thickness of the thick film resistors was observed to be uniform in
the range of 25 μm to 35 μm.
3.3. Elemental Analysis (EDAX Analysis)
The elemental analysis of the synthesized ZnO thick film resistors fired at 500, 600 and 700 oC was
carried out using EDAX (JEON, JED-6360 LA, Germany). The EDAX analysis shows presence of
only Zn and O as expected, no other impurity elements were present in the ZnO thick films. From the
EDAX spectra, it is found that wt% and at% of Zn and O is nearly matched. Table 1 gives quantitative
elemental analysis of ZnO thick films.
Table 1. Composition of the ZnO Thick film resistors at different firing temperatures.
Firing Temperature
500 oC
600 oC
700 oC
Element
Zn
O
Zn
O
Zn
O
At. Wt. %
77.33
22.67
79.67
20.33
80.39
19.61
Mass %
93.47
6.53
94.12
5.88
94.37
5.63
Fig. 2 (a, b, c) shows the EDAX spectra of ZnO thick film resistors fired at different temperatures. The
mass percentage of Zn was found to increase with an increase of the firing temperature due to release
of excess oxygen [53]. It was found that the ZnO films are non-stoichiometric. The deficiency or
excess of any type of atom in the crystal results in a distorted band structure, with a corresponding
increase in conductivity. Zinc oxide looses oxygen on heating so that zinc is then in excess. The
oxygen, of course, evolves as an electrically neutral substance so that it is associated with each excess
zinc ions in the crystal. There will be two electrons that remain trapped in the solid material, thus
leading to non-stoichiometricity in the solid. This leads to the formation of the n-type semiconductor
[54].
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Fig. 2 (a, b). EDAX Spectra of ZnO thick films fired at (a) 500, (b) 600 oC.
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Fig. 2 (c). EDAX Spectra of ZnO thick films fired at (c) 700 oC.
3.4. Microstructural Parameters and their Analysis
3.4.1. XRD Analysis
The microstructure in TFRs is very complex and affected by a variety of parameters. The composition,
softening point, viscosity, thermal expansion coefficient and wetting properties of the glass, the ratio
of the size of glass particles to that of metal oxide grains and the sintering properties of the conductor
material are some parameters known to affect the final microstructure of TFRs, besides the process
condition (temperature and time in a defined firing cycle).
Fig. 3 shows the XRD profiles of the ZnO TFRs fired at 500, 600 and 700 oC prepared from
synthesized ZnO powder. It is revealed that all the samples have diffraction peaks corresponding to
(100), (002), (101), (102), (110), (103), (200), (112), (201) and (202) directions of the hexagonal
wurtzite ZnO crystal structure [JCPDS 36- 145)] similar to Joseph et al [55]. Some peaks of alumina
substrate (indicated with X) was also found in the XRD profile of the TFRs.
It has been observed that (101) reflections are of maximum intensity, which indicates that ZnO TFRs
have preferred orientation in the (101) plane, the intensity of which increasing with increase in firing
temperature of the TFR. The higher peak intensities of an XRD pattern is due to the better crystallinity
and bigger grain size. Crystalline nature increases as firing temperature increased to 700 °C. This is
clear from the increase in peak intensity. Also in the present case the increase in Zn content and firing
temperature is the reason for high preferential orientation along the (101) plane. A high degree of
crystal orientation reduces the probability of the scattering of the carriers at the grain boundary.
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Fig. 3. XRD profiles of ZnO thick film resistors fired at (a) 500, (b) 600 and (c) 700 oC.
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The XRD pattern was used to calculate the grain (crystallite) size of ZnO. The average grain size of
ZnO TFRs fired at 500, 600, and 700 ºC was observed as 46.48, 51.47 and 53.39 nm respectively. It
has been observed that the grain size increases with increase in firing temperature. The bigger grain
size can be attributed to the agglomeration of particles due to increase in firing temperature [56-58].
While the XRD patterns of all the films seem to be qualitatively similar. Upon increasing the firing
temperature from 500 to 700 oC, the 2θ increases and the FWHM value decreases, indicating that the
(101) spacing decreases and the grain size of the ZnO TFR is improved with increasing firing
temperature as indicated in Fig. 4 (a). These results are thought to be related to the reduction in oxygen
atoms due to higher firing temperature. This change influences the stoichiometry of ZnO TFRs. The
interplanar spacing of (101) plane is therefore 2.4797, 2.479 and 2.4777 Å for the TFRs fired at 500,
600, and 700 ºC respectively [Fig. 4 (b)], which is in good agreement with the standard value 2.4759 Å
shown by JCPDS card 36-1451 file data. Table 2 shows the values of 2θ, interplanar (d) spacing and
FWHM values of the ZnO TFRs deposited at different firing temperatures for (101) plane.
Fig. 4. Plot of FWHM, Grain size and interplanar spacing versus firing temperature.
Table 2. 2θ, interplanar spacing and crystallite size of the ZnO TFRs deposited at different
firing temperatures for (101) plane.
Firing
Temperature
500 oC
600 oC
700 oC
JCPDS 36-1451
(hkl) plane
101
2θ
36.23
36.24
36.25
36.26
Interplanar spacing, d
(Å)
2.4797
2.4790
2.4777
2.4759
FWHM
β
0.3141
0.2837
0.2735
--
3.4.1.1. Lattice Constants
From the XRD pattern, the lattice constants of synthesized ZnO TFR material can be calculated using
the equation:
1/d2(101) = 4/3(1/a2) + 1/c2,
(2)
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where d is the interplanar spacing, a and c are the lattice constants (being hexagonal structure, a=b,
c/a =√8/3).
The calculated values of lattice constants are illustrated in Table 3. Lattice constants of ZnO are
slightly depending on its stoichiometry. From Table 3, it has been observed that there is variation of
lattice constants from JCPDS value (a = 0.3249 nm, c = 0.5205 nm). From XRD pattern it has been
observed that there is shifting of diffraction peaks with firing temperature. It may be attributed due to
slight variation of Zn and/or O stoichiometry in ZnO. This assumption is further confirmed by the
results of EDAX analysis. As a semiconductor, in ZnO the radius of O2-(0.132 nm) is larger than that
of Zn2+ (0.074 nm). So the variation of O content in ZnO will lead to the variation of its lattice
constants, which cause the shifting of the diffraction peaks [59, 60].
3.4.1.2. Texture Coefficient (Tc)
Texture coefficient (Tc) is used to quantify the preferential orientation of the films fired at different
temperatures. The effect of the firing temperature on the orientation of the films was investigated by
calculating the texture coefficient using the following equation [61, 62, 63]:
TC(hkl) 
I (hkl) /I O(hkl)
1/N[  N I (hkl) /I O(hkl) ]
(3)
where h(hkl) is the texture coefficient of the (hkl) plane, I(hkl) is the measured intensity, Io(hkl) is the
JCPDS standard intensity and N is the number of diffraction peaks. It was observed that Tc is larger
than unity for a preferentially oriented (hkl) plane [62, 63]. The lower values of Tc reveals that the
films have poor crystallinity and this may be improved at a higher firing temperature. Fig. 5 shows the
variation of the texture coefficient with firing temperatures for the (100), (002) and (101) planes. From
figure it has been observed that the preferred orientation is the (101) plane for all firing temperatures.
The increase in preferred orientation is attributed to an increased number of grains along the plane. The
texture coefficient increases with increase in firing temperature for all planes.
Fig. 5. Variation of the texture coefficient with firing temperatures.
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3.4.1.3. Microstrain and Dislocation Density
XRD lines are usually broadened in their shape. These effects can be classified into instrument and
specimen broadening. Instrument broadening originates from the non-ideal optical effects of the
diffractometer and from the wavelength distribution of the radiation. In the present work instrumental
broadening is corrected by using a standard defect free silicon sample.
Specimen broadening arises due to small crystallite (grain) size and strain (lattice distortion). Grain
size causes the radiation to be diffracted individually [64]. The prepared ZnO thick film resistor is
polycrystalline in nature, and hence large number of grains with various relative positions and
orientations cause variations in the phase difference between the wave scattered by one grain and the
others. The total intensity scattered by all grains is the sum of individual intensities scattered by each
grain. On the other hand, lattice strain broadening is caused by varying displacement of the atoms with
respect to their reference-lattice positions. A uniform compressive or tensile strain (macrostrain)
results in peak shift [65] of X-ray diffraction lines, whereas a non-uniform tensile and compressive
strain results in broadening of diffraction lines (microstrain). Thus grain size and microstrain effects
are interconnected in the line broadening of peaks, which makes it difficult to separate. Many
approaches exist for the evaluation and separation of size and strain parameters from the occurring line
broadening. Williamson-Hall technique [64] is adopted in the present work where grain size D and
micro strain ε is related as:
βC cos θ 1
 sin θ 
 ε

λ
D
 λ 
(4)
where βc is the instrumental effect corrected full width at half maximum of the peak measured in
radian, θ is the diffraction angle and λ is the wavelength of X-ray.
The slope of the plot of βc cosθ/λ and sinθ /λ gives the microstrain and the inverse of intercept on
y-axis gives grain size value. Fig. 6 shows the Williamson-Hall plot of ZnO TFRs fired at different
temperatures. It shows grain size increases from 46.48 nm to 53.39 nm as firing temperature increases
but microstrain value deceases from 2.237 to 1.363.
Fig. 7 shows the variation of RMS microstrain and dislocation density as function of firing
temperature. Dislocation density is defined as the length of dislocation lines per unit volume of the
crystal. A dislocation is an imperfection in a crystal associated with the misregistry of the lattice in one
part of the crystal with another part [66]. The dislocation density was calculated using the following
equation:
ρ= √12 (ε 2)1/2 /(dD),
(5)
where ε is the RMS micro strain, d is the interplanar spacing and D is the crystallite size.
It was observed that the microstrain and dislocation density decrease with an increase in the firing
temperature. This leads to a reduction in the concentration of lattice imperfections [58, 67]. A similar
trend has been reported by Mahalingam et al. [66] for electrodeposited ZnO thin films.
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Fig. 6. Williamson-Hall plot to determine grain size and microstrain
of ZnO TFRs fired at different temperatures.
Fig. 7. Variation of micro strain and dislocation density with firing temperature.
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3.4.1.4. Stacking Fault Probability
A stacking fault is a planar imperfection that arises from the stacking of one atomic plane out of
sequence with another while the lattice on either side of the fault is perfect. The presence of a stacking
fault gives rise to a shift in the peak positions of observed reflections with respect to the ideal JCPDS
positions of the sample [67]. From the XRD patterns of ZnO films, the peak shift Δ(2θ) for the
oriented (101) plane was observed with a change in firing temperature. The stacking fault probability
(α) was calculated using equation-6 [66] at different firing temperatures.

2 2  ( 2 )
45 3 tan 
(6)
The stacking fault probability decreases with an increase in the firing temperature. This leads to a
reduction in the concentration of lattice imperfections [58, 68] in the ZnO TFRs, indicating less
defect/dislocations in the film as firing temperature increases as shown in Fig. 8. A similar trend has
been reported by T. Mahalingam et al [66] for electrodeposited ZnO thin films.
Fig. 8. Stacking fault probability versus firing temperature.
3.4.2. SEM Analysis
SEM images of Fig. 9 show that all of the films are porous with small grains (sub micron). The films
fired at 500 and 600 oC are more porous as compared to film fired at 700 oC. The grain size is little bit
larger as the increase of firing temperature although the increase is not significant. All the SEM images
are recorded at 50 k magnification for comparison.
The microstructure consists of primarily irregularly shaped of 1 to 6 μm aggregates of fine particles
oriented randomly leading to a moderate porosity. The material is characterized by some intergranular
porosity (about 20 %). Some large open pores of several micrometers in diameter and small open pores
of several tens of nanometers in diameter are present in sample. Microstructure of TFR fired at 600 oC
shows that some necks are formed within the structure. Agglomeration of small crystallites also seems
to be present in the certain region. The contrast difference is due to different orientations of the
crystallites. At 500 oC firing temperatures the surface morphology of the ZnO film showed individual
grains clearly and the grain size enlarged above firing temperature of 500 oC.
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(a) SEM image of ZnO TFR fired at 500 oC
(b) SEM image of ZnO TFR fired at 600 oC
(c) SEM image of ZnO TFR fired at 700 oC
Fig. 9. SEM images of ZnO TFRs fired at different temperatures.
The microstructure of ZnO TFR fired at 700 oC shows the formation of submicrometer crystallites
distributed more or less uniformly over the surface with very small number of open pores and several
particles connected with each other and shows the strongly agglomerated structure with neck growth.
The TFR fired at 700 oC also reveal lower surface porosity, large particle size and smaller specific
surface to volume ratio than the films fired at 500 and 600 oC. As seen in micrographs, the grain size
increases with the firing temperature. The number of particles which has hexagonal structure increased
with increasing firing temperature. The grain size in ZnO thick film resistors increases with increasing
firing temperature.
Table 3 illustrates the variation of Microstructural parameters calculated at different firing
temperatures.
Table 3. Microstructural parameters of ZnO TFRs fired at different temperatures.
Firing
Temp.
(o C)
500
600
700
ASTM
Grain size,
(from
XRD)
D, (nm)
46.48
51.47
53.39
Grain size
(from
SEM)
D, (μm)
1.25
1.69
2.063
Lattice
Constants (101)
a (nm)
0.3241
0.3240
0.3238
0.3249
c (nm)
0.5293
0.5291
0.5288
0.5205
Texture
Coeff.
TC(101)
Micro
strain,
ε
Dislocation
density, ρ
(Lines/cm2)
2.3713
2.9790
3.0896
2.237
1.771
1.363
0.0670
0.0482
0.0476
Stacking
Fault
probability,
α
0.02322
0.01548
0.007736
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4. Conclusions
ZnO thick film resistors can be deposited by a screen printing method on alumina substrates. The
structural and morphological properties of the ZnO TFRs were influenced by firing temperature. XRD
pattern confirms polycrystalline wurtzite ZnO with a preferential orientation along (101) plane. Grain
size and microstrain is obtained using Williamson–Hall plot method. As firing temperature increases
grain size increases and microstrain decreases. It is observed that the texture coefficient in the ZnO
thick film resistors increases along the (101) direction with the firing temperature. SEM images
indicated that the TFR fired at 700 °C has the highest crystallite size.
Acknowledgements
The authors are thankful to Management authorities of M. G. Vidyamandir, M. S. G. College
Malegaon (Nasik), for providing all the required facilities for doing the work and also like to thank the
authorities of Physics Department, Pune University and NCL Pune for cooperation in the field of XRD
and Scanning electron microscopic analysis respectively. The authors also gratefully acknowledge the
financial support from the University Grant Commission, New Delhi.
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__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Design and Analysis of Bulk Micromachined Piezoresistive
MEMS Accelerometer for Concrete SHM Applications
1
S. Kavitha, *2 R. Joseph Daniel, 3 K. Sumangala
1, 2
National MEMS Design Centre (NPMaSS),
Department of Electronics and Instrumentation Engineering,
Annamalai University, Annamalai Nagar- 608 002, Tamil Nadu, India.
*
Tel.: 09445112208, fax 04144-239732
3
Department of Civil and Structural Engineering,
Annamalai University, Annamalai Nagar- 608 002, Tamil Nadu, India
E-mail: kaviraj_2003@rediffmail.com, josuma.au@gmail.com, josuma@rediffmail.com
Received: 3 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Structural Health Monitoring (SHM) using non destructive testing generally involves
measurement of shift in natural frequency of the monitored structure. Vibration sensors play a crucial
role in such SHM systems and the present day SHM systems use commercially available off the shelf
MEMS accelerometers. In this work, an attempt has been made to design a MEMS accelerometer that
is specifically intended for concrete SHM applications. This paper presents the design methodology
of a MEMS silicon piezoresistive single axis accelerometer with the seismic mass (m) suspended by
four symmetrical cantilever beams. The simulation and analysis results using CoventorWare MEMS
design tool show that this newly designed accelerometer is capable of measuring vibrations up to
2 g. The modal analysis results indicate that the accelerometers considered for this analysis (Device-A,
Device-B, Device-C) using CoventorWare simulation tool has its first mode natural frequency of
1040 Hz and 946 Hz respectively against the specified 900 Hz. The piezoresistive sensitivity of
Device-A (with larger mass and optimum stiffness) is found to be the maximum thus demonstrates that
the beam length and half side length of the mass should lie in the region (L<a).
Copyright © 2012 IFSA.
Keywords: Structural health monitoring, MEMS, Piezoresistive, Single axis Accelerometer.
1. Introduction
Structural Health Monitoring (SHM) involves determination of structural health status of the concrete
structures and potentially predicts the damage of the structure. Conventionally wired sensors are
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
installed to manually acquire the vibration data and natural frequency is obtained from the vibration
data using FFT analysis. The shift in the natural frequency indicates damage and the magnitude of the
frequency shift can be used to quantify the damage levels [1]. This kind of health monitoring is
restricted by either the cost of permanently installed sensors or of manual collection of structural data
using portable equipment. In the recent past, wireless sensors have been considered as a potential
alternative to the wired sensors since it offers a more cost effective approach for capturing the
vibration data from the structure.
Even among the wireless sensors being developed, the MEMS vibration sensors are beginning to play
crucial role since the sensing and data transmission can be integrated as a single chip. Jerome Peter et
al [2] has conducted extensive research on structural health monitoring integrating off-the-shelves
accelerometers for sensing the vibration and wireless communication equipment for transmission of
acquired vibration data to explore the benefits of wireless structural monitoring systems. To date, the
standard practice in the SHM community has been to adapt commercial off-the-shelf (COTS) sensing
technologies to the particular proof-of concept experiment at hand. In the recent past, COTS MEMS
accelerometers have been used for SHM 2. Micro electro-mechanical systems (MEMS) sensor is
fabricated through micro-fabrication techniques. In MEMS sensors, electro-mechanical transduction
mechanisms can be combined with micro-circuitry thereby forming a sensor. The sensor is now a
miniaturized version of the traditional transduction element along with substantial circuitry for signal
processing and computation [3]. Andreas Vogl et al [4], reported the design and implementation of a
novel wireless MEMS Piezoresistive accelerometer sensor with a sensitivity of 0.19 mV/g/V for
condition monitoring of AC motors. However, little attention has been paid to the development and
implementation of MEMS sensors with the intent of specifically addressing issues related to concrete
SHM.
The fundamental building blocks of structural monitoring systems are the sensing transducers. The
quality and completeness of the data set collected for a given structure largely depends upon the
capabilities and quality of the transducers used to record structural responses. Especially, the MEMS
sensors used for concrete structure health monitoring should be of high sensitivity with ultra noise
floor since most ambient vibrations in civil structures are characterized by low-amplitude
accelerations. Secondly, the natural frequencies of civil structures are relatively small and hence the
MEMS accelerometers designed for Civil SHM need not have larger band width. Ultimately, such
sensors should be of low cost and consume low power. The authors of this present paper have made an
attempt to design a Piezoresistive MEMS accelerometer that satisfies the requirements of an
accelerometer meant for concrete SHM applications. The results of such a design and the modal
analysis on the designed accelerometer obtained through CoventorWare simulation tool are presented
in this paper.
1. 2. Proposed MEMS Accelerometer (Vibration Sensor)
The cross sectional view of the MEMS accelerometer considered in this study for concrete SHM
applications is shown in Fig. 1 and the top view of the MEMS accelerometer and view of the structure
created for analysis by CoventerWare are presented in Figs. 2 (a) and 2 (b) respectively.
The seismic mass (m) is suspended by four symmetrical beams that determine the stiffness constant
‘k’. This structure with the seismic mass (m) suspended by four symmetrical cantilever beams has
been preferred in this study to reduce the cross-axis sensitivity. The other advantage is that device can
be realized using bulk micromachining and hence it paves way for using a large mass which is
typically required for achieving higher sensitivity at low frequency vibrations. Four silicon
piezoresistors strategically embedded on these four beams gives the vibration in terms of change in
their resistances. The strategic locations at which these resistors are placed will be discussed in a later
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
section. The four piezoresistors were organized in a Wheatstone bridge to sense single axis
(z-direction) vibration. Considering the fact that this accelerometer is intended for concrete SHM
applications, the device parameters are specified as given in the Table 1.
Fig. 1. Cross sectional view of the MEMS accelerometer.
R2
R1
R3
a
L
R4
TL
Fig. 2 (a). Top view of the MEMS accelerometer.
Fig. 2(b). MEMS accelerometer structure created in CoventorWare simulation software.
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Table 1. Design specifications of the MEMS vibration sensor.
Parameter
Acceleration Range
Resonance(Bandwidth)
Sensitivity
Cross axis sensitivity
Specified value
0-2 g
900 Hz
1 mV/g/V
<5 %
3. Analytical Model for Natural Frequency
The natural frequency of this accelerometer can be estimated from the well known equation
o  k / m
(1)
where k is the effective stiffness of the beams and m is the mass of the effective seismic mass and the
stiffness constant (k) of the present beam structure is obtained as
k
48 EI
L3 ,
(2)
where E is the young’s modulus of the beam material and the moment of inertia, I is thus
I
1 3
bt
12
,
(3)
where b and t are the breadth and thickness of a beam respectively. The existing analytical model for
natural frequency of such an accelerometer [4, 5] has been given as
fo 
1
2
4 Ebt 3
mL3
(4)
4. Structural Design of the MEMS Piezoresistive Accelerometer Sensor
The main design requirement of an accelerometer with an intended application of concrete SHM is
high sensitivity for low frequency vibrations. High sensitivity can be achieved with large mass and
lower stiffness. But, large mass and lower stiffness will result in lower resonance frequency and hence
lower bandwidth. However, this is a favourable situation while considering an accelerometer for SHM
applications since this application typically needs lower bandwidth. The other major advantages of this
structure is that it needs no sacrificial etching in realizing the suspended proof mass and therefore the
conventional stiction problems faced in the surface micromachined structures are eliminated. Further
this structure helps us to realize larger mass unlike surface micromaching where large mass realization
may be difficult with thin film.
A brief survey [6, 7] of the literature indicates that the maximum frequency of the excitation signals
used for SHM applications is 100 Hz. The natural frequency of the accelerometer in the present study
has been fixed at 900 Hz, considering safe design for low noise floor. The next step in this design is to
arriving at the required mass and stiffness to achieve this frequency.
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4.1. Proof Mass and Beam Length Design
The proof mass design is limited by the maximum die size of the accelerometer within which it is
required to realize this structure. The die size is fixed to be 6 mm×6 mm. It is understood from the top
view of the accelerometer as shown in Fig. 2(a). That sum of the beam length (L) and half side length
of the proof mass (a) is fixed and it is denoted as “TL”. In the present case this TL= 2250 μm. If L is
increased, a is reduced and the right design should determine the value of L and a so as to get
maximum sensitivity which is achieved by placing the resistors in the maximum stress region. It is
known that these dimensions are related to the natural frequency since the half side length of the mass
(a) and beam length (L) decide the natural frequency for a given beam thickness and width. The side
length of the mass decides the mass (m) and the beam length decides the stiffness (k). Hence the
natural frequency is plotted against the beam length. The values of L and a for the chosen resonant
frequency (900 Hz in this case) are obtained from the natural frequency versus beam length plot as
shown in Fig. 3.
Proof Mass half Side length (a) in m
1800
1800
1600
1200
1000
800
600
400
200
Theoritical frequency
1600
Natural Frequency (f0) in Hz
1400
1400
1200
1000
Device-A
Device-B
(L= 2050 m,a = 200 m)
(L= 500 m,a = 1750 m)
L<a
800
L>a
L=a approximately
600
400
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 3. Natural frequency versus beam length.
It is evident from Fig. 3 that the natural frequency of 900 Hz can be obtained for two different
dimensions of the proposed structure viz (L= 500 μm, a = 1750 μm) and (L= 2050 μm, a = 200 μm).
Hence both structures are considered for detailed analysis to select the best. Based on this analysis, the
dimensions of the piezoresistive accelerometers designated as Device-A (L= 500 μm, a = 1750 μm)
and Device-B (L= 2050 μm, a = 200 μm) of our design are specified as given in Table 2.
Though natural frequency is one of the important design parameter, it is equally important to achieve
maximum sensitivity with the piezoresistor for the designed sensor. Hence the determination of L and
a should not only decide the bandwidth or natural frequency but also should result in maximum
sensitivity. In order to find the dimensions of the L and a that provide maximum sensitivity, the
displacement in the Z-axis is calculated and plotted using the equation (5).
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Table 2. Geometries of the sensor.
Structural parameters
Proofmass half side length (a)
Proofmass thickness (h)
Beam length (L)
Beam width (b)
Beam thickness (t)
Die size
Dimension in μm
Device-A
1750
860
500
45
15
6000 × 6000

Dimension in μm
Device-B
200
860
2050
45
15
6000 × 6000
Dimension in μm
Device-C
900
860
1350
45
15
6000 × 6000
nmgL3
48 EI ,
(5)
where n is the acceleration in g (g = 9.8 m/s2) , m is the mass of the proofmass, E is the Young’s
modulus of silicon and I is the moment of inertia.
The calculated deflections are plotted against the beam length and half side length of mass. The
deflection obtained using CoventorWare simulation for various L and a is also plotted as shown in
Fig. 4 and the values match satisfactory with the theoretical deflection values. The deflection seems to
be increasing in the region (L<a) where large stiffness and mass (m) together decide the deflection and
reaches the maximum at L ≈ a (optimum stiffness and mass) and then it decreases as L>a where
saturating small stiffness and smaller mass (m) as shown in Fig. 5(a) and Fig. 5(b). A larger ‘a’ (proof
mass half side length) indicates larger mass and large L (beam length) indicates lower stiffness. It is
also seen that the maximum deflection is obtained for the case a = 900 µm and L = 1350 µm. Hence it
is decided to estimate the sensitivity of this device designated as Device-C also apart from Device-A
and Device-B for further analysis and the dimensions for all these three devices are given in Table.2.
Proof Mass half Side length (a) in m
3.5
3.0
Deflection  , in m
2.5
1800
1600
1400
1200
1000
800
600
400
200
L=a approximately
CoventorWare
Deflection
Theoritical
Deflection
2.0
L>a
L<a
1.5
1.0
0.5
0.0
200
Acceleration (a) =2g
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 4. Beam length versus deflection.
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Proof Mass half Side length (a) in m
1800
1600
1400
700
1200
1000
800
600
400
200
Stiffness
Stiffness (k) in N/m
600
500
Device-A
400
300
200
100
Device-C
Device-B
0
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 5(a). Beam length versus stiffness.
Proof Mass half Side length (a) in m
-5
1800
1600
1400
1200
1000
800
600
400
200
3.0x10
mass
-5
Device-A
2.5x10
-5
Mass (m) in Kg
2.0x10
-5
1.5x10
-5
1.0x10
Device-C
-6
5.0x10
Device-B
0.0
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 5(b). Beam length versus mass.
4.2. Piezoresistor Design
The frame is fixed to the system whose acceleration is to be measured. As the system accelerates, the
frame moves with it. The proof mass, due to its inertia tries to remain in its earlier position and in the
process gets deflected up and down, depending on the direction of the motion of the system. As a
result, stress will be developed at the frame and proof mass ends of each flexure. This stress developed
is directly proportional to the vibration or acceleration and exact measurement of this stress will lead to
successful measurement of acceleration [8]. Boron doped p-type Silicon piezoresistors are used in this
accelerometer to sense the stress and convert this stress into change in resistance. Four piezoresistors
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
are implanted at maximum stress points on each beam as shown in Fig. 2. The resistors R1 and R3
experience tensile stress and R2 and R4 experience compressive stress when subjected to a positive
acceleration (+g). This leads to an increased resistance in the piezoresistors experiencing tensile stress
and decreased resistance in the piezoresistors experiencing compressive stress. This condition is
reversed in the case of negative acceleration (-g). Surface resistors were selected for achieving high
sensitivity [9, 10]. Here the sensitivity of the sensor was maximized by large mass with almost the full
wafer thickness, thin and narrow beams as explained in the earlier sections. The zero g resistance is
chosen as 1000 Ω. The dimensions of the piezoresistors and other material properties are given in
Table 3.
Table 3. Piezoresistor specifications.
Physical parameters of Piezoresistor
Piezoresistor length (l)
Piezoresistor width (w)
Piezoresistor thickness(h)
Resistivity (ρ)
Young’s modulus
Poisson’s ratio
Dimensions
50 μm
10 μm
0.6 μm
0.012 Ω-cm
160 GPa
0.3
The electrical sensitivity S for the Wheatstone bridge of boron doped silicon piezoresistors can then be
roughly calculated by introducing the effective piezo-resistive coefficient  eff . For the piezoresistors
described here, the resistance is given by R  l A , where l is the length and A is the cross-sectional
area of the silicon resistor. Assuming that the dimensional changes can be neglected and that the stress
is applied in the longitudinal direction, the change in resistance, ∆R, is given by
ΔR
  l σ l for R1 and R3
R0
or
ΔR
  t σ t for R2 and R4,
R0
(6)
where R0 is the initial resistance,  l and  t are the longitudinal and transverse piezoresistive
coefficients respectively,  l and  t are the longitudinal and transverse stresses respectively. The
piezoresistive coefficients are dependent on the dopant concentration, crystal orientation and
temperature. At room temperature, the measured piezoresistive coefficients for p-type single-crystal
silicon are  11 = 6.6 × 10-11 Pa-1,  12 = -1.1 × 10-11 Pa-1 and  44 = 138.1 × 10-11 Pa-1. These values have
been used in the CoventorWare simulation and the stress levels experienced by the beam at +2g are
plotted against the distance from midpoint of proof mass as shown in Figs. 6 (a) and 6 (b). It is seen
from the Fig.6 (a) that R1 would undergo maximum tensile stress when located at the proof mass end
as indicated in the figure (between -1750 μm to -1800 μm in X-axis) and R3 should undergo maximum
tensile stress when located between 1750 μm to 1800 μm in X-axis. Similarly, in order to make the
resistors R2 and R4 experience compressive stress they are located between 2200 μm to 2250 μm and
between -2200 μm to -2250 μm respectively in Y axis as shown in Fig. 6 (b).
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
Fig. 6(a). Stress (Sxx) at +2g in MPa.
Fig. 6(b). Stress (Syy) at +2g in MPa.
5. Simulation Results and Discussions
These accelerometer structures were analyzed using modules of CoventorWare. Memmech solver has
been used for modal, displacement and stress analysis.
5.1. Modal Analysis
Resonance frequency is a function of mass and spring constant and it can be found by performing
modal analysis. The modal frequencies of both the devices measured by the FEA analysis
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
corresponding to mode1, mode 2 and mode 3 respectively are given in Table 4 and Mode 1 frequency
values obtained using equation (4) and CoventorWare simulation are plotted against beam length as
shown in Fig. 7.
Table 4. Modal frequencies.
Mode
Device-A
1041.1
1315.9
1315.9
Mode 1
Mode 2
Mode 3
Frequency (Hz)
Device-B Device-C
946.77
401.39
2035.9
668.91
2035.9
668.91
Proof Mass half Side length (a) in m
1800
1600
1400
1200
1000
800
600
400
200
Natural Frequency (f0) in Hz
1800
Theoritical frequency
CoventorWare frequency
1600
1400
1200
Device-A
1000
Device-B
800
600
Device-C
400
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 7. Beam length versus natural frequency.
The modal frequency (Mode 1) of Device-A and Device-B as estimated by CoventorWare are shown
in Figs. 8(a) and 8(b) respectively. Similarly the natural frequency has also been obtained for DeviceC.
5.2. Displacement Analysis
Figs. 9(a) and 9(b) show the displacement using CoventorWare simulation of the two Devices
(Device-A and Device- B) for acceleration applied up to 2g. From Figs. 9(a) and 9(b), it is observed
that the main axis (Z-axis) deflections are higher compared with the other X and Y axes deflections,
indicating that the cross axis sensitivity is reduced in this structure 11. Similarly the displacement has
been obtained for Device-C also.
From Table 5, it is observed that the maximum displacement is obtained for Device-C thus confirming
our earlier observation as shown in Fig. 4.
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(a)
(b)
Fig. 8. Simulation results of modal frequency of Devices: (a) –A, and (b) –B using CoventorWare.
(a)
(b)
Fig. 9. Acceleration versus displacement up to 2 g.
Table 5. Displacement results for all the devices.
Device
Device-A
Device-B
Device-C
Displacement
at 2g
0.46 μm
0.56 μm
3.1 μm
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5.3. Piezoresistive Analysis
The change in resistance occurs on each piezoresistor due to applied acceleration. When acceleration is
applied, the resistance value of each piezoresistor changes due to piezoresistive effect which in turn
changes the Wheatstone bridge output voltage. The bridge output voltage (Vout) for 2g acceleration
applied in the X, Y and Z-directions for three Devices obtained from piezoresistive analysis are
summarized in Table 6.
Table 6. Comparison of Z-direction (main axis) sensitivity for all the three devices at 2g.
Sensitivity
mV/g/V
0.5
0.02725
0.34
Device
Device-A
Device-B
Device-C
It is also learnt from Table 6 that the voltage sensitivity of Device-B is very less compared with the
voltage sensitivity of Device-A and Device-C thus predicting that larger deflection does not yield
maximum voltage sensitivity. The cross axis sensitivity has been obtained for Device-A and Device-B.
MEMPZR analysis has been used to verify the cross axis sensitivity and the results are listed in
Table 7.
Table 7. Comparison of Cross-axis Sensitivity for Both the Devices for 2g.
Device
Device-A
Device-B
Sensitivity
for
X-axis acceleration,
mV/g/V
0.0035
0.000625
Sensitivity
for
Y-axis acceleration,
mV/g/V
0.0029
0.003
Sensitivity
for
Z-axis acceleration,
mV/g/V
0.5
0.02725
It is evident from these results summarized in Table 7 that the main axis sensitivity is high compared
with the other axes sensitivity thus demonstrating the ability of this structure to reduce the cross axis
sensitivity. The x and y axes sensitivities are 0.7 % and 0.6 % of main axis sensitivity respectively.
5.4. Stress Analysis
Since the maximum voltage sensitivity is not achieved for the devices which have maximum deflection
(Device-C) it is necessary to probe the stress values in order to calculate the stress being experienced
by various devices being the change in piezoresistance is controlled by stress. The stress is related to
the change in resistance (∆R)
R   eff . max
,
(7)
where  max is the maximum stress and ∆R is the change in resistance. Therefore, the authors calculated
the stress for various L and a as done for displacement. It is important to note that the stress is not
obtained for the Devices with maximum deflection. This is due to the reason that the stress developed
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
in the beams with very small L and large mass (L<<a) is low and more stress is developed in the beams
with a larger mass and moderate stiffness (L<a) (Device-A) rather than lower mass (L>a) with lower
stiffness (Device-B and Device-C). This results coincide with the findings of Andreas et al. [4, 5]. The
stress values obtained by FEA simulation also confirm this and the response is shown in Fig. 10.
Proof Mass half Side length (a) in m
22
20
1800
1600
1400
1200
1000
Region-1
Stress , in MPa (Sxx/ Syy )
600
400
200
CoventorWare Stress Sxx
CoventorWare Stress Syy
Analytical Stress
18
16
800
Region-2
14
Device-C
12
Region-3
10
Device-B
Device-A
8
6
4
2
0
400
600
800
1000
1200
1400
1600
1800
2000
2200
Beam Length (L) in m
Fig. 10.Comparison of natural frequency and stress for both theoretical and practical.
It is learnt from the stress analysis that there are three regions of operation namely Region 1, 2 and 3 as
shown in Fig.10. In the Region-1, the stress levels are low due to high stiffness and large mass (L<<a).
Since the stress levels generated in this region are low, the voltage sensitivity will also be less. In the
Region-2, the ‘L’ is moderately low compared with ‘a’ (L<a) and therefore large stress levels are seen
and hence this region is the most preferred region for achieving larger voltage sensitivity. In the
Region-3, the L is moderately higher with smaller ‘a’ (L>a) or L is very high with small a (L>>a).
Therefore, the stiffness is smaller than the mass and hence the stress developed in this region is
considerably low. So, selection of ‘L’ and ‘a’ that is falling in this region should be avoided or the
resonant frequency of the accelerometer should not fall in this region (L>a) should be avoided.
Hence it is understood that the beam length should be chosen in such a way that L < a. This region is
marked by a rectangular box in Fig. 10. It is very important to see that the required f0 lies in this region.
In the other regions where L  a or L > a the stress developed is considerably less. This is the reason
for larger voltage sensitivity achieved with Device-A compared with Device-B and Device-C.
6. Conclusion
The design and analysis of piezoresistive MEMS accelerometer for concrete SHM applications has
been presented in this paper. The analytical model for natural frequency of the MEMS piezoresistive
accelerometer whose mass is suspended by four symmetrical cantilever beams was used to design the
dimensions of cantilever beam and seismic mass. Silicon piezoresistors embedded in the beams have
been designed and placed strategically to achieve maximum sensitivity. Two devices of different
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 62-75
dimensions obtained for same resonant frequency and one device with La (Device-C) were simulated
and analyzed. Comparison of the voltage sensitivity obtained thro piezoresistive analysis in mV/g/V
indicates that the structure with large mass and optimum stiffness (L<a) is better compared with the
one that has lower mass and lower stiffness (L>>a). The Device – A with higher mass and optimum
stiffness (L<a) gives 0.51 mV/g/V sensitivity in the Z-axis is the highest sensitivity device and it is
0.7 % than the other axes sensitivities. Thus, it is concluded that this structure is ideally suited for
single axis accelerometer. The natural frequency of this Device-A is measured to be 1040 Hz and
Device-B is measured to be 946 Hz against the design value of 900 Hz.
Acknowledgements
The authors acknowledge the support received from National Program on Micro and Smart Systems
(NPMaSS) and financial support from University Grant Commission (UGC), New Delhi, India
through MRP (Major Research Project) scheme.
References
[1]. Antony Jeyasehar, C., and Sumangala, K., Nondestructive Evaluation of Prestressed Concrete Beams Using
an Artificial Neural Network (ANN) Approach, International Journal of Structural Health Monitoring, 5,
4, 2006, pp. 313-323.
[2]. Jerome, P., Lynch, Aaron Partridge, Kincho, H. Law, Thomas, W. Kenny, Anne, S. Kiremidjian and Ed
Carryer, Design of Piezoresistive MEMS-Based Accelerometer For Integration With Wireless Sensing Unit
For Structural Monitoring, Journal of Aerospace Engineering, 16, 3, 2003, pp. 108-114.
[3]. Stephen, D. Senturia, Microsystem Design, Kluwer Academic Publishers, New York, USA, 2001.
[4]. Andreas Vogl, Dag T. Wanga, Preben Storasa, Thor Bakkea, Maaike, M. V. Takloa, Allan Thomsonb,
Lennart Balgardc, Design, Process and Characterisation of A High-Performance Vibration Sensor for
Wireless Condition Monitoring, Sensors and Actuators A, 153, 2, 2009, pp. 155–161.
[5]. Bao, M. H, Micro Mechanical Transducers, Pressure sensors, Accelerometers and Gyroscopes, -Handbook
of Sensors and Actuators, Vol. 8, Elsevier, Amsterdam, 2000.
[6]. Lynch, J. P., Law, K. H., Kiremidjian, A. S., Kenny, T. W, Carryer, E., and Partridge A., The Design of A
Wireless Sensing Unit For Structural Health Monitoring, in Proceedings of the 3rd International Workshop
on Structural Health Monitoring, Stanford, CA, USA, 2001.
[7]. Jerome Peter Lynch, Issues in Wireless Structural Damage Monitoring Technologies, in Proceedings of the
3rd World Conference on Structural Control (WCSC), Como, Italy, 2002.
[8]. Jesper Eklund, E. and Andrei, Single-mask Fabrication of High-g Piezoresistor Accelerometers with
Extended Temperature Range, Journal of Micromachines and Microengineering, 17, 4, 2007, pp. 730-736.
[9]. Aaron Partridge, J., Kurth Reynolds, Benjamin, W., Chui, Eugene, M., Chow, Alissa, M., Fitzgerald, Lian
Zhang, Nadim, I., Malufand Thomas, W., Kenny, A High-Performance Planar Piezoresistive
Accelerometer, Journal of Micro Electromechanical Systems, 9, 1, 2000.
[10].Adam Kovacs and Zsolt Vizvary, Structural Parameter Sensitivity Analysis of Cantilever and Bridge Type
Accelerometers, Sensors and Actuators A, 89, 3, 2001, pp. 197–205.
[11].Ravi Sankar, A., Das, S., and Lahiri, S. K., Cross-axis Sensitivity Reduction of a Silicon MEMS
Piezoresistive Accelerometer, Microsystem Technologies, 15, 4, 2009, pp. 511–518.
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
75
Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 76-91
Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Lumped Parameter Modeling of Absolute and Differential
Micro Pressure Sensors
1*
S. Meenatchisundaram, 2Ashwin Simha, 3 Mukund Kumar Menon,
4
S. M. Kulkarni and 5 Somashekara Bhat
1, 3
Department of Instrumentation and Control Engineering,
Department of Electronics and Communication Engineering,
Manipal Institute of Technology, Manipal, Udupi, Karnataka
2, 4
Department of Mechanical Engineering,
National Institute of Technology Karnataka, Surathkal, Karnataka
e-mail: meenasundar@gmail.com, ashwinsimha@gmail.com, smkulk@gmail.com
5
Received: 26 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Mechanical systems may be modeled as systems of lumped masses (rigid bodies) or as
distributed mass (continuous) systems. The latter are modeled by partial differential equations,
whereas the former are represented by ordinary differential equations [1]. In this paper a lumped
parameter model of absolute and differential pressure sensors are developed, whose diaphragm is
designed to undergo very small deflections (typically less than 25 % of the thickness). A simple
approximate model with proper assumptions are considered and analyzed first. A more appropriate
model with refined approximation is considered later. Estimation of various parameters like mass,
spring constant and damping of the diaphragm & fluid are done and used to estimate the transfer
function. The transfer function is then used to understand the frequency and stability analysis of the
system. A square, rigidly fixed diaphragm pressure sensor is considered in this work. By limiting the
maximum deflection to one-fourth of the thickness, the analysis has been done for a maximum applied
pressure of 100 MPa. MATLAB® is used as a tool to carry out the analysis. Copyright © 2012 IFSA.
Keywords: Lumped parameter model, Absolute pressure sensors, Differential pressure sensors, Micro
electro mechanical systems (MEMS), Modeling.
1. Introduction
Pressure sensing is one of the most established and well-developed areas of sensor technology. One
reason for its popularity is that it can be used to measure indirectly various real-world phenomena like
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 76-91
flow, fluid level and acoustic intensities, in addition to pressure [2]. Pressure sensors invariably use a
thin elastic member such as a diaphragm which acts as the primary transducer. Application of pressure
on the diaphragm results in the change of one or more physical attributes of the diaphragm like
displacement, stress, strain, etc. However these quantities have a very small magnitude and cannot be
read out directly. In view of this difficulty various transduction techniques are adopted such as
piezoresistive, piezoelectric, capacitive, optical, resonance etc.
Most pressure sensors today use sealed gas or vacuum filled cavities. The basic operation of such a
sensor is to couple the pressure to be measured to one surface of a membrane and to measure its
deflection. The Fig. 1 shows the different type of pressure sensor designs commonly implemented in
micromachined form. Pressure sensors can be built to measure pressure relative to a sealed reference
cavity or differentially using two input ports. For sealed cavity designs a vacuum is preferred since
there will be no temperature dependent pressure changes in the reference pressure [3].
Fig. 1. Commonly used Pressure sensors.
2. Mechanical Lumped Model
A 100 mm diameter wafer with a thickness of 500 µm is considered in this work. The sensor geometry
and dimensions are taken as listed in the Table 1 and the side view of a bulk micromachined pressure
sensor is shown in Fig. 2. The thickness is considered as 495 µm for practical reasons, where there will
be a reduction in thickness due to cleaning and smoothening of the surface.
Table 1. Geometry and Dimensions of Silicon Pressure Sensor.
Diaphragm geometry and wafer thickness
Side of the diaphragm (a)
Thickness of the diaphragm (h)
Max. central deflection of the diaphragm (wmax)
Young's modulus (E)
Poisson's ratio (γ)
Yield strength of silicon(100) (Sy )
Input pressure range (P)
Density of silicon (ρ)
Flat square silicon(100) and 500 mm
783 um [4]
63 um
15.75 um(limited to h/4 for linearity) [5]
131 GPa
0.27
7 GPa
0 – 100 MPa
2300 kg/m3
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Fig. 2. Dimensions of Silicon Die.
3. Lumped Model of an Absolute Pressure Sensor
A 3 Degree of Freedom with respect to the fluid, diaphragm and the air between diaphragm and casing
is considered in this model. The model and its parameters are described in the Fig. 3.
Fig. 3. Complete 3 DOF model of the pressure sensor.
Description of Symbols:
Mf = mass of the fluid in the chamber in kg;
Kf = stiffness contributed by the fluid in the chamber in N/m;
Bf = damping introduced due to fluid-structure interaction at the fluid-diaphragm boundary in Nm/s;
Md = Mass of the diaphragm in kg;
Kd = stiffness of the diaphragm in N/m;
Bd = damping introduced by diaphragm in Nm/s;
Ma = Mass of the air in the cavity in kg;
Ba = damping introduced due to interaction between the diaphragm and air in Nm/s;
Ka = stiffness constant of air in N/m.
3.1. Computation of Bf, Kd, Md and Mf Values
Using the values in Table 1 and the standard formulas, the following parameters are calculated and
given below.
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1. Stiffness of the diaphragm (Kd)
Kd 
Eh3
N
 4.26  106  
2
0.0138a
m
(1)
2. Squeeze film damping introduced due to fluid-structure interaction [6] at the fluid-diaphragm
boundary
Bf 
96 a 4
 4 hi3 ,
(2)
where Hi is the height of the inlet chamber. Referring to Fig. 2 Hi = 432 um. Thus
Bf = 2.06510-22 Ns/m
3. The mass of the fluid is given by
Mf = pfV,
(3)
where, pf is the density of the fluid admitted in kg/m3; Vf = a2Hi is the volume of the fluid in m3.
Assuming the fluid admitted is water with pf = 1000 kg/m3.
Mf = 2.6810-7 kg
4. The mass of the diaphragm is given by
Md = ρVd
Using the values in Table 1 yields
(4)
Md = 8.10510-8 kg
3.2. First Approximate Model
Several parameters in the model can be assessed only experimentally or via complex mathematics involving
more than one physical phenomenon at a time. To overcome this difficulty only those parameters which can be
readily estimated are considered as a first approximation along with the following assumptions:
 The effect due to air friction between the diaphragm and the casing is not considered;
 The material damping associated with the diaphragm Bd is ignored since silicon does not exhibit mechanical
hysteresis;
 Squeeze film damping contributes to the value of Bf and Bd;
 Liquids are incompressible. Hence Kf is very high and is not considered;
 Slope in the walls of the cavity due to anisotropic etching of silicon is not accounted.
After considering the above assumptions, the model is redrawn as given in Fig. 4.
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Fig. 4. First Approximate Lumped Model.
The governing equations for the above system in Fig. 4 are [1]:
(5)
(6)
Rearranging equations (5) and (6) and taking Laplace transformation yields
(7)
(8)
Representing equations 7 and 8 in matrix form and solving using Cramer's rule,
(9)
(10)
Using Final Value Theorem,
(11)
(12)
(13)
The fact that equation (13) was arrived at using final value theorem successfully verifies that the
governing equations are derived in proper sense. Using the values of the parameters obtained from (1)
to (4) and substituting into (10), the transfer function of the system is obtained as
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 76-91
(14)
3.3. Refined Model
It can be recognized that different fluids can enter the pressurizing chamber of the sensor. In view of
this, the parameters Bf, Kf and Mf associated with the fluid entry are eliminated in the refined model.
The parameters Ba and Bd are introduced just to check that it can be possible to establish any
comparison in the magnitudes between the various parameters of the model. The model shown in
Fig. 4 is redrawn to satisfy the condition shown in refined model is shown in Fig. 5.
Again using the concept of free body diagrams and Newton’s second law the following equations are
established for the nodal equilibrium of forces at the two nodes.
(15)
(16)
Fig. 5. Refined Model.
Taking Laplace transform and rearranging equations (15) and (16) in matrix form yields
 M d s 2  Bd s  K d

 ( K d  Bd s )
  X 1 ( s)   0 
( K d  Bd s)


M a s 2   Ba  Bd  s   K a  K d    X 2 ( s )   Fin ( s ) 
(17)
Solving by Cramer’s rule gives,
(18)
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For a step input of amplitude ‘F’, Fin(s) = F/s,
Using Final Value Theorem,
(19)
x1(t ) 
F  Ka  Kd 
Ka Kd

F
K a Series K d
(20)
4. Lumped Model of a Differential Pressure Sensor
A 3 Degree of Freedom with respect to the high pressure fluid, low pressure fluid, and diaphragm is
considered in this model. A simple back to back diaphragm type pressure sensor is considered in this
work. The parameters with the values listed in Table 1 are used. The model is shown in the Fig. 6.
Fig. 6. Complete 3 DOF model of a differential pressure sensor.
4.1. Approximate Lumped Model
The assumptions listed in section 3.2 are considered in this case also. If the concept of free body
diagrams and Newton’s second law is used then, we will get six set of equations and solving for
transfer function will be very difficult. To avoid complications, the equivalent circuit using forcevoltage analogy is drawn as shown in Fig. 7 and the governing equations are given below.
The governing equations for the above system are:
(21)
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 76-91
Fig. 7. (a) Approximate lumped model and (b) its equivalent circuit
(22)
Then,
(23)
Now, P  F and I  sX (Force-Voltage Analogy), then the required transfer function can be
obtained as:
(24)
or,
(25)
Using Final Value Theorem,
,
(26)
where, Fin(t)=F.u(t).Taking Laplace Transform, ∆F(s)=∆F/s, Then,
X 
F
2Kd
(27)
4.2. Computation of Bf, Kd, Md and Mf Values
1. Stiffness of the diaphragm (Kd)
kd 
Eh3
0.0140a 2 = 5.6153106 N/m
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2. Squeeze film damping introduced due to fluid-structure interaction at the fluid-diaphragm boundary
(Bf)
Bf 
96 a 4
 4 hi3 = 4.089410-6 Ns/m,
where, hi is the height of the inlet chamber. Referring to Fig 2, hi = 432um
3. The mass of the fluid is given by
Mf = 2.648510-7 kg
4. The mass of the diaphragm is given by
Md = 8.99910-8 kg
Using the values of the parameters obtained, the transfer function of the system is obtained as
(28)
5. Results and Discussion
5.1 Absolute Pressure Sensor
5.1.1. Frequency Analysis of First Approximate Model
The bode plot for the transfer function of the first approximate model absolute pressure sensor is given
in equation (14) is shown in Fig. 8. It can be concluded that the resonant frequency of the system is
(7.25106)/(2л) = 1.138 MHz. This is in agreement with the theoretical results given by
fr 
Kd
Md
2

4.26  106
8.1 108
2
= 1.153 MHz
(29)
The fact that the amplitude at resonance being high and phase changing by an angle -180 degrees very
rapidly indicate that the damping caused by the fluid Bf is negligible provided the inlet cavity of the
height hi is sufficiently high.
5.1.2. Root Locus Plot of First Approximate Model
Root locus analysis is done to assess the stability of the above system. Since the parameter of interest
is the correct estimation of Bf, which characterizes the flow of different fluids at different velocities,
the equation (10) can be rearranged as,
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Fig. 8. Magnitude – Phase plot of equation.
(30)
which represents the closed loop transfer function of a non-unity negative feedback control system.
The denominator of equation (30) is defined as the characteristic equation of the form
.
(31)
Rearranging equation (31) and let Bf = K, gives
M
1 K
d
 M f  s2  Kd
M f M d s3  M f K d s
M
G ( s) H ( s)  K
d
0
 M f  s2  Kd
M f M d s3  M f K d s
(32)
(33)
The rootlocus plot of the equation in (33) is shown in Fig. 9.
Referring to Fig. 9 the gain term K is nothing but Bf. It can be concluded that the system is stable since
the entire root locus plot is on the left half of the s-plane. Also with respect to equation (10) the order
of the system is 3. It is observed from the above plot that the value of Bf = 0.656 the only value
resulting in minimum overshoot of the response. But this value of Bf is nowhere closer to the value
predicted using squeeze film damping model. Also the value of Bf obtained from the root locus plot is
extremely high compared to the typical values found in microsystems which are of the order of 10-6
Ns/m. The third order system can be further decomposed into one first order and one second order
system. The evaluation of Bf requires the understanding of fluid structure interaction and
Computational Fluid Dynamics and hence will be refined further.
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Fig. 9. Rootlocus plot for equation (33).
5.1.3. Frequency and Stability Analysis of Refined Model
Fig. 10 to Fig. 13 shows the step response of the sensor for Pin = 100 MPa and the frequency domain
plot of the open loop transfer function derived in (18) as the refined model for different values of Bd.
Four different values of Bd are considered to show different cases namely Bd=100Ba, Bd=10Ba, Bd=Ba
and Bd=0.1Ba. By observing the phase changes in the frequency domain plot for different cases, we
immediately conclude that as the value of Bd decreases, the slope of the phase curve increases rapidly
at resonance which indicates a decrease in the damping ratio/damping factor of the system. This is
accompanied by an increase in the overshoot of the step response as can be observed from the plots. It
is also worthwhile to observe that the settling time increases as the damping factor reduces which is
consistent with our understanding on basic Control Theory.
Fig. 10. Step and frequency response of the sensor Bd = 100 Ba.
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Fig. 11. Step and frequency response of the sensor Bd = 10Ba.
Fig. 12. Step and frequency response of the sensor Bd = Ba.
Fig. 13. Step and frequency response of the sensor Bd = 0.1Ba.
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5.1.4. Effect of Ka on the Static Deflection of the Diaphragm
The magnitude of Ka relative to Kd can have a considerable effect on the static deflection of the
diaphragm. From Eqn. (20) it is readily seen that for Ka >>Kd, the value of x1 (t) depends only on the
stiffness of the diaphragm and the force Fin. If the magnitude of Ka is comparable to Kd, there will be
an appreciable change in the value of x1 (t). The same is depicted by Fig. 14 for a fixed value of
Bd=100Ba and two cases namely Ka=100Kd and Ka=Kd. The former yields a value of the static
deflection much closer to the true value (~16 µm) while the latter results in a much higher value of
about (32 µm).
Fig. 14. Effect of Ka on the step and frequency response (Bd =100Ba).
Fig. 14 (b) shows the effect of the magnitude of Ka relative to Kd on the frequency domain plot of the
sensor. When Ka =100Kd the magnitude plot indicates that the damping factor is lower than that for
Ka=Kd. This implies a much lesser settling time for Ka=100Kd which is evident from the step response
of Fig. 14. It is also to be noted that when Ka=Kd (or Ka has a magnitude comparable to Kd), the
effective value of the stiffness K series is higher than that for Ka=100Kd and hence a shift in the
resonant frequency is observed in the increasing/positive direction.
5.2. Differential Pressure Sensor
5.2.1. Frequency Response Analysis
From Fig. 15, it can be concluded that the actual resonant frequency of the system is 0.80 MHz, which
is almost closer to the theoretical result given by:
fr 
Kd / 2
Md
2

4.26  106
2  8.105  108
= 0.816 MHz
2
(34)
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Fig. 15. Magnitude – Phase plot of equation (28).
5.2.2. Root Locus Plot
Bf can be set as the parameter K in the equation for plotting the root-locus diagram, which represents
the closed loop transfer function of a non-unity negative feedback control system as shown in Fig. 16.
The denominator of equation (25) is defined as the characteristic equation of the form
(35)
Fig. 16. Rootlocus plot for equation 4.10.
Since the entire root locus plot is on the left half of the s-plane, and it is a stable system. Also with
respect to equation (25) the order of the system is 2. It is observed from the above plot that the value of
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Bf = 1.09106 the only value resulting in minimum overshoot of the response. But this value of Bf is
nowhere closer to the value predicted using squeeze film damping model. Also the value of Bf obtained
from the root locus plot is extremely high compared to the typical values found in microsystems which
are of the order of 10-6 Ns/m. The evaluation of Bf requires the understanding of fluid structure
interaction and Computational Fluid Dynamics and hence will be refined further.
The step response of the equation given (28) is given below. It is evident from the step response that,
for differential pressure, the diaphragm will oscillate back and forth and then settles. The oscillation is
in the order of 10-7, which again agrees the deflection properties of micro structures.
Fig. 17. Step response of the transfer function given in eqn. (28).
6. Conclusion
Thus the lumped parameter model of absolute and differential micro pressure sensors are developed,
whose diaphragm is designed to undergo very small deflections. A 3 Degree of Freedom with respect
to the fluid, diaphragm and the air between diaphragm and casing is considered. A simple approximate
model with proper assumptions are considered and analyzed first. The transfer function obtained from
the model is analyzed for its frequency and stability. The analytical natural frequency is found
matching with natural frequency obtained from the model with a small difference.
A more appropriate model with refined approximation is considered later. The effect of diaphragm
stiffness is compared with stiffness of air in casing. Also various ratio of damping of the fluid with
damping of diaphragm is considered and analyzed.
Later the first approximation is applied to the differential pressure sensor and the stability and
frequency of the model is analyzed and found to be more appropriate.
The analytical value of Bf is not matching with the value predicted using squeeze film damping model.
The evaluation of Bf requires the understanding of fluid structure interaction and Computational Fluid
Dynamics and hence will be refined further.
The refined model of the differential pressure sensor and analysis about the mismatch of damping of
fluid are left as future work.
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[2]. S. Soleimani, E. Abbaspour-Sani, Design of a Novel Micromachined Capacitive Engine Oil Pressure
Sensor, ICSE Proc., Penang, Malaysia, 2002, pp. 57-60.
[3]. Duane Tandeske, Pressure Sensors: Selection and Application, CRC Press, 1991.
[4]. Tai-Ran Hsu, MEMS and Microsystems Design and Manufacture, 1st edition, Tata McGraw Hill Education
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[5]. Norhayati Soin and Burhanuddin Yeop Majlis, An Analytical Study on Diaphragm Behavior for Micromachined Capacitive Pressure Sensor, in Proceedings of the ICSE 2002, Penang, Malaysia, 2002,
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Wiley India Pvt. Ltd., 2010.
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
91
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Geometrical Amplification of SMA Actuator Displacement
Using Externally Actuated Beam
1
Elwaleed Awad Khidir, 1 Nik Abdullah Mohamed, 2 Sallehuddin Mohamed Haris
1
Institute of Space Science, Faculty of Engineering and Built Environment,
Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
2
Department of Mechanical and Materials Engineering, Faculty of Engineering and Built
Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
Tel.: 0060142314544, fax: 00603-8921 6856,
E-mail: elwaleed@ukm.my
Received: 4 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: A major deficiency of shape memory alloy (SMA) actuators is that their displacement is
limited. This paper discusses the utilization of deflected flexible beams to amplify the displacement of
a SMA actuator. The actuator is composed of a SMA wire fixed eccentrically along a flexible beam
dividing it into equally spaced segments. A geometrical model based on the assumption that the
geometry of the beam when subjected to bending can be approximated by an arc (part of a circle). The
model is built to compute the beam end displacement and deflection upon heating the SMA wire for
different number of segments and different eccentricities. The model has been experimentally verified
and the results showed that the model is useful to predict the geometrical behavior of the actuator.
Copyright © 2012 IFSA.
Keywords: Shape memory alloy, Beam, Amplification.
1. Introduction
Shape memory alloy (SMA) materials have received increasing attention in the development of
innovative engineering systems for their dual functionality of sensing and actuating [1]. SMA actuators
can attain a high strength to weight ratio, which makes them ideal for miniature application compared
with conventional actuators such electrical, hydraulic and pneumatic which have difficulties in
generating significant forces when their size and weight are scaled down. Many linear SMA actuators
have been developed by researchers [2-11]. However; there are some limitations that need to be
overcome while using such actuators.
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The main physical limitation that needs to be overcome is the absolute percent strain that SMA’s can
achieve. The workable strain is usually around 5 percent. Many designs of actuators using SMA
depend on mechanically amplifying the displacement either through the use of long straight fibers or
through the use of coils [12].
Generally, two types of SMA beam actuators were proposed; internal (or embedded) actuators [13]
and external actuators [14] to control the beam characteristics or behaviour upon loading. External
actuators have much more control authority because with them differential movement between the
actuator and the beam is possible. This differential movement between the actuator results in an
additional moment as the beam deflects. External actuators can also be placed at different offset
distances from the beam. The moment, caused by the actuation force from the externally line actuator,
is much greater than that in a composite beam with an embedded line actuator along the beam and with
the same magnitude of the actuation force. Such a configuration also allows the introduction of fast
convection cooling [15].
The objective of this research is to amplify the SMA actuator strain (displacement) using externally
actuated flexible beams.
2. Materials and Methods
The actuator used in this research is an external actuator for the advantages mentioned before. It was
fabricated from a beam (150mm x 15mm x 1mm). The beam was divided into six segments by drilling
seven holes throughout its length, 25mm apart. Seven screws drilled laterally were used for fixing the
wire eccentrically along the beam (Fig. 1). The wire was electrically insulated from the beam by
inserting the screws through nylon insulating spacers (M3 x3).
Beam
SMA Wire
Fig. 1. Shape memory alloy beam actuator.
The experiments were conducted to test the beam deflection and hence the axial displacement. The
beam was tested for one, two, three and six segments (Fig. 2). Fig. 3 shows examples of one and two
segments configurations upon heating the SMA wire. The hypothesis is that for a higher number of
segments a higher deflection and end displacement is obtained upon heating the SMA wire.
Beam
SMA Wire
(a) one segment
(b) six segments
Fig. 2. SMA beam different configurations.
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
x

x
Fig. 3. Performance of externally actuated beam for one and two segments.
3. Mathematical Modeling
A geometrical analysis was first carried out to investigate the deformed shape of a flexible beam
caused by an externally-attached SMA wire. The proposed bending actuator configuration originates
from that the geometry of the flexible beam when subjected to bending can be approximated by an arc
(part of a circle).
The SMA wire provides actuating force to produce bending of the flexible beam. When the wire is
heated above austenitic start temperature, the wire will start to contract to its original length, thereby
applying an actuation force on the beam. As the actuator is cooled below the martensitic finish
temperature, the wire will elongate back approximately to its prestrained length by the virtue of the
flexural rigidity of the beam. Heating and cooling the wire results in cyclic contraction and expansion
of the actuator.
The first configuration considered is that in which the SMA wire is attached to the ends of the beam.
Since the interest is in bending, the wire was attached eccentrically at an offset distance (a) (Fig. 4).
Before heating the wire:
Lm
where L and m are the beam length and the SMA wire length, respectively.
After heating the wire:
m  L(1   )
(1)
where  is the SMA wire strain.
Since the curved beam was assumed to be part of a circle:
L  R
(2)
where R and : are the circle radius and the central angle, respectively.
 
m  2R  a sin  
2
(3)
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Hence the end displacement, x, is given by:
xLm
(4)
 
x  L  2 R  a  sin  
2
(5)
or
Therefore, Solving Eq. 2 and Eq. 3 for a given L and , the end displacement can be found. The beam
deflection can be given by:

  
  R  1  cos   
2
(6)
 


m
x
a

R
Fig. 4. Deformation of one segment beam.
For two segments (Fig. 5), as observed a new term is included, which is k. Following the same
previous procedure (for one segment), the end displacement and the deflection can be found as
follows:
After heating the wire
m  L(1   )
L  R
(7)
 
m  4R  a sin  
4
(8)
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 
k  2R  a sin  
2
(9)
xLk,
(10)
where k is the distance between the beam ends
 
x  L  2 R  a sin  
2
(11)

  
  R  1  cos   
2
(12)


m/2
k
x
a
R

Fig. 5. Deformation of two segments beam.
Generally and for n number of segments the following equations are derived:
 
m  2nR  a sin  
 2n 
(13)
 
k  2 R  a sin  
2
(14)
 
x  L  2 R  a sin  
2
(15)

  
  R  1  cos   
2

(16)
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3. Result and Discussion
3.1. Simulation
The SMA wire used is of 0.7 mm diameter, and prestrained to a residual strain of 4.8 % (actual
displacement is 6.95 mm) so that the wire length is 150 mm. The eccentricity was 5 mm. Due to the
nonlinearity of the end displacement and the deflection equations; the simulation was performed using
a FORTRAN language. The constant parameters in the simulation were the beam length, the
eccentricity and the SMA wire strain. The program was run for different number of segments to obtain
the end displacement and the deflection for each segment.
Fig. 6 represents the results obtained using Eq. 15 for the end displacement versus the number of
segments. The figure shows that the end displacement increases as the number of segments increase.
However, for number of segments higher than 6 the increment is not significant. Fig. 7 represents the
results obtained using equation Eq. 16 for the beam deflection versus the number of segments. The
figure shows the beam deflection increases as the number of segments increase. However, again for
number of segments higher than 6 the increment is not significant. This indicates that the beam of six
number of segment is more suitable for the SMA actuator design.
25
Displacement (mm)
20
15
10
5
0
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30 32
Number of segments
Fig. 6. Analytical results for end displacement at different number of segments.
Fig. 7. Analytical results for beam deflection at different number of segments.
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3.1. Experimental Results
An experiment was conducted to verify the increase of end displacement and beam deflection when
increasing the number of segments. The tests were conducted for 1, 2, 3 and 6 number of segments.
The wire was heated above the austenite finish temperature until the whole applied strain is recovered.
Table 1 shows the results obtained for 1, 2, 3 and 6 number of segments. The end displacement
increases by 100 %, 220 %, 280 % for 2, 3 and 6 number of segments when compared with the
displacement obtained by 1 segment. As a result this type of actuators was successfully used as
presented by Elwaleed et al. [16,17].
Table 1. Experimental Results for End Displacement and Beam Deflection.
No. of segments
1
2
3
6
Deflection
()
15
21
23
25
End points distance
(k)
145
140
134
131
End displacement
(x)
5
10
16
19
Increment
(%)
0
100
220
280
Fig. 8 and 9 represent analytical and experimental results obtained for the end displacement and beam
deflection, respectively, versus the number of segments. The experimental results show that there is an
increase in both end displacement and deflection with the increase of number of segments. The
closeness of the analytical results and experimental results show that the analytical approach in this
research provides a useful tool to quantitatively predict the behaviour of the actuator. The
discrepancies could be attributed to the geometrical approximations. The accuracy can be improved by
using smaller screws to reduce the part of the wire gripped by the nuts and hence increasing the
activated wire length.
25
Displacement (mm)
20
15
10
x(analytical)
5
x(experimental)
0
0
1
2
3
4
5
6
7
Number of Segments
Fig. 8. Analytical and experimental results for end displacement at different number of segments.
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30
Deflection (mm)
25
20
15
10
delta(analytical)
5
delta(experimental)
0
0
1
2
3
4
5
6
7
Number of Segments
Fig. 9. Analytical and experimental results for beam deflection at different number of segments.
The previous analysis was performed for constant eccentricity (5 mm). However, if the eccentricity is
varied this will result in variation of the end displacement and beam deflection. Fig. 10 shows the
variation of end displacement with wire contraction for different eccentricities. It is obvious that the
increase of eccentricity lead to increase in displacement. The eccentricity is governed by conditions,
such as space occupied by the actuator, required moment and beam stiffness. This means the
eccentricity also play an effective role in the design.
Fig. 10. Variation of displacement with deformation for different eccentricities.
5. Conclusions
The results showed that the geometrical model is useful to predict the geometrical behavior of the
externally actuated beam in terms of end displacement and deflection. Both displacement and
deflection are increased when increasing the number of segments. An increment of 280 % in end
displacement can be obtained six segments. However, it is not beneficial to use actuators with more
than six segments due to the insignificant increment of deflection and end displacement.
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actuators for Automotive Tumble Flap, IECON, 2006.
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[11].S. V. Sharma, M. M. Nayak, N. S. Dinesh, Modelling, design and characterization of shape memory alloybased poly-phase motor, Sensors and Actuators A, 147, 2008, pp. 583–592.
[12].D. Grant, V. Hayward, Design of shape memory alloy actuators with high strain and variable structure
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[13].D. C. Lagoudas, I. G. Tadjbakhsh, Deformations of active flexible rods with embedded line actuators,
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[14].A. Baz, K. Imam, J. McCoy, Active vibration control of flexible beams using shape memory actuators,
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[15].G. S. Shu, D. C. Lagoudas, D. Hughes, J. T. Wen, Modeling of a flexible beam actuated by shape memory
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[16].Elwaleed Awad Khidir, Nik Abdullah Mohamed, Mohd Jailani Mohd Nor, Mohd Marzuki Mustafa, A new
concept of a linear smart actuator, Sensors and Actuators A, 135, 2007, pp. 244–249.
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__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
High Accuracy Resolver to Digital Converter Based on Modified
Angle Tracking Observer Method
1
Chandra Mohan Reddy Sivappagari, 2 Nagabhushan Raju Konduru
1
JNTUA College of Engineering, Pulivendula, 516390, India
Tel.: 9441023800
2
Sri Krishnadevaraya University, Anantapur, 515002, India
Tel.: 9866590987
E-mail: email2cmr@gmail.com, knrbhushan@yahoo.com
Received: 11 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: A resolver is a rotary transformer where the magnitude of the energy through the resolver
windings varies sinusoidally as the shaft rotates. These are unsurpassed in its ability to reliably supply
rotary angular position data in the harsh industrial environments. This paper presents the design of
software based resolver to digital converter based on modified angle tracking observer method to
estimated the rotor shaft angle. The proposed resolver to digital converter has been successfully
implemented in MATLAB® Simulink and the results are verified by simulation for different rotor
speeds. This method brings down the hardware cost and increases the accuracy and reliability.
Copyright © 2012 IFSA.
Keywords: Resolver, resolver to digital converter, angle tracking observer, synchronous demodulator
1. Introduction
The measurement of rotor shaft angle is one of the important requirements in the modern control
system, instrumentation and computing technologies. Every machine, process and monitoring system
has a rotating shaft in its mechanism. The techniques of the mechanisms for converting shaft rotation
to linear motion extend the usefulness of shaft angle sensing. Over the years, many different forms of
shaft angle transducers have been developed.
On the basis of their physical design, these angular transducers can be classified into two main groups:
optical and inductive. The built in semiconductors in optical transducers are used to amplify and to
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format the digital output signals. Whereas these semiconductors are sensitive to temperature and the
LED light sources commonly employed are susceptible to aging. Inductive transducers such as synchro
and resolvers are intrinsically absolute and require no semiconductors on the transducer itself and the
raw output signal can be transmitted over distances of more than 100 meters. In addition, since they
consist primarily of copper and steel, resolvers are virtually insensitive to temperature over a wide
range. Because of no sensitive electronics or optics are employed, resolvers are often supplied in an
unhoused or pancake configuration and can be mounted directly to the shaft whose position is to be
measured. Cost and length savings are realized by the user since no shaft−to−shaft coupling or extra
bearings are required. Synchros have three stator coils in a 1200 orientation. They are more difficult
than resolvers to manufacture and are more costly. Today, synchros find decreasing use, except in
certain military and avionic retrofit applications.
The resolver output analog signals have to be converted to estimate the rotor angular position. The
resolver to digital converter (RDC) is used to convert the resolver output signals into angular position
data. RDC performs two basic functions: demodulation of the resolver signals to remove the carrier,
and angle determination to provide a digital representation of the rotor angle. All RDC techniques use
the two analog signals to produce a digital output. The differences between the various converter
methods is in the resolution available, the speed at which the shaft can be rotated and still maintain the
designed resolution and the sensitivity of the system to the unwanted distortion of the resolver signals
[1]. The main drawback of RDC is its cost, which is about the same price as that of the resolver [2] and
[3].
Recently, researchers have paid attention on RDCs with soft computing techniques to improve the
linearity, resolution and accuracy of the rotor shaft angle of the resolvers. Several simple and cost
effective methods are proposed in the literature to convert the resolver signals into digital data and to
avoid the use of RDCs.
An angle tracking based RDC with bang-bang type phase comparator is proposed for fast tracking [4]
to solve the problem in PLL based technique. This method suffers from tracking errors at high speeds
and out of lock conditions of the PLL, amplitude demodulators and carrier oscillators. A simple hybrid
structure board for RDC that contains a clock unit, two analog to digital converters (ADCs), two signal
conditioning circuits and an electrically programmable read only memory (EPROM) proposed in [3]
and [5]. However, the proposed method is a low cost but requires hardware and is an open loop
method. In [6], the calculated angular position of a resolver is obtained by a closed loop operation.
This method may not be practical when the low cost fixed point digital controller is used. A resolverto-3600 linearized converter method that doesn’t need a processor is proposed in [7]. This method does
not provide the advantage on hardware like oscillators, amplitude demodulators and consequently
weight, size and cost.
A high precision, hybrid electronic structure for RDC is developed in [8]. The instantaneous rotor shaft
angle is determined by using a linearization technique. In addition, a separated waveform generator is
required. In [9] and [10], an open loop angle estimation method was introduced and is based on the
comparison between the excitation signal and output signals of the resolver. However, a separate
signal generator is required to generate signals for resolver excitation and demodulation. Based on the
principle of synchronous demodulation of the resolver output signals, a software-based RDC algorithm
is presented in [11]. In this method, a lookup table is used with arctangent function to estimate the
angular rotor shaft position. This method increases the software load on the control processor.
However, the usage of a waveform generator is avoided and economized the related cost. The
drawbacks of this method are that it is limited to low-speed applications and the sampled angular
position envelopes are very sensitive to noise. A linearized tangent/cotangent converter using analog
circuitry is implemented in [12]. The tangent/cotangent method is an open loop method that may not
provide high angle accuracy. To make the sampled angular position envelopes insensitive to noise, an
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improved of version of [11] is proposed in [13]. The demodulation method of output signals and
design aspects of RDC have not been discussed.
Software based RDC allows saving on costly oscillator required for excitation of rotor and hardware
efficient demodulation of the resolver output, even in the presence of wide variations in the resolver
carrier. This software based approach does not cause any time delay and the dynamics of the system
using this method is not affected. Based on the above literature, it is concluded that it is essential to
implement a high accuracy software based RDC to measure the rotor angle and speed of a resolver.
In this paper, an RDC algorithm based on modified angle tracking observer method (ATO) is
presented. This algorithm is simple and can be incorporated in an advanced motor control drive
system. The cost of the RDC is reduced by exciting the resolver with a square wave signal that can be
generated by any microprocessor. The proposed algorithm attempts to minimize the error between
actual rotor angle and estimated rotor angle using a feedback loop.
The paper is organized into five sections. Section 2 describes the basic principle of a resolver. This is
followed by the design procedure of the proposed RDC in section 3. Simulation, results and
discussions are presented in section 4 and 5 respectively. Finally conclusions are drawn in section 6.
2. Basic Principle of a Resolver
A resolver is a position sensor or transducer which measures the instantaneous angular position of the
rotating shaft to which it is attached. The resolvers are derived from the fact that they operate by
resolving the mechanical angle of their rotor into its orthogonal or Cartesian components [14]. The
frequency response of resolver is shown in Fig. 1 and is similar to that of a transformer with a high
leakage reactance. Corner and peak frequencies depend on the impedance of the individual sensor.
Most resolvers are specified to work over 2 V to 40 V rms and at frequencies from 400 Hz to 10 kHz.
Angular accuracies range from 5 arc-minutes to 0.5 arc-minutes.
Fig. 1. Frequency response of a resolver.
Fundamental transformer theory is the basis of resolver design. The placement of the reference and
output windings with respect to the shaft of a resolver is shown in Fig. 2. In a resolver, the iron core
for the primary and secondary are two multi toothed lamination stacks, one being stationary (stator)
and one which rotates (rotor). The output voltage is affected by change in the position of the secondary
winding relative to the primary winding.
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Fig. 2. Internal view of a resolver.
As the rotor turns, the amplitude of the secondary voltage changes by modulating the input carrier.
Secondary windings are always placed with their axes at right angles. This establishes two separate
outputs having a sine and cosine relationship. An equivalent cross sectional view of the resolver with
angular position of the rotor, θ, with respect to the windings and the associated signals are shown in
Fig. 3 and Fig. 4 respectively.
Fig. 3. Equivalent cross sectional view.
Fig. 4. Resolver excitation and output signals.
The winding of the rotor is supplied with a high-frequency sinusoidal carrier signal:
V p  A  Sin( wc t )
(1)
,
where A is the peak amplitude and wc  2fc , where fc is the frequency. The resolver operates as a
rotary transformer with two outputs. The angular velocity d  of the rotor is much lower than w , the
c
dt
two stator windings of the resolver modulated signals are given by
V s1   A Sin ( w c t ) Sin ( ) 

V s 2   A Sin ( w c t ) Cos ( ) 
,
(2)
where θ is the angular position of the shaft of the resolver and α is the transformation ratio constant
between rotor and stator windings. These two output signals Vs1 and Vs2 are called as quadrature
signals. By simple demodulation of the stator signals in (2), the excitation signal may be removed,
resulting in sine and cosine signals. The demodulation and amplification of (2) results in normalized
signals:
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VS  Sin ( ) 

VC  Cos ( ) 
(3)
The rotor angle, θ can be extracted from (3) using a suitable RDC. The RDC uses the excitation signal
and both output signals of resolver to determine the rotor shaft position. The RDC is based on ATO
method in which an estimated angle tracks the real angle of the rotor. This RDC also gives the speed
information of the rotor.
3. Proposed Method
3.1. Generalized Angle Tracking Observer (ATO) Method
The block diagram of generalized ATO based RDC is shown in Fig. 5. The two outputs of the resolver
are applied to cosine and sine multipliers. These multipliers incorporate sine and cosine lookup tables
and function as multiplying digital to analog converters. Assume that the current state of the up/down
counter is a digital number representing a trial angle, ф. The converter seeks to adjust the digital angle,
ф, continuously to become equal to and to track θ, the analog angle being measured.
Fig. 5. Generalized angle tracking observer based resolver to digital converter.
 A Sin(wct ) Sin( ) Cos()
(4)
The digital angle ф is also applied to the sine multiplier and multiplied by Vs2 to produce the term:
 A Sin(wct ) Cos( )Sin( )
(5)
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These two signals in (4) and (5) are subtracted from each other by the error amplifier to yield an AC
error signal of the form:
 A Sin(wct ) Sin( ) Cos( )  Cos( )Sin( )
(6)
Using a simple trigonometric identity, this reduces to:
 A Sin(wc t ) Sin(   )
(7)
The detector synchronously demodulates this AC error signal, using the resolver's rotor voltage as a
reference. This results in a DC error signal proportional to Sin(θ – ф). The dc error signal feeds an
integrator, the output of which drives a Voltage Controlled Oscillator (VCO). The VCO, in turn,
causes the up/down counter to count in the proper direction to cause:
Sin (   )  0
(8)
And using the Taylor approximation around zero
Sin(   )  (   ) for     1
(9)
(   )  0
(10)
 
(11)
When this is achieved,
and therefore
to within one count. Hence, the counter's digital output, ф, represents the angle θ. The latches enable
this data to be transferred externally without interrupting the loop's tracking.
In this ATO method, the estimated angle was accepted as a real angle and this can be accepted at low
speeds only. Whereas at high speeds, the value of (   ) is dependent on the rotor speed. In order to
remove this error at high speeds, a new modification is proposed in the generalized ATO method.
3.2. Implementation of Modified ATO based RDC
The basic idea of the proposed RDC is to trigger the two samples and hold circuits to demodulate the
resolver output signals. The excitation of rotor is a square wave signal with a frequency of 5kHz,
which is high enough for high speed applications. The triggering must be in synchronous with the
excitation signal. The sampling and conversion is performed when the raising edge of the excitation
signal. This is a synchronous demodulation method requires the sampling frequency to be perfectly
synchronized with the input signal. For perfect synchronization, it is proposed to use the same square
wave signal source for resolver excitation and to trigger the two samples and hold circuits. By using
this technique the cost of the oscillator circuit is saved.
The proposed modified RDC block diagram is shown in Fig. 6. After sampling the resolver output
signals, the available demodulated signals are
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A

Sin ( n ) 

2

A
Vcn 
Cos ( n ) 
2

Vsn 
(12)
Fig. 6. Block diagram of the Proposed modified RDC.
For an observer, it is very critical to have an appropriate initial estimated value, Φ. As the values of
Sin ( n ) and Cos ( n ) are available after the first sampling, the rotor angle can be estimated based on
the quadrant of the rotor angle at the initial stage. For the first cycle 0, 2  , the values of Sin ( n ) are
 
 5

less than Cos ( n ) in the interval  0,  and  , 2  thus the initial estimation of the rotor angle for
 4
 4

13

these intervals is taken as the mean of two angles and it is equal to and
respectively. Similarly
8
8
  5 
the values of Sin ( n ) are greater than Cos ( n ) in the interval  ,  and the initial estimation of the
4 4 
3
. This initial estimated angle at time, t=0 second is taken
rotor angle for this interval is taken as the
4
out from a conditional statement and is implemented in MATLAB® Simulink. For other than t=0
second, the estimated angle is equal to the output of the proposed system. By using this algorithm the

maximum possible angle error at the initial time t=0second is equal to or less than  22 .50 .
8
The initial estimated value of the rotor position,  n 1 is given as input to the sine and cosine
multipliers. Any error, ε between the estimated values and next sampled values of
Sin ( n ) and Cos ( n ) results in nonzero value in the following equation:
A
2
Sin( n ) Cos(n1 )  Cos( n )Sin(n1 )  A Sin n  n1    n1
2
(13)
The equation (13) provides good approximation for small values of  n   n 1    n 1 . The estimated
value of the angle Φn-1 has been modified to decrease the error εn-1. The modified Φn value is given as:
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n  n1  K n1 ,
(14)
where K is the gain factor. The value of K is important for stability of ATO to force the error to
converge to zero and it is calculated as
 
 
K    Sin   1.02617
8
8
(15)
To have a good convergence of the estimated value of the rotor angle, the value of K is assigned in the
range (1, 1.026). In the proposed RDC, the value of K is taken as 1.01.
This angle tracking observer functions as an integrator and provides a robust noise insensitive system
because of its inherent filtering behavior.
4. Simulation
The proposed system is crated in MATLAB® Simulink blocks. The main blocks in the model of
modified ATO based RDC are synchronous demodulator, sample and hold circuit and angle tracking
program logic. MATLAB® Simulink model of modified ATO based RDC is shown in Fig. 7. The
excitation signal generator feeds the high frequency square wave signal to the resolver. When the rotor
of the resolver is rotated, the resolver induces two amplitude modulated output signals, as in (2). These
amplitude modulated signals need to be demodulated using synchronous demodulator to obtain the
rotor shaft angle position. The block diagram of synchronous demodulator system is shown in Fig. 8.
Fig. 7. MATLAB® Simulink model of modified ATO based RDC.
Fig. 8. Block diagram of synchronous demodulator.
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5. Results and Discussions
The proposed RDC and its angle tracking algorithm are validated through several tests with different
speeds. The frequency of excitation square signal is taken as 5kHz and is applied to the resolver. In
Fig. 9, the square wave signal and resolve two output signals for a rotor speed of 1200 rpm are
presented. The output signals of the resolver are amplitude modulated with the square wave. In Fig. 10
and 11, the demodulated signals of the output of the resolver using synchronous demodulator are
presented. The same square wave signal is used to synchronize the resolver and demodulator. The
square wave signal of frequency 5 kHz is eliminated perfectly from the output signals of the resolver.
Fig. 9. Excitation and resolver output signals.
Fig. 10. Resolver sine output and its demodulated signal.
The rotor angle is extracted from the demodulated signals based on the software algorithm. Fig. 12
shows the Vs, Vc and the estimated angle for a rotor speed of 1200 rpm. As per the results shown in
Fig. 12, the maximum estimated angle error of the proposed RDC is 0.0136 for a rotor speed of
1200 RPM. The Plots of excitation signal, resolver output signals; Vs, Vc, estimated angle; estimated
angle estimated angle and angle error, and actual angle and measured angle are obtained with the help
of MATLAB® Simulink and are shown in Fig. 13, 14, 15 and 16 respectively for the rotor speed of
3000 rpm.
Fig. 11. Resolver output and its demodulated signal.
Fig. 12. Vs, Vc and estimated angle.
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Fig. 13. Excitation and resolver output signals.
Fig. 14. Vs, Vc and estimated angle.
Fig. 15. Estimated angle and error.
Fig. 16. True and estimated angles.
(blue color is true angle and green is estimated angle)
The estimation error has been obtained for different speeds and the results are presented in Table 1.
For speeds less than 600 rpm the proposed method gives a negligible error and as the speed of the rotor
is increased the error will increase by a factor of 0.0030. The graph between the rotor speed and
estimated error is shown in Fig. 17.
Table 1. Estimated error for different speeds.
Rotor Speed
(rpm)
120
240
300
600
900
1200
1500
2400
3000
Estimated error
(degrees)
0.0017
0.0030
0.0038
0.0071
0.0104
0.0136
0.0172
0.0272
0.0335
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Fig. 17. Graph between estimated angle error and rotor speed.
6. Conclusions
Through mathematical performances analysis, it is showed that all the proposed blocks computed the
estimated angle with negligible error through simulation implementation. The proposed modified ATO
based RDC has been successfully implemented in MATLAB® Simulink. This method brings down the
hardware cost and increases the accuracy and reliability. According to simulation results, the angle
tracking program algorithm tracks the angle and force the error to zero. Thus the estimated angle
eventually matches with the true rotor angle.
References
[1]. Jens Onno Krah, Heiko Schmirgel and Marcel Albers, FPGA based resolver to digital converter using
delta-sigma technology, in Proc. of PCIM, Europe, 2006, pp. 931-936.
[2]. Davood Arab Khaburi, Software based resolver-to-digital converter for DSP-based drives using an
improved angle-tracking observer, IEEE Transactions on Instrumentation and Measurement, Vol. 61,
No. 4, April 2012, pp. 922-929.
[3]. C. Attaianese and G. Tomasso, Position measurement in industrial drives by means of low-cost resolver-to
digital converter, IEEE Trans. Instrum. Meas., Vol. 56, No. 6, Dec. 2007, pp. 2155–2159.
[4]. Choong-Hyuk Yim, In-Joong Ha and Myoung-Sam KO, A resolver-to-digital conversion method for fast
tracking, IEEE Transactions on Industrial Electronics, Vol. 39, No. 5, 1992, pp. 369–378.
[5]. C. Attaianese, G. Tomasso and D. DeBonis, A low cost resolver-to-digital converter, in Proceedings of the
IEEE International Electrical Machine Drives Conference, Cambridge, MA, June 2001, pp. 917–921.
[6]. A. O. Di Tommaso and R. Miceli, A new high accuracy software based resolver-to -digital converter, in
Proceedings of the IEEE International Conference, 2003, pp. 2435-2440.
[7]. Mohieddine Benammar, Lazhar Ben-Brahim, and Mohd A. Alhamadi, A novel resolverto-3600 linearized
converter, IEEE Sensors Journal, Vol. 4, No. 1, 2004, pp. 96–101.
[8]. Mohieddine Benammar, Lazhar Ben-Brahim, and Mohd A. Alhamadi, A high precision resolver-to-DC
converter, IEEE Transactions on Instrumentation and Measurement, Vol. 54, No. 6, December, 2005,
pp. 2289-2296.
[9]. Lazhar Ben-Brahim, Mohieddine Benammar, Mohd A. Alhamadi, Nasser A. Al-Emadi and Mohammed
A. Al-Hitmi, A new low cost linear resolver converter, IEEE Sensors Journal, Vol. 8, No. 10, October
2008, pp. 1620-1627.
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[10].Lazhar Ben-Brahim, Mohieddine Benammar and Mohd. A. Alhamadi, A resolver angle estimator based on
its excitation signal, IEEE Transactions on Industrial Electronics, Vol. 56, No. 2, February 2009,
pp. 574-580.
[11].S. Sarma, V. K. Agrawal and S. Udupa, Software-based resolver-to-digital conversion using a DSP, IEEE
Transactions on Industrial Electronics, Vol. 55, No. 1, January 2008, pp. 371-379.
[12].Mohieddine Benammar, Mohamed Bagher and Mohammed Al Kaisi, Novel linearizer for
tangent/cotangent converter, in Proceedings of the IEEE 2009 International Conference, pp. 575-578.
[13].Zhu Ming, Wang Jianming, Ding Ling, ZhuYi and Dou Ruzhen, A software based robust resolver-todigital conversion method in designed in frequency domain, in Proceedings of the IEEE International
Symposium on Computer Science and Society, 2011, pp. 244-247.
[14].Advanced Motion Technology, Understanding resolvers and resolver-to-digital conversion, Catalog of
Admotec, 1998. Available online at: http://www.admotec.com/TT02.pdf
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Development of Single Place Multiple Obstacle Avoidable
System for Guarded Tele-operated Trolley, a Service Robot
Using Single Ultrasonic Sensor
Subrata CHOTTOPADHAYA and Soumendra Nath KUNDU
Department of Electrical Engineering, National Institute of Technical Teachers’ Training
and Research, Kolkata [Under MHRD, Govt. of India],
Block-FC, Sector-III, Salt Lake City, Kolkata-700 106, India
E-mail: subrata0507@sify.com, santu_phonix@yahoo.co.in
Received: 18 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: This paper depicts the development of single place multiple obstacle avoidable system for a
guarded tele-operated trolley-service robot. A guarded tele-operated mobile robot is that which must
have the ability to sense and avoid obstacles but otherwise it will navigate as driven, like a robot under
manual tele-operation. The configuration of the system consists of ultrasonic sensor, signal
conditioning circuits, radio communication module, controller, and actuators. The Obstacle avoidance
algorithm is developed based on physical realization of the requirement. The ultrasonic switch is
designed to sense the front obstacle of the robot. An AT89C52 microcontroller is used in order to
receive the sensor signal and generate the algorithm and control the movement of the mobile robot for
obstacle avoidance. System implementation is briefly described to depict the system as a whole.
Experimental results are presented to demonstrate and validate effectiveness of the technique used.
Copyright © 2012 IFSA.
Keywords: Guarded tele-operated mobile robotic system, Ultrasonic sensor, Automatic multiple
obstacle avoidance, Human control mode.
1. Introduction
In the real world situation many condition occurs where man can see the environment where some
useful service is needed but no one can go there due to some hazardas condition or some health
restrictions or due to some security constrains. But a guarded tele-operated trolley can provide the
service in the hospital ICU environment to the nuclear polluted environment area very smoothly. In
human environment unwanted obstacles are common factor for any area of consideration. So the need
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for unwanted obstacle avoidance is always significant for robot navigation system. To avoid them the
system have to identify the obstacle first, then the system have to avoid the obstacle using some
avoiding algorithm. So designing a system for avoiding the accident of obstacles such as the chair or
any human equipment on the way is essential to make the trolley robot’s safe working. Ultrasonic type
of sensor has been widely used for environment detection and obstacle avoidance of autonomous
mobile robots [1, 2]. There exists single sensor application [3] as well as multiple sensor application
for better results [4, 5]. It has wide detection angle and offers a less expensive solution. However, the
drawback of this type of sensor is that ultrasonic waves are transmitted through air and the reflex
surface texture will affect the measurement. But we select ultrasonic sensor for it’s wide surface
measurement property & low cost. There exists many particular application based design & use of
ultrasonic systems [6, 7]. The ultrasonic trans-receivers are used in different positions in mobile robot.
They are may be in the front for front obstacle avoidance, may be in backside [8], may be in lateral
position [9]. The work described in this paper is mainly concerned with the ultrasonic obstacle
avoidance system based on RF+AT89C52 of the guarded tele-operated trolley, a service robot. The
whole architecture, hardware and software design of the obstacle avoidance system will be discussed
later.
2. The Whole Architecture of the System
The core of the guarded tele-operated trolley ultrasonic obstacle avoidance system is AT89C52
microprocessor; the modules which are connected with the processor are designed ultrasonic sensors,
RF module, driver of the actuators and the actuators to control the motion of the trolley in the time of
obstacle avoidance. Its main function is monitoring the obstacles within 36 cm in front of the trolley’s
way, helping the trolley to avoid multiple obstacles timely if there are obstacles. If obstacle is not there
the system’s main function is sending the actuator control signal information to the robot
microcontroller via RF transmitter-Receiver module. The hardware schematic of the system is depicted
in Fig. 1.
Fig. 1. System Block Diagram.
3. System Hardware Design
The system hardware can be divided into three different parts. They are The Ultrasonic Transducer,
The FSK Radio transmitter Module with control switch and The FSK Radio Receiver Module with
Driver, Actuator & microcontroller. Now we will go through this parts one by one.
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3.1. The Ultrasonic Transducer
The Ultrasonic transducer consists two basic circuits. They are Continuous Ultrasonic Transmitter
Circuit and The Ultrasonic Receiver Circuit. Description of both is given below.
3.1.1. The Continuous Ultrasonic Transmitter Circuit
Transmitter circuit is a simple circuit, consisting of signal generator and an ultrasonic transmitter. IC
4047 functions as signal generator, producing 40 kHz electrical signal burst. We get this signal from
pin no 10 & 11 of the IC & fed to two input pins of Ultrasonic Transmitter. This IC acts as an asteble
multivibrator, and generates a squire wave such that there is constant voltage difference between two
terminals of transmitter, while having oscillatory electrical input pulses. High voltage pulses excite the
piezoelectric element in the ultrasonic transducer, causing this element to oscillate at 40 kHz
frequency. This will cause the transducer to produce an oscillatory acoustic output, and thus ultrasonic
sound waves are generated due to piezoelectric effect. Fig. 4 describes the circuit diagram.
Fig. 2. Ultrasonic Transmitter module.
3.1.2. The Ultrasonic Receiver Circuit
The Fig. 3. Describes The Ultrasonic Receiver Circuit. The reflected sound waves are detected by the
ultrasonic receiver. When an acoustic wave reaches the piezoelectric element; the element produces a
voltage which is the sensor signal. This signal is generally of low amplitude, and less strength. This
signal may also contain unwanted noise signals, which are generated from atmospheric sources. Those
signals received from transducer are fed to FET Amplifier for amplification. The Amplifier is designed
in such a way that it amplify with an overall gain of 100, in two stages. This amplifier is operated in
inverting mode with negative feedback. Amplifier output is collected across 7th pin and connected to 4
-input NAND gate, which functions as a Schmitt trigger. All the pins 1, 2, 4 and 5 are connected to the
amplifier output, through a variable resistance of 10 kOhm. This variable resistance is used for Range
and Sensitivity adjustment. Pin 3 of IC7413 is not connected, and output is collected at 6th pin of the
Schmitt trigger IC. The output of Schmitt trigger is not an exact replica of 40 kHz input electrical
signal but it’s frequency is 40 kHz. This Schmitt trigger output is fed to input pin of PLL. The PLL
circuit is designed such a way that, if it detects 40 kHz pulse in the input signal its output will become
low. For any other frequencies, output is high. The PLL (LM567) acts as a tone decoder set to lock
onto 40 kHz signal. The output of the tone decoder is HIGH when no echo is heard and swings LOW
when an echo is detected. From pll we get 4 V output when obstacle is detected & we get 4.9 V when
obstacle does not exists. This change is fed to a comparator whose output is 10.5 V when obstacle
exists & 1.9 V when not. These voltages are made 4.7 V when obstacles exist & 0.85 V when not by
proper voltage divider circuit (POT). Fig. 4 shows different Signals across transmitter & receiver
circuit.
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Fig. 3. Ultrasonic Receiver Module.
(a) Signal of 40 kHz
Burst from IC 4047
(b) Signal before amplifier
(c) Signal after capacitor
(d) Signal with ultrasonic
loading
(e) Signal after amplifier
(f) Signal after Schmitt Trigger
Fig. 4. Signals across transmitter & receiver circuit.
3.2. The FSK Radio Transmitter Module with Control Switch
Transmitter circuit is a simple circuit, consisting of HT-12E encoder which encodes the switching
signals and send it to the FSK transmitter module which sends the signal to the robot through a
10 Ohm antenna. When power is on the circuit is able to control the trolley’s motion with a human
navigator by four switches sw1 for forward, sw2 for backward, sw3 for rotate right, sw4 for rotate left.
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The switches are connected through pull-up & pin 1 to 9 of HT-12E connected to ground to get the
logic ‘ground to on’ in this circuit. The resistor 760 k connected to in built crystal pin 15&16. We
will get all connections details in Fig. 5.
Fig. 5. Human control module.
3.3. The FSK Radio Receiver Module with Driver, Actuator & Microcontroller
The receiver circuit also is a simple circuit, consisting of a 10 Ohm RF antenna which catches the
transmitted signals with the help of receiver module and send it to the HT-12D decoder which decodes
the receiving signals and fed it to the microcontroller.
Fig. 6. Mobile Robot Circuit.
The microcontroller receives the control signal from the pins P2.0, P2.1, P2.3 & P2.4 and controls the
left and right motor’s forward or reverse movements to control the trolley movement via pins P1.0,
P1.1, P1.3 & P1.4 which are connected to the driver IC-L293D. The sonar data is fed to the
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microcontroller pin P2.5. The microcontroller monitors these pins all time. The details of circuit
diagram are shown in the fig.6 & fig.7 shows the pictorial representation of the robot.
Fig.7. Pictorial view of Robot.
4. The Design Formula
4.1. Astable Mode Design Formula
In the astable mode operation on time that is Ton and OFF time that is Toff is given by,
Ton= -RC ln ( Vtr/ Vdd + Vtr )
Typically this is,
Ton= 1.1 RC
Toff= = -RC ln ( Vdd – Vtr / 2 Vdd + Vtr )
Typically this is also,
Toff= 1.1 RC
So Time period,
T = Ton+Toff [in terminal 13]
And Time period
T’=2(Ton+Toff) [in terminal 10]
4.2. Op-Amp Gain Formula
In the negative feedback mode of operation, operational amplifier gain is given by,
AV = Rf / R1
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4.3. PLL Centre Frequency
In PLL tone detector, the free-running frequency of the current controlled oscillator in the absence of
an input signal, is known as centre frequency, which is given by,
fO = 1 / (1.1 R1 C1)
4.4. PLL Detection Bandwidth
The frequency range, centered about fO, within which an input signal above the threshold voltage
(typically 20 mVRMS) will cause a logical zero state on the output. The detection bandwidth
corresponds to the loop capture range.
BW (in % of fO) = 1070 V1 / (fO C1)
5. The Software System
5.1. Algorithm Development
1.
2.
3.
4.
5.
Start the program.
Initialize direction value by left.
If obstacle exists enter the avoidance program else go for computer command.
Initialize degree value by ‘0’ degree.
If obstacle exists in front of the vehicle swap direction value left to right or right to left & increment
degree value by 45 degree if not go for recovery program i.e., go forward for two delay, rotate
vehicle by degree value in the direction of direction value, again go forward for two delay, again
rotate vehicle by degree value in the direction of direction value and again go forward for two delay
and rotate vehicle by degree value in the direction of negative of direction value and achieve the
axis.
6. Rotate the vehicle by degree value in the direction of direction value looking for free space.
7. Until direction value is180 degree goes to the 5th line.
8. If the degree value is 180 degree & still obstacle exists rotate the vehicle left by 90 degree i.e., turn
back the vehicle & go to the 1st line if not also go to the start.
This algorithm can be realized by the Fig. 8.
Fig. 8. Realization of the algorithm.
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5.2. Flow Chart
The program of this work had done in Keil uVision4 software. In the program the timer of the
microcontroller is used to generate the required time delay of 1.68 s. which is required to rotate the
robot 45degree each time. The microcontroller monitors the sonar data all time. If the sonar data is
1 microcontroller executes the avoiding algorithm and after successful avoidance it recovers the
direction displacement and acquires the axis of navigation. Fig. 9 depicts the flowchart.
Fig. 9. Obstacle avoidance & axis recovery flowchart.
Now when the obstacle is not exists in front of the trolley the microcontroller controls the motion of
the robot according to the four pins of port 2. Fig. 10 describes the whole thing. Fig. 11 describes the
real experimental situation of robot obstacle avoidance.
Fig. 10. Human control flowchart.
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Fig. 11. Real Situation.
6. The Results
6.1. Robot Simulation Results for Multiple Obstacle Avoidance
Fig 12 represents the five scenarios of Obstacle Avoidance. In these all experiments less than one
degree error occurs during ninety degree movement of the robot. This movement error can be
minimized by stepper motor actuator.
Fig. 12. Five scenarios of Multiple Obstacle Avoidance.
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7. Conclusion
The objective of this project is to design and implement Ultrasonic Obstruction Detection intelligence
for a guarded tele-operated trolley-service robot. As described in this report a system is developed that
can detect objects up to a distance of 2.5 to 3 feet and depending upon that sensors intelligence the
micro controller can take navigating command from human or from obstacle avoidance algorithm.
This system gives satisfactory result as requirement. With respect to the requirements for an ultrasonic
switch & the system the following can be concluded.





The system is able to generate 40 KHz continuous burst.
The system is able to detect objects within the sensing range.
This system has the capability to acquire its axis & direction after obstacle avoidance.
This trolley can navigate by human command through RF communication.
This system can also communicate with PC through printer port. A human can give command via
P.C.
References
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gleaning, in Proc. of the IEEE Int. Symposium on Intelligent Vehicles, 2000, pp. 602 -607.
[2]. A. Heale, L. Kleeman, Fast Target Classification Using Sonar, in Proc. of the IEEE/RSJ Jnf. Conf. on
Intelligent Robots and Systems, 2001, pp. 1446 -1451.
[3]. Tan Tiong Cheng and Muhammad Nasiruddin Mahyuddin, Implementation of Behaviour-Based Mobile
Robot for Obstacle Avoidance Using a Single Ultrasonic Sensor, in Proceedings of the Conference on
Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA 2009), 2009.
[4]. Zou Yi, Ho Yeong Khing, Chua Chin Seng, Zhou Xiao Wei, Multi-ultrasonic sensor fusion for mobile
robots, in Proc. of the IEEE Int. Symposium on Intelligent Vehicles, 2000.
[5]. Choon-Young Lee, Ho-Gun Choi, Jun-Sik Park, Keun-Young Park & Sang-Ryong Lee, Collision
Avoidance by the Fusion of Different Beam-width Ultrasonic Sensors, IEEE SENSORS 2007 Conference.
[6]. Yang Kai, Zhang Junmei, Li Wenbin, Yang Liu, Gao Lin, Xue Huixia, Design of Ultrasonic Obstacle
Avoidance System of Fruit-Transportation Gyro car Based on ARM, in Proc. of the 3rd IEEE International
Conference on Measuring Technology and Mechatronics Automation 2011.
[7]. Yin Mon Myint, Implementation of Guidance System in Modelled Autonomous Mobile Robot for Obstacle
Avoidance Behavior, in Proc. of the 2nd IEEE International Conference on Instrumentation Control and
Automation, 15-17 November 2011, Bandung, Indonesia.
[8]. Fairus M. A, Sy. Najib Sy. Salim, Irma Wani Jamaludin, M. Nizam Kamarudin, Development of an
Automatic Parallel Parking System for Nonholonomic Mobile Robot, in Proc. of the International
Conference on Electrical, Control and Computer Engineering, Pahang, Malaysia, June 21-22, 2011.
[9]. Kai-Tai Song, Chih-Hao Chen and Cheng-Hsien Chiu Huang, Design and Experimental Study of an
Ultrasonic Sensor System for Lateral Collision Avoidance at Low Speeds, IEEE Intelligent Vehicles
Symposium University of Parma, Parma, Italy, June, 2004.
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
A Real Time Radio Frequency Field Imaging
for Detection of Impurities in Liquids
Mohammad MEZAAEL
Department of Electrical and Computer Engineering,
Lawrence Technological University (LTU), Michigan, USA
E-mail: mmezaael@ltu.edu
Received: 16 June 2012 /Accepted: 17 September 2012 /Published: 28 September 2012
Abstract: The objective of this paper was to detect of impurities in liquid materials. The application of
the technique to the assessment of single object and the appropriate starting material is discussed.
Basic theory will be used to show how the high frequency field imaging is transformed into a time
varying charge distribution at three transducer faces. It will also be shown that this gives a critical
assessment of the factors limiting the performance of this transducer. The donor density is assumed to
be variable of the coordinates. This transducer will be used as a radio frequency photo sensor device in
an optically scanning system, to improve the quality of imaging. This invention can detect accurately
the impurity contents of unknown object in refined metallurgical Grease liquids (such as glycerol and
glycerin). A digital signal processing technique is used to improve the quality of image. A digital
correction technique will be shown to offer a simple and convention means of eliminating the effects
of system non-uniformities. This technique is simple, low-cost and suitable for industrial applications.
Copyright © 2012 IFSA.
Keywords: Image processing and remote sensing.
1. Introduction
A radio frequency photo sensor device package having improved for capable to detect the impurities in
liquids. A major part of this work is concerned the design and manufacture of the electronics radio
frequency drive and signal processing circuits suitable to give output signals for both qualitative visual
images and quantitative records of data obtained from typical samples. In addition, some mechanical
design and fabrication of a suitable specimen chamber will be required, including the provision of an
optical 2-D laser scanning source and electronic display. A D.C. reverse bias can be produced across
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the photo sensor device by applying a radio frequency. This has been observed to be roughly
proportional to the amplitude of the applied radio frequency signal.
In previous work we have designed a theoretical model of RFT at two dominations and assuming the
charge distribution (donor density) is constant. In this paper a large area of high resistivity Ge Schottky
photodiode is specially fabricated as a transducer, it is called the High Frequency Transducer (HFT).
The semiconductor is the substrate of the HFT and can be divided into two regions [3]. Region I is the
depletion region between (x=0, x=d, y=0, y=r). Region II is the neutral region between (x=d, x=d+t,
y=r, y=r+s) and both of them are modulated by resistance capacitance (RC) network. For the analysis
of the semiconductor layer (i.e. Ge of high resistivity) with a high frequency field (such as Radio
Frequency and Microwave) of some volts, the under depletion approximation charge distribution N is
assumed function of the coordinates (x, y). The potential distribution in the depletion region of
Schottky barrier junction depends upon its spatial frequency k, the applied voltage, and the Ge
resistivity and permittivity.
2. Theoretical Modelling
We would have to solve the second order partial differential equation in three dimensions given
appropriate boundary conditions. However, since any distribution of high frequency potential or field
in y-dimension can be synthesized by means of an infinite sum of terms of different spatial frequency
k= 2π/λs, we choose to find a specific solution for a single spatial frequency. The three dimensional
model shown in Fig. 1, will be used.
Fig. 1. The 3-D model of High Frequency sensor device.
The applicable equations for the two regions containing electric fields are Poisson’s equation [4, 5] for
the depletion region;
ΦD (x, y, z) = c f(x, y)
(1)
And Laplace’s equation [6] for the Neutral region:
ΦN (x, y z) = 0,
(2)
where фD and фN are the depletion and neutral potential respectively. A specific solution to equ.1 and
equ.2 can be obtained for a spatial frequency k respectively is:
фN(x, y, z) =C1 sinhk(x+y)/ √2 sinhkz + C2 (xy)2,
(3)
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where [0 < x < d, 0 < y < r], and
фN(x, y z) =C3 [exp(k(x+y) /2) + exp(–k(x+y) /2) ] Sinhkz + C5 xy + C 6,
(4)
where d < x < t+d, r< y < r+s.
C1, C2, C3 are the constants and C4, C5, C6 are the potential amplitudes. The following boundary
conditions are used to solve Eq.3 and Equ.4.
(i) ΦD (x, y, z) = 0, at x = 0, and y= 0.
(ii) ΦD (d, r, z) is continuous at x=d, y= r.
i.e. ΦD (d, r, z) = ФN (d, r, z)
(iii) The total current is continuous at x=d. and y = r. i.e. M dфD /dx = dфN /dx.
M
 D  j
,
 N  j
 D = The conductivity of the depletion region
 N = The conductivity of the neutral region
ε = The permittivity of Germanium material
(iv) ΦN(d, r z) = Vo+ VkSinkz, where x = d + t=d, and y = r + s = r
Vo = the constant potential uniform with respect to z
Vk = the external ac voltage produced by high frequency source with spatial frequency k.
From equ.1, equ.2, and relation (ii), we obtained;
C1 Sinhkl = C3 exp k l + C4 exp –k l
(5)
C2 l 2 = C5 l + C6
(6)
and
where
l = d + r / √2, l = rd / √2
From equ.1, equ.2, and relation (iii), we have;
MC1 Coshkl = C3 exp k l – C4 exp –k l
(7)
3MC2 l1 = C5
(8)
C3 exp (kl2) + C4 exp (-kl2) = Vk
(9)
and
From equ.2 and relation IV, we have
and
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Vo = C3 l3 + C6
where L2 = d+ s+ r+ t = l+ s+ t = (d1+ r1)/√2, l3= (d + t) (r + s) =d1r1
Solving the above equations, the constant values are evaluated. See Table 1. Substituting these values
in equ.1 and equ.2. We obtained the final solution.
Table 1. Constants.
C1= Vk /Sinhklcoshkm (1+M tanhkm cothkl)
C2= Vk [1+ ω (1- l1)] / l12 (1+ ω)]
Vk exp-kl (tanhkl+M)
C3 = -----------------------------------2 sinhkm (M+ tanhkl cothkm)
Vk expkl (tanhkl – M)
C4= --------------------------------Sinhkm (M+ tanhkl cothkm)
C5 = 2MVo l1 / (l1 + ω)
C6 = Vk [l1 (1-2M) + ω] / (l1 + ω)
The theoretical analysis described above has been used determine the behaviour of a high frequency
sensor of the type sketched in Fig. 1. Using the solution of Passion’s and Laplace’s equations on 3-D
has been found which permits reduction of the potential amplitude distribution as a function of the
spatial frequency and the characteristics of the photoconductivity semiconductor layer.
Fig. 2 shows the potential amplitude in the depletion region of the HFT as a function of the spatial
frequency k from this graph, it is apparent that the potential amplitude related to various parameters
such as Ge resistivity and high frequency as shown in Fig. 3, and Fig. 4, respectively.
These plots suggest that the highest potential amplitude is obtained when the Ge resistivity is high and
for operation at higher frequencies.
2. Experimental Set-Up
In this work, grease (glycerol, glycerin) were used as a dielectric liquids, the dielectric constants being
30 and 50 respectively. Fig. 5 has shown the electronic system and Fig. 6 shown the package, in which
the liquid is contained inside a rubber ring. The inner diameter of the ring is 2 cm and the outer
diameter 4 cm, with the thickness 2 mm. This ring is placed on top of the metal ground plate and the
grease was filling the interior area. A piece of polythene in the form of a triangle object (1/2 cm,
1/2 cm, 1/2 cm) and 1 mm in thickness is inserted in the grease as an impurity object. During the work
detailed calculations have been completed to determine the optimum frequency and drive level
required for the optical sensor device with typical values of conductivity and permittivity [3]. As a
result it has been decided that a signal frequency will be used initially, around 29.5 MHz for glycerol
and 12 MHz for glycerin, with an output level of about 10 volts peak into a load of 100 Ohms.
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Fig. 2. Te Variation of Φ with the Spatial frequency k at various High Frequencies.
Fig. 3. Variation of ΦD with Resistivity of Ge material at various High Frequencies.
Fig. 4. Variation of ΦD with High Frequency for different material resistivities.
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Using this assumption the design of the input stages of the signal processing circuits for the
photovoltaic output at 50 kHz has been initiated. The system is an ideal application for the use of a
look-in amplifier, since a reference signal at 50 kHz is used to modulate the scanned optical beam. The
modulated r.f. drive has been used in a place of the presently favored C.W. drive. The potential
advantage achievable from this approach would be a reduction in drive level, leading simple signal
processing and a lower requirement for r.f. drive power.
Fig. 5. The radio frequency field image system block diagram.
Fig. 6. Package of liquid and the optical sensor.
Fig. 7. The photograph of r.f image showing the impurity object.
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3. The Results and Discussions
Analog radio frequency image of the object are shown in the Fig. 8. Also the line scan and colour coded
images were shown in Fig. 9 and Fig. 10 respectively. The frequency was 29.5 MHz for glycerol and
12 MHz for glycerin and the light modulation frequency was 50 KHz. The useful outcome of this work is
to show the effect of varying dielectric constant on the enhancement of the photovoltaic output under the
influence of a radio frequency field. It was difficult to see a different between the glycerol and glycerin
radio frequency images because the very enhancement of photovoltaic signal has resulted in each case. The
difference was apparent in the gain, for example, at radio frequency potential or 20 volts across the glycerol
and glycerin packages. The photovoltaic gains were 28 dB and 25 dB respectively.
Fig. 8. The photograph of r.f image showing the impurity object.
Fig. 9. The colour coded r.f image of the impurity object.
Fig. 10. Line-scans before and after correction.
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The digital image correction technique [4, 5] has been used for removing the effects of the non-uniform
response of the sensors. Fig. 10 shows the amplitude of r.f image line scan (i.e. 33, 44) of the impurity
object and the corrected line after applied the correction technique.
References
[1]. Michael J. Haji-Sheikh, Gilbert Morales, Baki Altuncevahir, and Ali R. Koymen, Anisotropic
Magnetoresistive Model for Saturated Sensor Elements, IEEE Sensors Journal, Vol. 5, No. 6, December
2005.
[2]. Fabrizio De Nisi, Fiorenzo Comper, Lorenzo Gonzo, Massimo Gottardi, A CMOS Sensor Optimized for
Laser Spot-Position Detection, IEEE Sensors Journal, Vol. 5, No. 6, December 2005.
[3]. G. Wysocki, R. F. Curl, F. K. Tittel, R. Maulini, J. M. Bulliard, and J. Faist, Widely tunable mode-hop free
external cavity quantum cascade laser for high resolution spectroscopic applications, Appl. Phys. B, Vol. 81,
No. 6, Oct. 2005, pp. 769–777.
[4]. D. A. Thompson, L. T. Romankiew, and A. F. Mayadas, Thin film resistors in memory, storage and related
applications, IEEE Trans. Magn , Vol. MAG-11, No. 4, Jul. 1975, pp. 1039–1050.
[5]. José Gerardo Vieira da Rocha and Senentxu Lanceros-Mendez , 3-D Modeling of Scintillator-Based X-ray
Detectors, IEEE Sensors Journal, Vol. 6, No. 5, October 2006.
[6]. D. W. Liu, F. Iza and M. G. Kong, Electron heating in radio-frequency capacitively coupled atmosphericpressure plasmas, Applied Physics Letters, 93, 2008, 261503.
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Design and Simulation of a Microgripper with the Ability
of Releasing Nano Particles by Vibrating End-Effectors
Hamed Demaghsi, Hadi Mirzajani, Ehsan Atashzaban, Habib Badri Ghavifekr
Department of Electrical Engineering
Sahand University of Technology, Iran
E-mail: h_demaghsi@sut.ac.ir, h_Mirzajani@sut.ac.ir, e_atashzaban@sut.ac.ir, badri@sut.ac.ir
Received: 17 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: this paper investigates the design and simulation of a new type of microgrippers which is
able to release nano particles by vibration. After picking and transferring the object to the desirable
substrate electrothermally, an electrostatic oscillation system (comb-drive) generates vibration at the
gripper arms that facilitates the release process by taking advantage of inertial effects.
Copyright © 2012 IFSA.
Keywords: Micro electro mechanical systems (MEMS), Vibrating microgripper, Nanohandling, Active
release technique.
1. Introduction
Due to continuous progress in the field of microassembly, microgrippers have become inevitable
options for micromanipulation and nanohandling. High precision, robustness and reliability are
characteristics that enable microgripper to be employed in nanohandling to pick and place nano
particles especially nanotubes/wires/fibers.
Since microfabrication process is able to fabricate complex systems, researchers have employed
different actuation to build various microgrippers in science and industry. Electrostatic and
electrothermal are suitable actuations that have been used in most researches. Kim et al. [2] developed
a polysilicon electrostatic comb-drive microgripper. Anderson et al. [3] designed more mechanically
stable, electrothermal three beam microgripper with high gripping force to pick and place an as-grown
carbon nanotube. In addition to the three beam microgripper, Carlson et al. [4] investigated an
Asymmetric RibCage (ARC) gripper which was able to provide more gripping force than three beam
due to a rigid end effector.
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Since the handling objects are in the range of micrometer and nanometer, interactive forces such as
Van der Waals force, surface tension force and electrostatic force between micro/nano particles and
gripper surface become more dominant [5]. As a result, it is easy to pick up an object using adhesion
forces but the release process is very difficult [6].
To release objects rapidly and accurately, several strategies have been proposed in the past decade.
Aray et al. [5] analyzed the balance of the adhesion forces between the objects and proposed methods
to reduce adhesion forces based on the micro physics and also fabricated a gripper arm with rough
surface to overcome the adhesion. Kim et al. [7] coated the gripper arms with chemical materials to
facilitates the release process.
Generally there are two techniques for releasing process: passive release and active release [8].
Passive release method depends on the adhesion forces between the micro object and substrate to
detach the object from end-effector [8, 9].
Active release method is independent of the substrate and it detaches object from end-effector without
touching substrate. Brandon et al. [8] designed a novel electrostatic microgripper integrated with a
plunging system to impact micro object to gain sufficient momentum to overcome the adhesion force.
Vibration is a strategy to release an object. In fact, vibrating the end-effector generates enough inertial
force to overbalance the adhesion forces [6]. Sinan et al. [10] fabricated a gold coated silicon micro
beam to pick the micro object and employed vibrating the beam to overbalance adhesion to achieve the
release. Chen et al. [9] designed and fabricated a micro manipulation system including a MEMS-based
microgripper fixed on a PZT ceramic. The electrostatic microgripper was able to pick the micro object
and vibrate the end-effectors horizontally (in-planely) and PZT vibrated the microgripper vertically. So
the compound vibration takes the advantage of inertial effects to overcome adhesion forces.
In this paper, we investigate and design a microgripper that is able to grab and pick the nano objects
from the substrate electrothermally, transfer to the desirable substrate and release the object by
vibrating the end-effectors. An electrostatic comb drive system which oscillates at resonant frequency
along x-direction (in-planely) provides the vibration to detach the object from the end-effector. Fig. 1
shows the schematic drawing of the microgripper.
2. Design Consideration
2.1. Actuation
Chevron or V-shaped bent beam actuator requires low driving voltage, produces larger force and
generates large displacement through motion amplification [7], hence in this work is employed as an
actuator. The electric current that passes through the beams, generates heat due to resistive heating.
The thermal expansion of the beams causes that the apex to move downward (along x-direction)
considering that the beams are located between two anchor points. It results in closing the gap. Table 1
shows the microgripper dimensions.
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Fig. 1. Schematic diagram of microgripper:
a) Chevron actuator details; b) Comb-drive details; and c) End-effector details.
Table 1. Geometrical parameters used in this work.
Frame
Ɵ2 (°)
Ɵ3 (°)
g (мm)
le (мm)
Wf (мm)
W8 (мm)
W9 (мm)
W10 (мm)
h (мm)
10
30
1.4
140
8
2
2
1.1
3
Comb drive
Ls (мm)
ds (мm)
Wr (мm)
W7 (мm)
W3 (мm)
W4 (мm)
W5 (мm)
W6 (мm)
ncd
Chevron actuator
158
15
15
8
1
10
5
1.2
34
Ɵ1 (°)
Wch (мm)
Lch (мm)
W1 (мm)
W2 (мm)
Anc (мm)
t (мm)
d (мm)
nch
4
1.2
70
1.4
8
16
2.5
1
12
Flexure beam
Lsp (мm)
Wsp (мm)
anw (мm)
ans (мm)
-
45
2
10
20
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In Table 1 t is microgripper thickness, d is distance between microgripper and substrate, ncd is number
of comb fingers in each comb drive system and nch is number of chevron actuator bent beams.
Due to thermal conductivity of polysilicon is much larger than air and heat lost through radiation is
considerable at high temperature [11] , we neglect the heat dissipation through convection and
radiation. The material properties of poly silicon which are used in simulations are listed in Table 2.
Table 2. Material properties of polysilicon.
Material Properties
Young’s Modulus (GPa)
Poison Ratio
Electrical resistivity (Щ-m)
Thermal Conductivity (W/m.k)
Thermal Expansion Coefficient (1/k)
Density (Kg/m3)
Value
160
0.22
5.110-5
30
2.710-6
2300
References
[15]
[15]
[17]
[16]
[16]
[14]
Since high temperature is not sustainable for some nano particles [3], we try to keep temperature at
low values at the end effectors as possible. In this regard, we limit the temperature below 200 °C at the
middle of the chevron actuator (Fig. 2) because in this case the microgripper is capable of closing the
gap larger than 1 μm (g = 1.4 μm) and the temperature at the end-effector is below 170 °C.
Fig. 2. Thermal distirbution for case 2 (Ɵ2 = 10, Ɵ3 = 30). Tempreture is maximum
at the middle of the chevron actuator.
Vin = 1.4 V.
2.2. Frame
We employ the frame to amplificate and convert x-directional motion of the chevron actuator to
y-directional motion at the end-effectors. Frame needs to be mechanically strong to provide high
gripping force, on the other hand, it has to close the gap at the temperatures below 200 °C in the
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chevron actuator. Angles Ɵ2 and Ɵ3 play crucial roles in the frame stiffness. To find the desirable
frame, we investigate different angles (cases) effects on the frame stiffness by FEA simulation (Fig. 3).
Table 3, shows angles of each case.
Base point of Frame
Fig 3. Two crucial angles of the Frame.
Table 3. Angles of each case applied in simulations to find desirable frame.
Case1
Case2
Case3
Case4
Case5
Case6
Ɵ2 (degree) 10
10
10
10
0
5
Ɵ3 (degree)
45
30
15
0
30
30
In the simulations, the pressure is applied to the end-effectors and increased to the extent that the endeffectors start to move backward. Fig. 4 shows the end-effector deflection versus force for each case.
The slope of each curves indicates the compliance (the inverse of stiffness is compliance) of the frame
at each case.
Fig. 4. The end-effector deflection versus force for each case.
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Case 1 is the stiffest frame but it is not able to close the gap at temperature below 200 °C. The
minimum stiffness belongs to case 5 therefore it is able to generate low gripping force. Case 2 has
acceptable behavior because it is stiff enough to generate forces at the range of 1 μN and it can close
the gap whereas the temperature in the chevron actuator is less than 188 °C and at the end-effector is
less than 170 °C (see Fig. 2). Note that it is necessary for each gripper arm to deflect more than half of
the gap to close the gap firmly and produce gripping force [3]. Fig. 5 shows the end-effector deflection
for case 2.
Fig. 5. The end-effector deflection versus voltage for case 2. The numbers in the figure indicates the endeffectors temperature (°C).
Since the best amplification at the frame occurs when the base point of the frame is immovable, the
flexure beams are designed to avoid moving the base point of the frame largely along the x-direction at
the gripping phase (dimensions in Table 1).
3. Vibrator
At the release stage, the free end of object is placed on the substrate, if the adhesion between object
and substrate is larger than the adhesion between object and gripper surface, it is released. Otherwise,
by vibrating the end-effector, the adhesion force between particle and gripper arm decreases due to
inertial effects [9]. The interdigited-finger comb drive structure is one of the earliest surface
micromachined resonator design which commonly used in MEMS devices such as micro gyroscope,
micro accelerometer and resonators .As an example, Clark [12] designed and fabricated an interdigited
comb fingers that operated as a micromechanical resonator. When AC excitation voltage with
frequency close to the fundamental resonant frequency of the micro resonator was applied, the micro
resonator began to oscillate.
In this regard, we employ the interdigited comb drive system with two flexure beams that suspend the
shuttle 1 μm above the substrate and enable the shuttle to oscillate along x-direction parallel to
substrate. The frame amplificates and converts the shuttle oscillation to the vibration along y-direction
at the end-effectors.
By Modal FEA analysis, we are able to find the resonant frequency (Fr) in which the shuttle oscillates
along x-direction parallel to the substrate. In order to make oscillation, two voltage signals at this
frequency (Fr) are applied to the stators to excite the oscillation (the shuttle is ground). Fig. 6 shows
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these pulse signals. Additionally Fig. 7 indicates the boundary conditions at gripping stage and
vibrating stage. We assumed the substrate temperature is constant at 25 °C.
Fig. 6. The voltage signals applied to the stators, (Ts =1/Fr).
(a)
(b)
Fig. 7. Boundary conditions at a) gripping stage b) releasing stage.
The x-direction electrostatic force on each comb tooth is given by [2]:
,
(1)
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where ɛ0 is the air permittivity, t is the thickness of comb drive and w3 is the gap between stator teeth
and rotor teeth.
For precise investigation, one interdigited comb finger is simulated to estimate the electrostatic force
and compare the results with Equation 1.
Fig. 8 shows a cross-sectional view of the simulation. A spring is connected to the rotor for
electrostatic force estimation. It means by applying voltage across the rotor and stator, the electrostatic
force attracts the rotor toward the stator, it results the spring to elongate along x-direction. By
calculating the strain and stress of the spring, the force is estimated. Fig. 9 shows the comparison
between theoretical and simulation results.
Fig. 8. One interdigited comb finger electrostatic simulation at Vs =10 V.
Fig. 9. Electrostatic force for each comb finger.
Since the oscillation is parallel to the substrate, the sliding damping plays an important role. Sliding
damping factor is given by [13]:
,
(2)
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where A is the overlap area of the shuttle and the substrate, d is the gap between shuttle and the
substrate. Table 4 shows damping parameters briefly.
Table 4. Damping parameters.
1.8610-11*
6.710-2*
0.115
*
µ (MPa-s)
л (мm)
Cslide
Air viscosity
Gas (air) mean free path
Sliding damping factor
[1]
The effective viscosity is given by [13]:
(3)
where Kn is the Knudsen number, which is calculated by [13]:
(4)
Note that the sliding damping between the shuttle and substrate is much more than damping between
comb drive fingers, thus it is neglected.
At last, Harmonic FEA analysis is employed to estimate the end-effectors and oscillator (shuttle)
vibration amplitude at the resonant frequency. Table 5 shows value of resonant frequency and
vibration amplitude for each case. Harmonic simulation results for our choice (frame case 2) is in
Fig. 10.
Table 5. Resonant frequency and end-effector vibration amplitude at this frequency for each case.
Case1
Case2 Case3 Case4 Case5 Case6 Frequency
(kHz)
183
172
160
150
183
178
Amplitude of vibration
(nm)
11
18
22
25
15
17
4. Proposed Fabrication Process Flow
A common polysolicon surface micromachining with a photolithographic line width of 1μm is used to
fabricate the gripper. An outline of the proposed fabrication process with detailed information is given
in Fig. 11.
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Fig. 10. End-effector and shuttle vibration amplitude versus frequency for case 2.
Resonant frequency (Fr) = 172 kHz
Silicon
(a)
(b)
(c)
(d)
(e)
(f)
Silicon Nitride
Silicon Oxide
Polysilicon
Fig. 11. Fabrication process. At each part, left picture is cross-sectional view
and the right picture is general view.
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a) Silicon nitride thin film is deposited on top of the Si wafer using LPCVD.
b) Silicon oxide layer is deposited and patterned to form the anchors.
c) Polysilicon is deposited and patterned using photolithography and RIE.
d) Pilicon nitride thin film is deposited on both sides of the wafer using LPCVD.
e) The back side silicon nitride is patterned lithographically and used as an etch mask for anisotropic
potassium hydroxide etch of the silicon wafer carrier with the buried Sio2 as an etch stop.
f) The silicon nitride is removed in phosphorus acid and finally the microgripper structure is released
with hydrofluoric acid etch of the sio2 [3].
5. Conclusions
In this paper, we designed and simulated a microgripper that was able to grasp nano objects
electrothermally and release it by active release technique. Different cases (angles) for frame was
simulated to find desirable stiffness. So, the electrothermal chevron actuator with frame case 2
(Ɵ2=10, Ɵ3=30) showed appropriate functionality at the gripping stage. At this stage, chevron
actuator worked at Vin=1.4 V, while the temperature at the middle part of chevron actuator was less
than 188° C and at the end-effectors was less than 170° C. Each gripper arm deflection was
738 nanometers. At the release stage, we employed the interdigited com-drive system to make
oscillation and the frame converted it to vibration at the end-effectors. Modal and Harmonic FEA
simulation showed that the resonant frequency (Fr) for our choice (frame case 2) was 172 kHz and the
amplitude of vibration for the shuttle was 5.2 nm and for the end-effector was 17.2 nm.
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2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Linear Resistivity Response with Relative Humidity
of Gd Doped Magnesium Ferrite
Jyoti SHAH, Amish G. JOSHI and * R. K. KOTNALA
CSIR-National Physical Laboratory, Council of Scientific and Industrial Research
Dr. K.S. Krishnan Road New Delhi –110012, India
Tel.: 91-11-45608599, fax: 91-11-45609310
*
E-mail: rkkotnala@nplindia.org
Received: 26 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: In present work sintered samples of gadolinium doped magnesium ferrite has yielded a
linear response of resistivity for wide range of humidity. From 10 % to 95%RH change, a. c. resistivity
of 2 mol% Gd doped sample drops linearly from 3.39107 to 3.2105 Ω-cm at 1 kHz. Gd doping also
enhanced porosity. XPS shows enhancement in lateral oxygen peaks with Gd-doping in magnesium
ferrite. The response and recovery time observed for 30 - 70%RH range for 2 mol% Gd doped
magnesium ferrite are 140 s and 180 s respectively, which are excellent values for bulk humidity
sensor material. Copyright © 2012 IFSA.
Keywords: Humidity sensitivity, Resistivity, Porous microstructure, Gd-doped magnesium ferrite.
1. Introduction
Chemical reactivity and the physical interaction of solids with gases are influenced by porous
microstructure of the material [1]. As a result of physical interaction of material and gases electrical
parameter of the material varies. The change in electrical parameters of a material by exposure to
humidity is extremely sensitive to dopant and material microstructure [2, 3]. The localized electrostatic
field due to surface charges weakens the physisorbed H2O molecule bonding and dissociates water
molecules 106 times higher than water molecules [4]. The variation in electrical parameters of porous
material with humidity is a consequence of dissociation of water molecules. Metal oxides due to their
defective (surface charge, pores) structure show good sensitivity towards water vapors. Oxides like
TiO2 [5, 6] Fe2O3 [7-9] MgAl2O4 [10, 11] and MgFe2O4 [12, 13] exhibit fall in resistance when
exposed to water vapors at ambient temperature. However, only a few were found to be effective for
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
practical application due to their inherent drawbacks e.g. barely sensitive for a wide humidity range,
short lifetime, large humidity hysteresis, and slow response etc. According to Fleming’s model surface
charges generates localized electrostatic field that attract water molecules to physisorbed via
H-bonding [14]. Surface ions dissociate water molecules and form chemisorbed OH-layers. These
hydroxyl ions provide high electrostatic field and weaken the bonding of further water molecules. In
addition, porous microstructure allows free access for water vapors to interact with the pore wall.
Water vapors are dissociated as H+ and OH- ions on surface active sites forming chemisorbed OHlayers [15]. Chemisorbed hydroxyl ions around pore necks create high electrostatic field and these
capillary tubes exhibit lower-pressure hence water vapors get condensed inside them at higher
humidity [16]. Theoretically electrostatic interaction between water molecules and surfaces has been
also analyzed using topology [17]. With increasing humidity, physisorption of water molecules take
place over chemisorbed OH- layers. Protonic conduction happens in the physisorbed layers from one
water molecule to other hence further decreases the resistivity of the material [18, 19]. The schematic
for surface conduction mechanism on material surface is shown in Fig. 1. To obtain a linear response
of resistivity for wide range of humidity both surface charges for physisorption of water molecules and
porosity of the material should increase. The porosity of the metal oxide can be increased by adding
foreign elements and monitoring sintering temperature. In the present work to get linear resistivity
response for wide %RH range, 1 mol% and 2 mol% gadolinium oxide has been doped in MgFe2O4. A
small amount of rare earth oxide addition can modify both microstructure and the electrical resistivity
[20]. By Gd-doping, porosity, surface charge and base resistivity increased. A two order (107 to 105)
linear decrease in resistivity has been observed for a change of humidity from 10 % to 95 %RH for
2 mol% Gd doped sample. The surface composition and porosity of Gd added samples has been
investigated for enhancement of humidity sensitivity.
Fig. 1. Surface conduction mechanism steps, (a) Magnesium ferrite sample surface, Me (surface cations);
(b) Adosorption of water molecule; (c) Dissociation and chemisorption of water molecule; (d) Physisorption of
water molecules, protonic transportation through hydrogen bonding in physisorbed water molecules.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
2. Experimental
The samples were prepared with solid-state reaction method by mixing MgO and Fe2O3 in a molar
ratio 1:1. Gd2O3 was added at 1 and 2 mol% in prestine sample. The precursors were ground and
followed by prefiring at 800 oC in air for 8 h. Organic binder polyvinyl acetate (PVA) was added
0.01 % to form rectangular pellets of prefired powder at a pressure of 10 tons. Sintering of pellets was
carried out at 1050 oC with a heating/cooling rate 5 oC/min for 5 h in air. To carry out electrical
measurements the lateral edges of the rectangular pellets were silver pasted and cured at 350 oC for
1 h. Electrodes were soldered at these silvered edges. X-ray photoelectron spectroscopy measurements
have been carried out for the sample series, using a Perkin Elmer 1257 model. The average pore size
and grain size distribution were calculated by performing linear intercept method on SEM micrograph.
All resistivity humidity responses were carried out at 1 kHz by Fluke 81 50 MHz Function Generator.
One-volt a.c. was applied to avoid any polarization effect due to dipole moment of water molecules.
High resistivity was measured by Kiethley 6517A electrometer. For taking relative humidity versus ac
resistivity measurements a two pressure method based standard humidity generator (Thunder Scientific
2500 series) was used for 10-95 %RH change. The % RH accuracy of the generator is ±0.5 % with a
resolution of 0.02 %. Use of such standard humidity generator provides more precise measurements to
relative humidity compared to the saturated salt solutions. Samples response and recovery time were
determined at 25 °C for 30 to 70 % RH change.
The working principle of RH generator is based on a two vapor pressure technique:
% RH  Px Psat   100 ,
(1)
where Px is the partial water vapor pressure and Psat is the saturated water vapor pressure at a given
temperature.
The lattice parameter of the different samples was determined by the relation:
a  d  (h 2  k 2  l 2 )1 / 2
(2)
Here d is the lattice spacing and h, k, l are the miller indices.
Bulk Porosity of the samples was calculated by using the equation:
% P  1  d exp d x  100
(3)
where dx and dexp are the X-ray density and the experimental density of the samples.
The specific surface area of the particle was calculated by the formula
Asp  6 / D
(4)
where Asp is the specific surface area (m2/g) and ρ is the MgFe2O4 theoretical density (ρ=4.50 g/cm2)
and D is the particle diameter. All particles considered to be spherical shaped.
The electrical resistivity of the samples was calculated by using the relation:
 res  RA / L
(5)
Here R, A and L are the resistance, contact area and length of the rectangular pellets respectively.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
3. Results and Discussions
[311]
XRD peaks as shown in Fig. 2 confirm the spinel phase formation of MgFe2O4 [JCPDS Card
No. 36-0398]. By Gd-doping in pure sample XRD peaks slightly broaden. Broadening may arise due
to increase in oxygen vacancies defects [21]. No second phase peaks have been observed with Gddoping. This suggests that such a nominal amount of Gd has been incorporated in to spinel lattice. The
lattice parameters of MgFe2O4 decreased with Gd-doping.
20
30
40
50

[440]
[511]
[422]
[400]
Intensity (arb. unit)
[220]
MgFe2O4
1 mol% Gd2O3
2 mol% Gd2O3
60
70
Angle (2 )
Fig. 2. XRD for magnesium ferrite, 1 mol% Gd2O3, and 2 mol% Gd2O3 doped samples.
XPS survey spectra performed in the range of 0-1350 eV is shown in Fig. 3. Corrections due to
charging effects were taken care by using C(1 s) as an internal reference and the Fermi edge of a gold
sample. It is evident from survey scan spectra depicting its position as sharp peaks of C 1s (2845 eV),
O 1s (532 eV), Mg 1s (1304 eV), Gd 3d5/2 (1186 eV), Fe(2p3/2) and Fe(2p1/2) at 710.6 eV and
724.3 eV respectively. Auger peak of O (KLL) and Mg (KLL) were also observed. All XPS peaks are
very close to reported literature value [22]. To make out the effect of oxygen ions, deconvolution
performed on O (1s) core level spectra.
Fig. 3. XPS Survey scan spectra of MgFe2O4 with different Gd concentration.
Fig. 4(a) shows the deconvoluted spectra of oxygen that has two-peak structure. The first peak P1 is
characteristic peak of O2– ions of the lattice oxygen, while peak P2 denotes O (1 s) lateral structure.
This lateral peak corresponds to the ionization of weakly adsorbed species [23]. Further, it suggests
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
that the existence in the subsurface of oxygen ions that bear lower electron density than the O2– ions.
Normally, these oxide ions described as O– species or excess oxygen [24]. When Gd doping increased
the area ratio of these two peaks also changes. The variation of P1 and P2 with Gd concentration is
shown in Fig. 4(b). Hence X-ray photoelectron spectroscopy confirms enhancement in lateral oxygen
peak P2 by deconvoluting O(1 s) peak of the samples. Consequently, more active sites for water
molecule adsorption increases. It leads to enhancement of humidity response of Gd doped magnesium
ferrite. This effect is distinctly evident in resistivity variation with humidity.
Fig. 4(a). XPS deconvoluted spectra of O 1s core level.
Fig. 4(b). Variation of P1 and P2 as function of Gd2O3 concentration.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
The SEM micrographs of the different samples revealed the microstructure of the samples altered due
to the doping of gadolinium oxide as shown in Fig. 5. It is clearly observed in SEM micrograph a
porous microstructure with distributed interconnected pores. Pore size distribution, grain size
distribution was calculated by applying linear intercept method on SEM micrograph. It is clearly
visible from SEM images pore size distribution increased with Gd doping in MgFe2O4. This may be
due to creation of oxygen vacancies as observed by decrease in lattice oxygen XPS peaks and
broadening of XRD peaks.
(a)
(b)
(c)
Fig. 5. SEM pictures of (a) MgFe2O4, (b) 1 mol% Gd2O3, and (c) 2 mol% Gd2O3 doped samples.
Fig. 6 shows the resistivity response of three sample pellets with increasing relative humidity.
Resistivity of the sample was determined by using equation 5. Resistivity response of undoped
MgFe2O4 was unchanged up to 40 %RH then starts to drop linearly with increasing %RH. Slope of
resistivity for 1 mol% Gd doped sample slightly increased than undoped sample. 2 mol% Gd doped
sintered sample showed drastic linear drop in resistivity for the entire humidity range 10-95 %RH.
However linear resistivity response with humidity observed in case of nanostructure due to high
reactivity of nanoparticles [25]. The resistivity at 10%RH of the undoped sample 1.38107 Ω-cm
increased to 3.4107 Ω-cm for 2 mol% Gd doping. This may be due to increased porosity from 19 % to
36.6 % as observed [26, 27]. The percentage porosity was calculated by using equation 3.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
Fig. 6. Evolution of ρac as a function of relative humidity for the investigated sample series measured at 1kHz.
The linear dependence of resistivity with porosity in our samples observed is shown in Fig. 7.
Theoretical modeling on the effect of porosity on material constants has also been proposed [28].
Resistivity gradient of pure sample was 2104 Ω/%RH up to 40 %RH that exhibits less defective sites
for water molecules to dissociate. By 1 mol% Gd-doping resistivity response increased to
5.3104 Ω/%RH at lower humidity. With 2 mol% Gd-doping a large slope in resistivity
2.6105 Ω/%RH was observed up to 40 %RH. This increment in RH sensitivity reveals more defective
sites availability for hydroxide bonding. OH- ions at pore necks create high electrostatic field for
further water vapors to dissociate. It is also observed in XPS that lateral oxygen peaks increased by
Gd-doping due to more OH- adsorption.
The resistivity values of pure sample with Gd-doping reveals Gd3+ ion substituting Mg2+ ion hence
increasing the base resistivity. However, substitution of Mg2+ ion (0.78 Å) by Gd3+ ion (1.07 Å) due to
larger ionic radii does not allow but lower decomposition temperature of Gd2O3 (2330 oC) than MgO
(2800 oC) allows partial replacement of Mg2+ by Gd3+. Gd3+ ion occupies more spacious octahedral
position due to larger ionic radius. Mg ion placement to interstitial site by Gd ions makes magnesium
ferrite more sensitive towards bonding with OH- ions as Mg ions possess high affinity towards water
molecules. Thus surface cations and hydroxyl ions create high electrostatic field to weaken
physisorbed water molecules bonding and provide hydronium ions to conduct. Also continuous pores
connected through grain necks facilitate transportation of ions at low RH. Resistivity gradient
increased (2.48107 Ω/%RH) at high RH for 2 mol% Gd doped sample is observed. Macropore neck
provide high field to dissociate water molecules and large surface area to interact with pore walls that
can accommodate multilayer of water molecules. Protons (H+)/hydronium ions (H3O+) are the major
conduction carriers at higher RH [29]. Protonic conduction increases as physisorbed water vapor layer
increased. Porosity plays dominant role for multilayer adsorption of water vapors. All the samples
show almost linear resistivity response at higher humidity >40 %RH due to porosity of the compound
[30, 31]. Gd-doping in pure sample increased the porosity hence high drop in resistivity at high
humidity. Porosity increased due to gradients in chemical potential, of MgO, Fe2O3 and Gd2O3 as
different diffusion couples, the vacancy flux may result in a large local concentration of vacancies that
condense into pores especially at grain boundary. Researchers have frequently observed such porosity
[32].
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
Fig. 7. % Porosity vs. Resistivity ρac at 10%RH, curve for three compositions.
Magnesium ferrite exhibits anomalous oxygen defects along a grain boundary due to large activation
energy of grain boundary diffusion [33]. This suggests the increased OH- species other than lattice
oxygen due to dissociation of water molecule. This was confirmed by XPS data by increased peak area
of lateral oxygen P2 than lattice oxygen peak. It reveals increased number of pores exposed more grain
boundary oxygen vacancies. It ultimately enhances more active sites for water molecules adsorption.
The response time and recovery time was measured for 30 %RH to 70 %RH i.e. the mid range of
relative humidity. The time taken by the sample with changing humidity from 30 % to 70 %RH is the
response time and resistivity response for 70 % to 30 %RH is the recovery time. Practically time taken
to attain 90 % of the resistivity value at 70 %RH is considered to be the response time. The
response/recovery time observed for three samples is given in Table 1. The humidity response time
was observed a least of 140 s for 2 mol% Gd doped sample is quite good for ceramic humidity sensor.
It is due to the higher porosity providing larger surface area of the grains ultimately increasing
adsorption of water molecules in less time. Moreover, samples were also prepared with higher Gd
doping but the base resistance of the samples increased beyond 700 MΩ, and also showed deviation
from linearity. The stability of sample resistivity with time was also observed by measuring its
resistivity at different %RH for the period of twelve months and it was found only 0.1-1 % variation in
resistivity values. This durability test establishes Gd doped magnesium ferrite a potential candidate for
humidity sensor application.
Table 1. Structural parameters and response time of MgFe2O4-Gd2O3 samples.
Samples
MgFe2O4
1 mol%
Gd2O3 doped
2 mol% Gd2O3
doped
Lattice
Parameter
(Ǻ)
Bulk
Density
(g/cm3)
Total
Porosity
(%)
Average
Pore
Diameter
(m)
8.36
3.62
19.5
1.3
Specific
Surface
Area of
Particle
(m2/g)
0.44
8.35
3.43
22.4
1.7
0.54
170/195
8.33
2.85
36.6
2.4
0.69
140/180
Adsorption
/Desorption
Time (s)
180/200
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 143-152
4. Conclusions
Doping of 2 mol% Gd in magnesium ferrite drastically increased the porosity and active sites for
dissociation of physisorbed water molecules. Resistivity response of such Gd doped sample shows
linear behavior of resistivity for the entire humidity range (10-95 %RH). The minimum humidity
response/recovery time is observed 140/180 s for 2 mol% Gd doped sample. Therefore it is an
appropriate material for humidity sensor with fast response time and good stability.
Acknowledgement
The authors are grateful to the Director of “National Physical Laboratory” New Delhi for providing
constant encouragement, motivation and support to carry out this work.
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
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Quartz Crystal Microbalance DNA Based Biosensor
for the Detection of Brugia malayi
1
1
Thongchai KAEWPHINIT, 2 Somchai SANTIWATANAKUL,
3
Supatra AREEKIT and 4Kosum CHANSIRI
Graduate School of Srinakharinwirot University, Sukhumvit 23, Bangkok 10110, Thailand
Tel.: +66 22664-1000 ext 4619
2
Department of Pathology, Faculty of Medicine, Srinakharinwirot University,
Sukhumvit 23, Bangkok, 10110, Thailand
3
Innovative Learning Center, Srinakharinwirot University,
Sukhumvit 23, Bangkok, 10110, Thailand.
4
Department of Biochemistry, Faculty of Medicine, Srinakharinwirot University,
Sukhumvit 23, Bangkok, 10110, Thailand
Tel.: +66 2260-2122 ext 4605, fax: +66 2260-0125.
E-mails: tkaewphinit@yahoo.com, titi41@yahoo.com, jeedkha@hotmail.com, kchansiri@yahoo.com
Received: 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: This Lymphatic filariasis is the major public health problem caused by Brugia spp. by the
conventional methods as PCR cannot discriminate well, takes a few days or several hours. This study
demonstrates a sensitive and specific quartz crystal microbalance (QCM) biosensor combined with
polymerase chain reaction (PCR) for diagnosis of Brugia malayi. The procedure concludes: formation
of self-assembled monolayer (SAM) on gold quartz crystals surface, attachment of the avidin to
activated carboxyl groups by EDC/NHS, attachment of the biotin-modified probe to the avidin, and
hybridization of probe to the target PCR. The PCR-QCM biosensor system was more specific than a
PCR-gel electrophoresis assay in detecting the DNA of B. malayi and B. pahangi.
Copyright © 2012 IFSA.
Keywords: QCM, DNA immobilization sensing, Lymphatic filariasis, B. malayi.
1. Introduction
Lymphatic filariasis or elephantiasis is the major public health problem that infected over billion
people in over 80 countries. Among them, Brugia malayi is mainly distributed in Asia countries such
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as China, South Korea, Japan, India, Myanmar, Indonesia, Malaysia, Borneo islands, the Philippines,
and Thailand [1]. It has been infected in cats, dogs, monkeys and humans previously reported [1-3],
but B. pahangi as closely related species that can infect cats [2-6]. Therefore, these animal reservoirs
play an important role as the carrier diseases which can lead to the problem of eradication in endemic
area. However, differentiation of B. malayi and B. pahangi microfilaria by using traditional Giemsa
staining even though the technique is convenient and inexpensive cannot morphologically distinguish
between those two species. Acid phosphatase staining is sensitive, but it is not reproducible and the
procedure is complicated. As well, The PCR based methods is the high sensitivity and specificity but,
this method cannot recognize mixed infection between closely related species [7-9].
Recently, there has been an increasing interest real time quartz crystal microbalance QCM based
biosensor technology by this biosensor is one of the candidate devices of biosensor technology for
detection of DNA hybridization that is rapid and sensitive detection among them, especially QCM
based biosensor by using oligonucleotide hybridization detection method. The system using a QCM
based biosensor in a flow cell might be developed for automated or continuous operation. The
relationship between the oscillation frequency change of a quartz resonator in contact with liquid and
accumulated mass had first realized by Kanazawa and Gordon in 1985 [10] that derived a relationship
by expressing the change in oscillation frequency of a quartz crystal in contact with a fluid. This
biosensor has its own advantages that the detection method is label-free from radioactive or fluorescent
tags [11]. There are many reports about the development of QCM specific DNA-based biosensor for
detection many pathogenic bacteria in real time such as Staphylococcus epidermidis [12], Escherichia
coli [13], and Pseudomonas aeruginosa [14] by using of PCR for the preparation of bacterial target
DNA
This objective was study of the QCM based biosensor for rapid and specific detection of B. malayi.
This method consists of the quartz crystal which was immobilized by using three probes as biotinmodified oligonucleotides probe that were designed from HhaI repetitive region (HR) sequence
element specific for B. malayi. This study can be extended to develop the new method which is high
sensitivity, specificity, cheap, easy to use, and rapid for detection of B. malayi in many fields of work
in clinical diagnosis.
2. Materials and Methods
2.1. Chemicals and Reagents
The chemicals and reagents used in the study were included 98% sulfuric acid (Sigma-Aldrich, USA),
30 % hydrogen peroxide (Merck, Germany), sodium chloride (Merck, Germany), sodium phosphate
(Na2HPO4; Merck), ethanolamine (Fluka, Switzerland), ethylenediaminetetraacetic acid (EDTA;
Merck),
3-mercaptopropionic
acid
aqueous
solution
(MPA)
(Sigma,
USA),
1-ethyl-3(3-dimethylaminopropil) carbodiimide ethanolic solution (EDC) (Sigma, USA), Nhydroxysuccinimide aqueous solution (NHS) (Fluka, Switzerland), ethanolamine (Fluka, Switzerland),
and hydrogen chloride (HCl; Merck).
2.2. Fabrication of Quartz Crystal Surface in Stepwise
The quartz crystals were commercially available as 12 MHz, AT-cut type (diameter 8 mm) coated with
gold electrodes (diameter 4 mm) on both sides (Kyocera-Kinseki Co.,Ltd., Thailand). The gold
electrode surface was cleaned with hot piranha solution for 30 seconds. The crystals were thoroughly
washed with distilled water, air-dried, and immediately used. The initial resonance frequency ( f 0 ) was
recorded as the baseline. The cleaned quartz crystal was soaked in the optimal concentration of MPA
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for 1 hour, rinsed with absolute ethanol, washed with distilled water. To activate the monolayer,
100 mg/ml EDC/NHS was placed left to react on the surface of gold electrode to form MPA
monolayer for 30 minutes followed by water rinsing. After that added 0.1 mg/ml avidin in
immobilization buffer (300 mM NaCl, 20 mM Na2HPO4, 0.1 mM EDTA, pH 7.4) was placed on the
electrode surface for at least 1 hour before washing and then, the quartz sensing was exposed to a
1 mM ethanolamine for 30 minutes, rinsed with distilled water and immobilization buffer. The DNA
biotin-modified probes optimal detection as 1 μM (Kaewphinit et al. 2010) (5’-Biotin-TTTTTT ATG
ACA ACT CAA TAC TCG AC-3’) was placed over the gold electrode surface for 20 minutes prior to
washing with immobilization buffer, distilled water, resonance frequency ( f1 ) was recorded and kept
at 4 °C for use.
2.3. Hybridization in Liquid Phase
One face of the quartz crystal was exposed to a 50 µl flow-through chamber, which was connected to
an inlet and outlet-flow tube drove by the peristaltic pump (ISM 834, USA). The whole flow cell was
placed in a shielding box to avoid some environmental interference. The apparatus included a
peristaltic pump to assure a 50 µl/min constant flow of the solutions.
The procedure of liquid phase QCM sensor was began the study while the frequency of the system was
regular frequency shift within ±1 Hz. Initially, the hybridization on the surface by using the buffer for
30 seconds that the baseline as resonance frequency ( f1 ) of the probe immobilization prior to injection
of DNA target (complementraly DNA probe; 5’GTC GAG TAT TGA GTT GTC AT-3’) corresponded
to position of HhaI repetitive region of B. malayi to hybridized with probe for 5 minutes, then washed
to remove unbound and frequency shift ( f 2 ) can observe. After the flow DNA target to probe
hybridization on QCM sensor for 5 minutes. The frequency difference (Δf) between resonance
frequency of initial and final values was determined (Δf = frequency of the immobilization probe on
the quartz crystal ( f1 ) - frequency of the hybridization reaction ( f 2 ), with f1 > f 2 ). The frequency
shift ( f = f1 - f 2 ) was related to the amount of target DNA hybridized to the DNA biotin modified
probe immobilized on the quartz crystal surface [15].
2.4. Blood Samples
Blood samples were taken from two cats naturally infected with Brugia from B. malayi endemic area
of Narathiwas and B. pahangi non-endemic area, the Lad Krabang district of Bangkok, Thailand. The
samples were previously screened by using the traditional blood smear technique before undergoing
the parasite isolation.
2.5. Parasites Isolation
Five milliliters of microfilaria-infected blood were taken from a host and transferred to a test tube
containing 7 mg/ml of EDTA as an anticoagulant. The blood was diluted with an equal volume of
phosphate buffer saline (PBS), pH 7.0, and was filtered through a 5 µm polycarbonate membrane
(Millipore). Microfilarias were then resuspended in PBS and centrifuged at 5,000 rpm for 10 minutes
at 4 °C. The pellet was washed with PBS for three times prior to storage at –70 °C until use.
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2.6. PCR Amplification of DNA Target
The genomic DNA was extracted from filarial parasites using a genomic DNA Purification Kit (Gentra
Systems, USA). Isolation was performed according to the instruction manual provided by the
company. PCR amplification of the HR from purified parasite DNA was performed using primers,
BM1-5’ GCG CAT AAA TTC ATC AGC AA 3’ and BM2-5’ ATG ACA ACA CAA TAC ACG AC
3’ previously described by Chansiri [6]. All reactions were 25 µl volume containing 50 ng of genomic
DNA in 10x PCR buffer, 1 µM each of primers, 100 µM of dNTP, 1.5 mM MgCl2 and 1.5 units of
proof reading Taq DNA polymerase (Invitrogen). PCR was performed by using a DNA thermal
cycler (MJ Research PTC-200 Peltier thermal cycler) for 30 cycles. Each cycle consisted of
denaturation at 94 °C for 1 minute, annealing at 62 °C for 1 minute and extension at 72 °C for
1 minute. PCR amplicon was analyzed by electrophoresis in a 1.5% agarose gel at 110V for
approximately 30 minutes prior to staining in 0.5 µg/ml ethidium bromide solution and observation
under ultraviolet light. PCR fragment was eluted from the gel and purified using the QIAGEN
Purification system prior to hybridization with probe. Purification was performed according to the
instruction manual provided by the company.
2.7. Sensitivity of Detection
The amplified PCR DNA fragments were denatured at 95 °C for 2 minutes to generate single stranded
DNA. Then, the reactions were subsequently cooled at 0 °C 1 minute, the flow dilutions of DNA target
as 0, 0.05, 0.1, 0.2, 0.5, and 1 µg/ml onto the quartz crystal for hybridized with the probe for
5 minutes.
2.8. The Specificity of Detection
Three quartz crystals were hybridized with DNA positive of each target solution of B. malayi, B.
pahangi, and buffer in total 50 µl were separately added to each quartz crystal.
3. Results and Discussion
3.1. The Responses of Complementary DNA Target
QCM was applied for detection specific DNA target sequences of B. malayi in infectious lymphatic
filariasis’s disease. In the QCM based biosensor system for rapid detection DNA target could be
measured F in continuous online monitoring, the resonant frequency of quartz crystal decreased
relationship with the mass increased on the quartz crystal surface. The complementary DNA target
concentrations of 0.25, 0.50, 0.75, 1.00, 1.50, and 2.00 µM caused the frequency shift was decreased
from 20±4.36 to 80±5.1 Hz. The frequency shift of each reactions (n=3) was presented as mean ± S.D.
as shown in Fig. 1.
3.2. The Sensitivity of Detection
The genomic DNA of B. malayi and B. pahangi was extracted from sample as described in Materials
and Methods. Upon PCR amplification of HR region from genomic DNA, the 280 bp fragment was
obtained in Fig. 2. PCR fragment was eluted from the gel and purified using the QIAGEN
Purification system was denatured at 95 °C for 5 minutes prior to hybridize with DNA biotin-modified
probe. The PCR amplicons were diluted with hybridization buffer ranging from 0, 0.05, 0.1, 0.2, 0.5,
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and 1 µg/ml. All of these PCR amplicons were denatured by heating denaturation as following; heated
at 95 °C for 2 minutes, chilled in ice bath for 1 minute, and hybridization assayed on specific QCM
based biosensor immediately. The concentrations of amplified B. malayi DNA target was diluted from
by measuring quality PCR amplicons at A260 /A280 , the frequency shift decreased from 28±7.85,
74±7.51, 108±12.77, 166±10.30, and 167±14.34 Hz , respectively which the frequency shift of each
reactions (n=3) was presented as mean ± S.D. as shown in Fig. 3. However, the detection limit of this
system as 0.05 µg/ml when can be the detection of real DNA target.
Fig. 1. The frequency shift of hybridization relationship between concentration of complementary DNA
target and specific probe. Error bars indicate the standard deviation (n=3).
Fig. 2. Agarose gel electrophoresis pattern of HR region. The 280 bp in size were amplified from Brugia. Lane
M represents 100 bp ladder plus marker, Lanes 1 negative control, Lanes 2 represent PCR products of HR
region from B. malayi, Lanes 3 PCR products of HR region from B. pahangi.
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Fig. 3. The frequency shift of hybridization relationship between concentrations of DNA amplicon
and probe. Error bars indicate the standard deviation (n=3).
3.3. The Specificity of Detection
The specificity was detected by using B. malayi, B. pahangi and negative control. The hybridization
buffer was the negative control. All PCR products were hybridized with 1 µM of biotin-modified
probe for 5 minutes at room temperature. The frequency shift of each DNA target (n=3) of
denaturation methods was presented as mean ± S.D. as shown in Fig. 4. B. malayi gave frequency shift
with higher frequency shift than of B. pahangi by no cross hybridization but, gel electrophoresis
cannot differentiate B. malayi from B. pahangi.
Fig. 4. Specificity test of QCM based DNA biosensor system on DNA amplicons of B. malayi, B. pahangi,
and negative control as buffer. Error bars indicate the standard deviation (n = 3).
Basically, B. malayi and B. pahangi are genetically closely related. The sensitive PCR and gel
electrophoresis cannot differentiate these two species. Moreover, the carryover contamination could
affect the PCR amplification leading to the false-positive or false-negative interpretation. The use of
antibody is costly and may lead to false positive diagnosis so that the control group for each single test
is needed. Previously, the differentiation of these two filarial species was relied on nucleotide
comparison of ITS regions [7-9] using PCR-based methods. This work provides the possibility of
using QCM based DNA biosensor systems to detect rapid and specific of B. malayi DNA target
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amplification of HR region by B. malayi and B. pahangi were a few differences in nucleotide sequence
between the two Brugia species. The method demonstrated two step of the detection by biotinmodified probe immobilization via stepwise kept at 4 °C for use and DNA target hybridization to
probe via liquid phase as rapid detection for 5 minutes. This QCM based sensor system detection limit
as 0.05 µg/ml and was comparable to that of PCR-based detection. No cross hybridization was
observed when the closely related species, B. pahangi, was used. This indicated that the DNA-based
QCM could be applicable for detection of B. malayi in feline reservoirs and mosquito vectors where
the co-infection with B. pahangi could be observed. Hence, the success in eradication of lymphatic B.
malayi should rely on the controlling of transmission of the parasite. The powerful diagnostic tool such
as DNA-based QCM methods permitted the accurate identification of infection that was essential for
the rapid epidemiological assessment as well as genetic inspection of these two closely related species.
4. Conclusions
The QCM biosensor appears to be a suitable and convenient tool for monitoring hybridization of
complementary stands of oligonucleotides compared to other biosensor methods. The method
demonstrated the sensitivity and specificity of the detection. The sensitivity of limited detection of
genomic DNA as 0.05 μg/ml. The specificity of sensor can be tested with HhaI repetitive region gene
also for differentiation of B. malayi from B. pahagi by PCR-gel electrophoresis cannot detected. This
study will help the selection of gene which is more suitable for detection of B. malayi in lymphatic
filariasis.
Moreover, this biosensor system may be developed for diagnosis lymphatic filariasis in clinical
samples
Acknowledgements
This work was supported by Faculty of Medicine, Srinakharinwirot University.
References
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__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
Recent Advance in Antibody or Hapten Immobilization
Protocols of Electrochemical Immunosensor
for Detetion of Pesticide Residues
Ying ZHU, * Xia SUN, * Xiangyou WANG
School of Agriculture and Food Engineering, Shandong University of Technology,
No.12, Zhangzhou Road, Zibo 255049, Shandong Province, P.R. China
Tel.:+86-533-2786558, fax: +86-533-2786558
E-mail: sunxia2151@sina.com; wxy@sdut.edu.cn.
Received: 12 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Electrochemical immunosensors have been used to detect or quantify the specific pesticide
based on the binding biomolecules (Ab or hapten) onto the transducer surface to interact with the analyte
of target (hapten or Ab), resulting in a detectable signal. In terms of the development of electrochemical
immunosensor, the Ab/hapten immobilization onto a transducer or a support matrice is a key step in
optimizing the analytical performance, such as response, reproducibility, stability, selectivity and
regeneration. This paper presents an overview of electrochemical immunosensors for the detection of
pesticides residues and various immobilization protocols of Ab or hapten, such as physical adsorption,
covalent coupling, entrapment, oriented immobilization, avidin–biotin affinity reaction, self-assembled
monolayer, nanoparticles. Future prospects toward the immobilization protocols for the development of
electrochemical immunosensor are discussed. Copyright © 2012 IFSA.
Keywords: Electrochemical immunosensor, Pesticide residues, Immobilization protocols.
1. Introduction
Pesticides derived from synthetic chemicals are essential inputs in increasing agricultural production by
preventing control pest and crop losses before and after harvesting. One-third reduction in crop yield
would be happened if pesticides are not used against pest [1, 2].
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Depending upon the species of pest, these chemicals have been divided into groups (e.g., herbicides,
insecticides, fungicides, rodenticides, and nematocides). However, their indiscriminate use, apart from
being an operational hazard, is posing a serious threat to human health [3]. By transformation through
the food chain, their bio-accumulation in animal and human body and eventually show their adverse
effects, like: cancer, hormone disruption, birth defect and neurological effects [4]. Therefore, there is a
growing need to introduce and develop new, sensitive, reproducible and rapid methods for monitoring of
pesticide residues in agricultural products at trace levels.
Numerous analysis methods such as gas chromatography [5], high-performance liquid chromatography
[6], capillary electrophoresis [7], flow injection immunoanalysis [8-10] and fluorimetry [11] have been
developed for detection of pesticides residues. However, these methods have some drawbacks such as
poor selectivity, high cost, slow response, poor stability and time-consuming [12]. Moreover, they can
only be performed by highly trained technicians and are not convenient for on-site or in-field detection,
which limit their application for real-time detection.
In this respect, biosensors are potentially useful as suitable complementary tools for the real-time
detection of pesticides residues and have been an active research area for some years [13].
Enzyme-based biosensors for pesticide determination have caused public interest due to their reliability,
fast response, high sensitivity and selectivity. In recent years, enzyme-linked immunosorbent assays
(ELISA) have grown rapidly as tools for pesticide measurement [14-19]. However, false positive may
easily appear and this method also need some improvements (e.g. for continuous detection).
Many biosensors which are used for pesticide detection are based on the inhibition reaction or catalytic
activity of several enzymes in the presence of pesticides [20-22]. Enzyme-based biosensors (e.g.
acetylcholinesterase biosensor) for pesticide determination have been widely reported in the literature
[23-27]. Since a number of pesticides have a similar mode of action affecting the activity of the same
enzyme, most of enzyme-based biosensors are used for screening purposes and are unspecific for
individual pesticides. They can only detect total pesticide content and do not provide specific
information about a particular pesticide [28].
Immunosensors have been used to detect or quantify the specific pesticide based on the binding
interactions between immobilized biomolecules (Ab or hapten) on the transducer surface with the
analyte of interest (hapten or Ab), resulting in a detectable signal. The sensor system takes advantage of
the high selectivity provided by the molecular recognition characteristics of an Ab, which binds
reversibly with a specific hapten. In solution phase, Ab molecules interact specifically and reversibly
with a hapten to form an immune complex (Ab–hapten) according to the following equilibrium
equation:
Ka

Ab  hapten  Ab  hapten ,
Kd
where Ka and Kd are the rate constants for association and dissociation, respectively [29]. They appear to
be appropriate for identification of a single pesticide or, in some cases, small groups of similar pesticides
in environmental monitoring as they are rapid, specific, sensitive and cost-effective analytical devices
[30]. Currently, many electrochemical, optical and piezoelectric immunosensors have been developed
for pesticides detection [28]. Among them, electrochemical immunosensors have received increasing
attention due to their lower cost, high sensitivity, simple instrumentation, and easy signal amplification
[31]. Excellent reviews that focused on electrochemical immunosensors for detection of different
pesticide molecules have been reported [32, 33].
However, there is a time gap between current status in the field and the most recent reviews. Thus, in this
review, we specifically provide an overview of the research carried out during the last 5 years relative to
electrocheimical immunosensor for pesticide residues detection. We will review several types of
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electrochemical immunosensors developed for their applications in pesticide analysis, various
immobilization protocols used for formation of a biorecognition interface. We also will discuss the
trends and challenges associated with designing a reliable immunosensor for practical applications in
detail.
2. Electrochemical Immunosensors
Formation of Ab–Ag complex in electrochemical transducers alters the change in ion concentration or
electron density on the electrode surface, which, in turn, is measured by electrodes. Electrochemical
transducers, classified as amperometric, potentiometric, conductimetric, capacitative and Impedimetric
measure changes in current, potential (voltage), conductance, capacitance and impedance respectively
[34-36]. Depending on if labels are used or not, immunosensors are divided into two categories: labeled
type and label-free type.
Electrochemical immunosensors could be competitive and revolutionize analysis, because of their
simplicity, rapidity and cheap technology. For pesticide detection, most of them use impedance or
amperometry in label or label-free format and label-free format is a tendency. Some examples of
electrochemical immunosensors for the detection of pesticides residues are presented in Table 1.
3. Immobilization Protocols
In terms of the development of electrochemical immunosensor, the Ab/hapten immobilization onto a
transducer or a support matrice is a key step in optimizing the analytical performance, such as response,
reproducibility, stability, selectivity and regeneration. A good immobilization method should meet the
following requirements: (1) be simple and fast; (2) produce immobilized reagents that are stable and do
not leach from the substrate; and (3) maintains its biological integrity flexibility, and proper active site
orientation toward the bulk solution. Thereby, Ab/hapten immobilization has been a critical issue in
immunosensor technology [54-58].
Ab/hapten immobilization consists of physical adsorption and chemical binding, which depends on the
driving force [59]. In general, they mostly fall into following methodologies.
3.1. Physical Adsorption
Physical adsorption is generally based on interactions such as van der Waals forces, electrostatic
interactions and hydrophobic interactions between the Ab/hapten and the transducer. Physical
adsorption is simple and easy, but nonspecific attractive forces easily causes Ab/hapten desorption [59].
In addition, the immobilized Ab/hapten can be susceptible to the reduction of biological activity by an
inappropriate orientation caused by physical adsorption [60]. This leads to the limitation that the sensing
elements have decreasing response with time and, thus, short life-times.
Gobi et al., created a functional sensing surface of the immunosensor by immobilizing an ovalbumin
conjugate of 2,4-D (2,4-D-OVA) by simple physical adsorption on a thin-film gold chip. It has been
established that the Au surface of the sensor chip was completely covered by 2,4-D-OVA up to a
monomolecular layer and that the 2,4-D-OVA immobilized sensor chip was highly resistive to
non-specific binding of proteins [61].
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3.2. Covalent Coupling
More specific and stronger attachment of Ab/hapten can be obtained by covalent modification through
formation of a stable covalent bond between functional groups of Ab/hapten and the transducer.
Covalent modification requires a bifunctional cross-linker, which has one functional group that reacts
with a base support, and another group that interacts with an active group of Ab/hapten [62, 63, 57]. The
procedure provides increased stability of the Ab. However, the immobilization by covalent coupling
may results in the random orientation of Ab/hapten, decreases the activity of Ab/hapten and is generally
poorly reproducible due to the chemical modification of critical residues and random protein
orientations [64]. In addition, blocking steps are usually necessary to limit the nonspecific binding. BSA
GNPs: colloidal gold nanoparticles; TU: thiourea; GCE: glassy carbon electrode; SiSG: silica sol-gel;
HRP: Horseradish peroxidase; DpAu: deposited gold nanocrystals; PA: staphylo-coccal protein A;
DMDPSE: 4,4’-thiobisbenzenethiol; GA: glutaraldehyde; ISFET: ion-selective field effect transistor;
SPE: screen-printed electrodes; PB: Prussian blue; IDμE: interdigitated microelectrodes; PANI:
polymer
polyaniline;
PVSA:
poly(vinylsulphonic
acid);
biotinyl-PE:
phospholipid
dipalmitoyl-sn-glycero-3-phospho-ethanolamine-N-(biotinyl); ProtA-GEB: Protien A-graphite-epoxy
biocomposite;
Poly(JUG-HATZ):
poly[N-(6-(4-hydroxy-6-isopropylamino-1,3,5-triazin-2
-ylamino)hexyl)5-hydroxy-1,4-naphthoquinone-3-propionamide] and tween is usually used as the
appropriate material to block nonspecific binding sites.
An example of where this approach has been exploited is that the Ab immobilization was carried out by
using carboxylic groups activated with EDC/NHS as a cross-linker to connect the NH2- group of the
antibody with the surface of carboxylized transducer (Fig. 1).
Fig. 1. Covalent immoblization of antibody onto carboxylized transducer activated
with EDC/NHS as a cross-linker.
By using 2,4-D immobilized through its carboxylic group covalently to the silanized surface of the gold
working electrode, Kalib et al., developed a disposable immunochemical biosensor for the herbicide
2,4-dichlorophenoxyacetic acid (2,4-D), with a detection limit close to 0.1 μg/L of free 2,4-D. For
covanlent immobilizations, the 2,4-D molecule was activated by isobutyl chloroformate and then it was
linked to the free amino group, which was obtained: (1) directly from the APTS moledule, (2) using
gultaraldehyde/hexametyhlenediamine spacer, (3) using glutaraldehyde/albumin spacer [65].
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Table 1. Some examples of electrochemical immunosensors for the detection of pesticides residues.
Pesticide
Detector
Label
Carbofuran
Amperometric
free
Carbofuran
Amperometric
free
Carbofuran
Amperometric
free
Carbofuran
Amperometric
free
Carbofuran
Amperometric
free
2,4-D
Potentiometric
HRP
Simazine
Potentiometric
HRP
Diuron
Impedimetric
free
Diuron
Amperometric
free
Paraoxon
Amperometric
free
Picloram
Amperometric
HRP
Atrazine
Conductimetric
GNPs
Atrazine
Amperometric
HRP
Atrazine
Amperometric
HRP
Atrazine
Impedimetric
free
Atrazine
Impedimetric
free
Atrazine
Amperometric
HRP
Atrazine
Impedimetric
free
Atrazine
Impedimetric
free
Atrazine
Impedimetric
free
Atrazine
Amperometric
free
Electrode Modification
carbofuran/BSA/Ab/GNPs/
TU/GNPs/GCE
carbofuran/BSA/Ab/SiSG/GC
E
carbofuran/HRP/Ab/GNPs/
L-cysteine/Au electrode
carbofuran/BSA/Ab/PA/DpA
u/Au electrode
carbofuran/BSA/Ab/
{DpAu/DMDPSE}2/Au
electrode
2,4-D-HRP/Ab/GA/Graphite
electrode
simazine-HRP/
glycine/Ab/PA/ GA/ISFET
diuron/Ab/GNPs/SPE
Ab/DCPU-BSA/PB-GNP/LCLAGE
paraoxon/Ab/NafionGNPs/GCE
HRP-G, anti-RIgG/picloram/
Ab/BSA-picloram/GNPs/
GCE
Ab2/Ab1/atrazine/ GPTS/
N-acetylcysteamine/IDμE
atrazine/atrazine–BSA/Ab/
immobilon membrane/H2O2
electrode
atrazine/atrazine-HRP
scAb/PANI/ PVSA/SPE
atrazine/bio-Fab/ neutravidin/
Gold/MHDA+biotinyl-PE
atrazine/bio-Fab K47/
BSA/PPy/ neutravidin/Au
electrode
atrazine-HRP/Ab/ ProtA-GEB
Atrazine/BSA/
histidine-Ab/poly NTA
-Cu2+/Au electrode
Ab11/antigen2d-BSA/3-(glyci
doxypropy)trimethoxysilane/
N-acetylcysteamine/IDμE
Atrazine/BSA/
histidine-Ab/poly NTA
-Cu2+/Au electrode
ATZ/α-ATZ/poly
(JUG-HATZ)/GCE
Detection
limit
Sample
Assay
time
Reference
0.11 ng/mL
cabbage
15 min
[37]
0.33 ng/mL
cabbage
lettuce
20 min
[38]
40 ng/mL
-
12 min
[39]
15 min
[31]
40 min
[40]
0.192ng/mL
0.06 ng/mL
Chinese chive,
celery, cabbage
Lettuce,
cabbage,
pepper, tomatoe,
chive,
strawberry
40 ng/mL
water, serum
12 min
[41]
1.25 ng/mL
-
50 min
[42]
5.46 ng/mL
water
-
[43]
1 ppt
-
-
[33]
12 ng/mL
aqueous
samples
20 min
[44]
0.5 ng/mL
peach
-
[45]
0.1 ng/mL
buffers
-
[46]
5.0× 10-11M
buffalo milk,
vegetal samples
15 min
[47]
0.1 ng/mL
-
-
[48]
20 ng/mL
PBS (pH 7)
-
[49]
0.1 ng/mL
PBS (pH 7)
-
[50]
6 μg/mL
orange juices
-
[51]
10 pg/mL
PBS (pH 7)
-
[52]
0.19 μg/mL
red wine
-
[32]
10 pg/mL
PBS (pH 7)
-
[52]
0.2 ng/L
-
-
[53]
Another example is that Ramon-Azcon et al., developed a novel impedimetric immunosensor based on
an array of interdigitated μ-electrodes (IDμE) and immunoreagents specifically developed to detect
atrazin. In this study, an atrazine-haptenized protein was covalently immobilized on the surface of the
IDμE area (interdigits space) previously activated with (3-glycidoxypropyl)trimethoxysilane. With this
configuration, the immunosensor detects atrazine with a limit of 0.19 μ/gL in red wine, far below the
Maximum Residue Level (MRL) established by EC for residues of this herbicide in wine (Fig. 2) [32].
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Fig. 2. Scheme showing steps used to prepare the immunosensor surfaces and antibody binding:
(a) IDμE, (b) step I: N-acetylcysteamine, gold protection, (c) step II: functionalization of Pyrex substrate
with (3-glycidoxypropyl)trimethoxy-silane, (d) step III: coating antigen 2d-BSA, covalent immobilization
and (e) step IV: antibody Ab11.
Valera et al., has designed and developed a novel conductimetric immunosensor for atrazine detection
using covalent immobilization of the competitor antigen which was performed on the interdigitated
μ-electrodes surface via the side chain amino groups of lysines or arginines with the epoxy groups on the
device surface. The immunosensor developed detects atrazine with limits of detection in the order of
0.1–1 μg/mL (Fig. 3) [46].
Using this technique, Valera et al., have developed a simple and low-cost method for the fabrication of
mechanically flexible interdigitated μ-electrodes (FIDμEs) for the development of a conductimetric
immunosensor for atrazine detection recently [66].
3.3. Entrapment
In an encapsulation method, the reagent is physically trapped within a porous matrix. It is simple and
compatible with various reagents. The reagents trapped in the matrix usually do not leach out or leach
out very slowly when an appropriate entrapment procedure is used. It appears that the encapsulation
method avoids the disadvantages and combines the advantages of the first two methods. Organic
polymeric matrices have been widely used for entrapment of sensing agents [67].
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Fig. 3. Schematic diagram of the complete assay system performed on the IDμEs: Step I, protection of
interdigitated μ-electrodes with N-acetylcysteamine; Step II, immunosensor surface functionalization with GPTS;
Step III, covalent immobilization of the antigen on the IDμE; Step IV, specific primary antibody (Ab1) capture in
the competition step; Step V, secondary labelled with gold antibody (Ab2) capture. In the Step IV, an amount of
the specific antibody (Ab1) is bounded on the coated antigen layer, whereas other amount is evacuated of the
IDμEs, this amount is related to the atrazine concentration. In the Step V, an amount of the secondary antibody
(Ab2) is bounded on the specific antibodies.
3.3.1. Sol-gel Entrapment
Recent development in the area of electrochemical immunosensors with sol-gel encapsulation of
Ab/hapten as an immobilization matrix is very encouraging and offers potential advantages. These
advantages include the ability of sol-gel (1) to form at low temperatures and under chemical, mechanical
stability and offers negligible swelling, (2) open to a wide variety of chemical modifications based on
the inclusion of various polymer additives, redox modifiers and organically modified silanes, resulting
in electrically conducting materials and (3) to exhibit tunable pore size and pore distribution, which
allows small molecules and ions to diffuse into the matrix while larger biomolecules remain trapped in
the pores, simplicity of preparation without any kinds of modifications [54].
Although it has many advantages over other methods, the sol-gel method has some disadvantages: low
response (as long as several minutes) in aqueous media and slightly change biological activities due to
reduced degree of freedom in the pores and/or interactions with the inner surface of the pores [54].
Turniansky et al., report the successful entrapment of an anti-atrazine antibody in a SiO2 sol-gel matrix,
retaining its ability to bind antigen from aqueous solutions based methods for monitoring pesticide
residues and other organo-synthetic environmental contaminants. Under appropriate sol-gel-forming
conditions, high amounts of atrazine were bound to the sol-gels, ranging between 60 % and 91 % of the
amount applied to the column. The combination of the properties of the sol-gel matrix (e.g., stability,
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inertness, high porosity, high surface area and optical clarity), together with the selectivity and
sensitivity of the antibodies, enable extension of this feasibility study to development of a novel group of
immunosensors which could be used for purification, concentration and monitoring of a variety of
residues from different sources [68].
Sun et al., developed a novel label-free impedance immunosensor for the direct detection of carbofuran
using silica sol–gel (SiSG) as immobilizing agent. Sol–gel technology provides a unique means to
prepare a three-dimensional network suited for the encapsulation of a variety of Ab [38].
3.3.2. Electrically Conducting Polymers Entrapment
The electrically conducting polymers (CP) are known to possess numerous features, which allow them
to act as excellent materials for immobilization of biomolecules and rapid electron transfer for the
fabrication of efficient biosensors [69].
Recently, CP, such as polyaniline(PANI) etc, has captured attention of scientific community due to its
applications including those in biosensors because of a number of useful features such as 1) direct and
easy deposition on the sensor electrode, 2) control of thickness, 3) redox conductivity and
polyelectrolyte characteristics, 4) high surface area, 5) chemical specificities, 6) long term
environmental stability and 7) tuneable properties. Fig. 4 is the 3D and 2D structure of PANI [70].
Fig. 4. (A) 3D and (B) 2D structure polyaniline.
A new electropolymerizable monomer, [N-(6-(4-hydroxy-6-isopropyl-amino-1,3,5-triazin-2-ylamino)
hexyl)5-hydroxy-1,4-naphthoquinone-3-propionamide], has been designed for use in a label-free
electrochemical immunosensor when polymerized on an electrode and coupled with a monoclonal
anti-atrazine antibody for the detection of atrazine (Fig. 5). This monomer contains three functional
groups: hydroxyl group for electropolymerization, quinone group for its transduction capability, and
hydroxyatrazine as bio-receptor element. This constitutes a direct, label-free and signal-on electrochemical immunosensorwith a very low detection limit of 0.2 ng/L, one of the lowest reported for such
immunosensors [53].
Ionescu et al., reported a label-free impedimetric immunosensor for the determination of atrazine, based
on a poly(pyrrole-nitrilotriacetic acid) (poly NTA) film and combined with an impedimetric detection of
atrazine without reagent and label. The poly NTA film constituted a convenient tool for the easy
anchoring of histidine-labelled antibody directed against atrazine, allowing the detection of extremely
low atrazine concentration namely 10 pg/mL [52].
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Fig. 5. Strategy for the electrochemical detection of atrazine based on the change in electroactivity of polymer
film, poly(JUG-HATZ). SWV recorded with (1) poly(JUG-HATZ)-modified electrode; (2) after complexation
with α-ATZ, poly(JUG-HATZ/α-ATZ)-modified electrode; (3) after addition of ATZ in solution.
Grennan et al., described the development of an electrochemical immunosensor for the analysis of
atrazine using recombinant single-chain antibody (scAb) fragments. The sensors are based on carbon
paste
screen-printed
electrodes
incorporating
the
conducting
polymer
polyaniline
(PANI)/poly(vinylsulphonic acid) (PVSA), which enables direct mediatorless coupling to take place
between the redox centres of antigen-labelled horseradish peroxidase (HRP) and the electrode surface
(Fig. 6) [48].
Fig. 6. Schematic diagram of the electrochemical real-time sensing process for atrazine detection.
3.4. Oriented Immobilization
Antibodies immobilized by these methods such as physical adsorption or covalent coupling, however,
often suffer from reduced hapten binding ability due to a combination of denaturation, random
orientation, and chemical modification of the antibodies [71]. Antibody-binding proteins (protein A, G,
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A/G, and L) have been widely used to overcome the aforementioned drawbacks [72, 73]. These proteins
specifically bind the Fc region of an Ab and, thus, properly orient the bound Ab for optimal hapten
binding [74]. Moreover, because the antibody-binding proteins capture antibodies without any chemical
modifications, bound antibodies fully retain their function. Improved surface orientation of these
engineered antibody-binding proteins enhanced the subsequent Ab/hapten immobilization. Despite the
evident advantages of using antibody-binding proteins for Ab immobilization, these proteins have
limitations; for example, they are susceptible to denaturation and are difficult to use in site-specific
modifications [75]. This technology has been widely used in electrochemical immunosensors through
the antibody's oriented immobilization for pesticides detection.
An immunological reaction for the detection of atrazine performed on the Protien A
(2%)-graphite-epoxy biocomposite (ProtA-GEB) biosensors is based on the antibody bonding through
Fc fragment to Protein A and a direct competitive assay using atrazine-HRP tracer as the enzymatic label
(Fig. 7). The electrochemical detection is thus achieved through a suitable substrate and a mediator for
the enzyme HRP. The detection limit for atrazine in orange juices was found to be 6μg/mL [51].
Fig. 7. The immobilization of anti-atrazine antibodies on the surface of the electrochemical transducer for the
detection of atrazine in orange juice with ProtA-GEB-based electrochemical immunosensors (A) and the
competitive immunological reaction (B).
Recently, Sun et al., introduced a strategy for preparing a new label-free amperometric immunosensor,
which successfully immobilized the anti-carbofuran antibody on the PA/DpAu modified electrode
surface for the detection of carbofuran. Due to PA’s specially binding ability of the Fc fragment of the
antibody molecules, the application of PA improves the capacity of antibody, thus enhance the detection
sensitivity. With this strategy, a detection limit of 0.1924 ng/mL was achieved for carbofuran (Fig. 8)
[31].
3.5. Avidin–biotin Affinity Reaction
One of the most valuable strategies for t he effective immobilization of biomaterial on different substrate
s is based on the avidin–biotin affinity reaction [76].This interaction is highly resistant to a wide range of
chemical (detergents, protein denaturants), pH range variations and high temperatures [77]. In addition,
the avidin–biotin based immobilization method maintains the biological activity of the biomolecule
being immobilized more successfully than other commonly used methods [78]. Ab/hapten can be readily
linked to biotin without serious effects on their biological, chemical or physical properties. In particular,
the extremely specific and high affinity interaction between the biotinylated antibodies and avidin (Ka
1015M−1) leads to strong associations similar to the formation of a covalent bonding.
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Fig. 8. Fabrication process of the stepwise Amperometric immunosensor based on a protein A/deposited gold
nanocrystals modified electrode for carbofuran detection.
This technique was used to immobilize anti-atrazine antibodies on the surface of avidin-graphite-epoxy
biocomposite based (Av-GEB-based) electrochemical transducer for the detection of atrazine in orange
juice (Fig. 9) [79].
Fig. 9. The immobilization of anti-atrazine antibodies on the surface of the electrochemical transducer for the
detection of atrazine in orange juice with Av-GEB-based electrochemical immunosensors (A) and the competitive
immunological reaction (B).
Another example of this technique is to attach the biotinylated anti-triazine Fab fragment to the
polypyrrole (PPy)/ neutravidin modified electrode throughout the well-studied biotin–neutravidin
interaction for the detection of atrazine. The immunosensor was very sensitive to atrazine antigen in the
range of 0.1–200 ng/ml and the detection limit attained 0.1 ng/ml (Fig. 10) [50].
3.6. Self-assembled Monolayer (SAM)
Self-assembled monolayers (SAMs) have aroused much interest due to their potential applications in
biosensors, biomolecular electronics and nanotechnology. This has been largely attributed to their
inherent ordered arrangement and controllable properties. SAMs can be formed by chemisorption of
organic molecules containing groups like thiols, disulphides, amines, acids or silanes, on desired
surfaces to fabricate immunosensors [80].
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Fig. 10. Schematic representation of the immunosensor architecture based on the immobilization of biotinylated
Fab fragment through the interaction biotin–neutravidin within the electro-generated polypyrrole for the detection
of atrazine.
The stability, uniform surface structure and relative ease of varying thickness of a SAM make it suitable
for development of biosensors. And the immobilization of biomolecules on a SAM requires very small
amount and desired analytes can be easily detected via various transduction modes. The use of an
appropriate SAM helps in oriented and controlled immobilization of biomolecules [81-83]. SAMs can
be used to prevent protein denaturation at an electrode surface and for enhancing stability of
biomolecules[84, 85].
A novel label-free amperometric immunosensor for the detection of carbofuran residues was developed
based on immobilization anti-carbofuran antibody on deposited gold nanocrystals
(DpAu)/4,4'-thiobisbenzenethiol (DMDPSE) multilayers (｛DpAu/DMDPSE｝n) through Au-S bond by
layer-by-layer self-assembly technology. Compared with a separate layer of DpAu/DMDPSE, the
presence of the multiple membranes not only promoted electron-transfer reactions, but also increased
the surface area to capture a large amount of antibodies, thus increased detection sensitivity with a
detection limit of 0.06 ng/mL (Fig. 11) [40].
Recently, describes the development of an electrochemical immunosensor for the analysis of atrazine
associated to biotinylated-Fab fragment K47 antibody. The sensors are based on mixed self-assembled
monolayer consisting of 1,2 dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-(biotinyl) (biotinyl-PE)
and 16-mercapto-hexadecanoic acid (MHDA). The tethered neutravidin was used the biotin sites present
in the mixed monolayer, with those associated to the biotinyl-Fab fragment K47 antibody (Fig. 12) [49].
3.7. Nanoparticles
In addition to these conventional methods, new materials such as nanoparticles have been employed in
immobilizing Ab/ hapten when constructing immunosensors. Gold nanoparticles (GNPs) have been
widely used for immobilization of biomolecules due to their large specific surface area, high surface free
energy and biocompatibility. GNPs can adsorb biomolecules and play an important role in the
immobilization of biomolecules for biosensor construction [86]. So far, GNPs have been widely applied
in the biosensors for detection of pesticide residues [87-89]. Biological interactions, such as
biotin/streptavidin interactions can be used to easily immobilize the Ab on the surface of nanoparticales.
Combining the catalytic and protein-adsorptive characteristics of gold nanoparticles, Hu et al., prepared
a label-free electrochemical immunosensor with paraoxon antibodies loaded on the gold nanoparticles to
monitor the concentration of paraoxon in aqueous samples with a detection limit of 12 μg/L. TEM
experiment of colloidal gold indicated an average diameter to be 10±0.5 nm (Fig. 13) [44].
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Fig. 11. Fabrication process of the stepwise the stepwise immunosensor based on deposited gold nanocrystals/
4,4’-thiobisbenzenethiol for determination of carbofuran.
Fig. 12. Schematic showing the assembly of a mixed SAM based immunosensor.
Fig. 13. TEM of gold nanoparticles.
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Recently, a type of ordered three-dimensional (3D) gold (Au) nanoclusters obtained by two-step
electrodeposition using the spatial obstruction/direction of the polycarbonate membrane was reported.
The electrodeposited Au nanoclusters built direct electrical contact and immobilization interface with
protein molecules without post-modification and positioning (Fig. 14) [45].
Fig. 14. Schematic diagram of the immunosensor based on 3D gold (Au) nanoclusters
and competitive immunoreaction.
Recently, Sun et al., developed a novel immunosensor for direct determination of carbofuran
concentration by immobilizing anti-carbofuran antibody on the gold nanoparticles(GNPs)/Thiourea
(TU)/GNPs composite film with the detection limit 0.11 ng/mL. The presence of GNPs can enhance
electron transfer between Ab and electrode surface and provide a favorable microenvironment for
immunoreaction. In addition, GNPs on the composite film had a profound influence on enhancing the
conductivity and biocompatibility (Fig. 15) [37].
Fig. 15. Schematic illustration of the stepwise procedure of the immunosensor preparation:
(a) electrodeposited GNPs; (b) modified TU; (c) electrodeposited the second layer of GNPs;
(d) adsorption o f anti-carbofuran and (e) BSA blockin.
In addition, Bhalla et al., reported a label-free detection of phenylurea herbicides by impedance
spectroscopy based on immobilization class specific anti-diuron antibodies on gold nanoparticles
(20 nm). Gold nanoparticles, used as signal enhancers cum immobilization matrix, were
electrodeposited on carbon screen-printed electrodes (SPE) and functionalized with specific anti-diuron
antibodies for the development of bio-interface (Fig. 16) [43].
Another example based on nanoparticles technology is reported for sensitive atrazine determination
based on magnetic beads. The immuno-method is a competitive solid-phase immunoassay where the
anti-atrazine antibody is immobilized on the magnetic beads surface and fixed at the reaction cell bottom
using a simple magnet, which generates a magnetic field. The performance of magnetic beads-based
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immunoassay for atrazine determination was evaluated demonstrating that the magnetic beads-based
immunoassay is one of the most sensitive methods for atrazine determination (Fig. 17) [90].
Fig. 16. Schematic illustration of the stepwise procedure of the immunosensor preparation.
Fig. 18. Principle steps for performing the magnetic beads-based immunoassay.
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4. New Trends and Challenges
4.1. Miniaturization
New analytical approaches are oriented to the development of portable systems with high accuracy,
low-cost, short-time response, and that can provide qualitative information about the composition of a
sample with minimum preparation. Future advances in immobilization will likely focus on directing
biorecognition elements to addressable locations on micro or nano-sensor arrays. A microelectrode, its
dimensions are in the micrometer range, which become a trend to replace common electrod due to its
miniaturization, faster response, greater sensitivity and increased response per unit electrode surface
area (greater current density, increasing the signal-to-noise ratio). Ramon-Azcon et al., have reported an
array of interdigitated μ-electrodes (IDμE) for atrazine detection (32).
4.2. High Throughput of Detection Samples
The ability to construct arrays of microelectrode will likely allow current multianalyte detection of
several compounds to be expanded to accommodate the analysis of perhaps hundreds or thousands of
separate compounds. The combination of microelectrod and microfluidic devices as analytical systems
will become a trend to realized high throughput due to their significant reduction of reagent consumption
and low operating costs as well as high throughput capability.
4.3. Integration of Detection System
One of the challenges that must be met for this type of system would be the development of parallel
computational methods to convert electronic responses for each analyte into meaningful concentration
data. Recently, silicabased monoliths, coupled with micro-fluidic devices, have been used as an
attractive alternative to packed columns for the analysis of proteins, peptides and nucleic acids with
special features of low diffusion resistance during mass transfer, controllable porosity and low back
pressure compared to packed columns.
4.4. Real Samples Detections
Despite the promise of immunosensors, they do have certain limitations. For example, few
immunosensors are commercially available at the present time and are yet to be established as research
or routine tools, due to a lack of validated protocols for a wide range of sample matrices.
4.5. Using Aptamer to Replace Antibody
Immunosensor, itself still has several problems, such as biomolecule deactivation or leaking and high
diffusion resistance of the substrate/biocomponent, which are also key factors in the development of
immunosensors that can be successfully applied to pesticide detection. Aptamers are short,
single-stranded, functional DNA or RNA molecules selected from random-sequencenucleic acid
combina-torial libraries by Systematic Evolution of Ligandsby Exponential Enrichment (SELEX).The
aptamers are more chemically stable, smaller in size, cheaper and can bind nearly any target with high
afnity and specicity compared to antibody [91].
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 30972055, 31101286),
Agricultural Science and Technology Achievements Transformation Fund Projects of the Ministry of Science and
Technology of China (No. 2011GB2C60020) and Shandong Provincial Natural Science Foundation, China
(No.Q2008D03).
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__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
PSoC Based Blood Coagulation Instrument
for the Analysis of PT & APTT
2
RAGHUNATHAN R., 1 NEELAMEGAM P. and 2 MURUGANANTHAN K.
1
School of Electrical and Electronics Engineering
Shanmuga Arts, Science, Technology & Research Academy (SASTRA)
Deemed University, Thanjavur-613 402,Tamil Nadu, India
2
PG and Research Department of Physics, A.V.V.M Sri Pushpam College,
Poondi, Thanjavur, Tamil Nadu, India
E-mail: neelkeer@hotmail.com, raghoo_einstien@yahoo.com , drkmsys@yahoo.com
Received: 22 July 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Internal bleeding and internal clotting is the a major cause of death in patients with chronic
Hepatitis, Carcinoma, Hemophilia, patients undergoing anticoagulant therapy and Diabetic patients
where the natural Blood clotting mechanism that coagulates and clots the bleeding injury doesn’t work
properly. Blood coagulation analyzer is the best tool in diagnosing these patients towards deciding the
course of treatment. Though there are several clotting factors which are involved in blood clotting
mechanism, Prothrombin Time (PT) and Activated Partial Thromboplastin Time (APTT) are the major
diagnostic tools in deciding the course of therapy and dosage. In this paper the design and
development of Coagulation Analyzer using PSoC (Programmable System-on-Chip) CY8C28433 is
presented. The designed Analytic Instrument has shown comparatively better results of Prothrombin
Time and Activated Partial Thromboplastin Time with standard Instruments. The error on comparison
is less than 2 % as agreed by international standards. The designed instrument is useful in diagnosing
of internal bleeding and clotting by interpreting the results AT comparatively lower cost.
Copyright © 2012 IFSA.
Keywords: Haemophilia,Warfarin, PT, APTT.
1. Introduction
Blood is a liquid connective tissue that acts as the main transporting system of the body. It transports
nutrients, respiratory gases, metabolic wastes and other substances from one part of the body to
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 182-191
another. The main components of blood are Erythrocytes or Red Blood Corpuscles (RBC), Leucocytes
or White Blood Corpuscles (WBC), Thrombocytes or Blood Platelets and Blood plasma. Blood
possesses two remarkable properties. The blood remains in a fluid state throughout our life, but when it
is shed, it loses its fluidity in a few seconds. Both these properties are essential for the life. The
property of blood i.e., losing its fluidity and setting into a semisolid jelly when shed, is called Clotting
or Coagulation of Blood [1-3]. This property is due to the natural phenomenon carried over by the
property of plasma proteins and platelets. On further keeping, the clot retracts to a smaller volume and
presses out a clear straw coloured fluid called the serum which will not clot anymore.
2. Coagulation of Blood
When there’s bleeding due to injury blood is shed, the platelets disintegrate and liberate
thromboplastin. It is also derived from damaged tissues and the plasma. It initiates the clotting process.
Platelets are necessary for clot reaction to occur. Therefore failure of a clot reaction is an indication
that the number of platelets in the blood might be lower. The classic theory of blood coagulation as
proposed by Paul Morawitz explains the blood clotting mechanism as follows. Thromboplastin
converts prothrombin into thrombin with the help of calcium ions. Ionic calcium greatly helps in the
formation of active thromboplastin by acting as a cofactor in the coagulation process. Prothrombin is a
plasma protein and it is present in normal plasma [4, 5]. It is manufactured in the liver. Vitamin K is
required by the liver for normal formation of prothrombin. During clotting, prothrombin is converted
to thrombin by thromboplastin. Thrombin is an active enzyme which converts soluble protein
fibrinogen into the insoluble protein fibrin [6, 7]. Thus platelets play a critical role in the influencing
the cascaded coagulation process. Finally, fibrin stabilizes the platelet-rich thrombus called blood clot .
Fibrin is the fine threads which finally form the frame work of the clot entrapping blood cells, platelets
and plasma. The fibrin threads adhere to damaged surfaces of blood vessels, therefore the blood clot
becomes adherent to any vascular opening thereby preventing blood loss [8, 9]. The normal
coagulation time is about 5 to 8 minutes. The fluidity of the blood vessels depends on intact blood
platelets, intact blood vessels and the presence of anticoagulant such as heparin and antithrombin. The
developed Instrument works under the principle of Opto Mechanics and the reaction of the sample
with reagent is Turbo-Densitometry. Opto Mechanics is the principle used to detect blood coagulation
by measurement of the transmitted light intensity [10].
3. System Block Diagram
The system block diagram is shown in Figs. 1 and 2 which depicts the design of the developed
instrument. The block 1 represents sample block, which consists of a Light source, Hyper RED LED
(KL33HHC) with water transparent lens emitting 670 nm wavelength incubation chambers and
Sample cup holder. Below the sample cup holder a dc motor with magnetic rotation mechanism is
fixed to mix the blood sample with the reagent. This magnetic agitator is controlled by the micro
controller. The measurement block also has a temperature control unit to maintain the block at 37 °C.
The temperature is controlled by PSoC using wire wound resistor for heating and temperature sensor.
Based on the temperature sensor output the PSoC switches ON or OFF the heater. Block2 represents
the photo sensor, Photodiode SI336-8BQ.The optical sensor S1336-8BQ (Si photodiode HAMAHATSU PHOTONICS) is used to detect the amount of transmitted light from the solutions.
These Si photodiode also has sensitivity in the UV to near IR range. Active area of photodiode is
5.8 mm - 5.8 mm and photosensitivity of the diode is 0.12 (A/W). It has excellent linearity with respect
to incident light, low noise, wide spectral response range and long life [11]. Block3 represents the
temperature sensor LM35DZ which is used to monitor the temperature of the Measurement Block
(Fig. 2). Block 4 is PSoC Chip CY8C28433-24PVXI from cypress semiconductors which includes two
programmable gain amplifiers (PGA) one for Photodiode and the other for Temperature sensor
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LM35DZ, 14-bit ADC, Multiplexer and processing unit all built inside a single chip. PSoC controller,
which integrates all the above components, becomes the dominant system architecture. A single PSoC
device can integrate several peripheral functions with a microcontroller saving customers design time,
board space and power consumption. The output signals from the photo diode and the temperature
sensor are given to the I/O Pins of PSoC for signal amplification. Further these amplified signals are
selected by Internal Multiplexer of PSoC for Analog to Digital conversion which is another internal
block of PSoC. The PSoC CY8C28433 is built-in with 12 Digital blocks, 6 regular and 4 limited
Analog blocks, one I2C, 2 Decimators, up to 24 Digital I/O, up to 24 Analog inputs, 2 analog outputs
1k RAM and 16k programmable memory [12]. Block 5 represents the keypad, Block 6 indicates LCD,
Block 7 specifies RS232 which provides connectivity to a PC. An LCD is connected to PSoC displays
the menus and the results.
Fig. 1. System Block Block diagram.
Fig. 2 Measurement Block.
4. Materials and Methods
PT reagent kit, Liquiplastin consists of PT reagent 5ml. APTT kit, Liquiceilin-E [13] consists of 5ml
of APTT reagent and 5ml of activator Calcium chloride solution (25 mmol/l).
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4.1. Sample Preparation
The Venous blood from the patient is collected using 2 ml syringe. About 1.8ml of the collected blood
is dispensed into a test tube containing 200 micro-liter of 3.2 % tri-sodium citrate (0.11 mol/l) [14]and
mixed well. The mixture is of ratio 9:1. It is then centrifuged for 15-20 minutes. The clear plasma
formed as supernatant is then separated and transferred to another fresh test tube [15].
4.2. Measurement of PT
For performing Prothrombin Test, a cuvette with an iron ball is placed in a sample block. Now the
sample of 50 micro- liter Plasma is taken in a cuvette. It should be incubated at 37 0C for 2 minutes by
keeping into the incubation block. After that 100 micro litre of liquiplastin (clotting Reagent) is
pipetted into the cuvette forcibly. As soon as the reagent is added the intensity decreases which is
measured by photodiode and there by the controller starts the motor, rotating the magnet which enables
the mixing of sample and reagent by rotating the iron ball inside the sample cup, simultaneously
measuring the time in seconds using the timer. When the clot is formed the motor is stopped by the
controller as sensed by the drastic change in intensity of light due to the increased turbidity of the
sample density. The time in seconds between starting and stopping of the motor gives a measure of
Prothrombin time. A mixture of plasma separated from 5-10 normal patients pooled and PT time is
measured for this Fresh Normal pooled Plasma (FNPP) , the test is repeated for 5-6 times and is
averaged. This average is MNPT (Mean Normal PT for reagent) and is used to calculate Karl
Pearson’s Coefficient; R. R is calculated using the formula.
Patient PT
R=
(1)
MNPT for Reagent
It is recommended by the WHO that MNPT should be established for each lot of PT reagents by each
laboratory, since PT results are dependent on the combination of reagent lot, instrument and technique
followed at each laboratory. International normalized ratio INR is calculated using R and International
sensitivity Index (ISI) value that is provided by the manufacture of the reagent
INR = ( R ) ISI
(2)
The INR calculation avoids the confusion of establishing the normal values as different laboratory has
different normal ranges. INR index is accepted as international standard.
4.3. Measurement of APTT
In APTT 50 micro-liter Plasma is added with 50 micro liter of liquicelin-E into a sample cup
containing iron ball for mixing and incubated for 2minutes. It’s then placed in the sample holder and
then about 50 micro-liter of calcium chloride (25 mmol/l) solution is added. Now the motor is started
and the timer in microcontroller measure time in seconds until the clot is formed which is indicated by
the drastic change in the photodiode output. This measurement in seconds gives the APTT for the
sample. There is no international convention like INR for APTT like PTT. Karl Pearson’s Coefficient
R is calculated using the formula:
APTT of patient plasma (in seconds)
R=
(3)
MNAPTT of Reagent (in seconds)
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Normal values using LIQUICELIN-E reagent are between 22-34 seconds. Between manual and Turbo
densitometric instrument results a variation of 1-2 seconds may be expected. For photo optical
instruments, it is recommended that each laboratory must establish normal range of their own.
5. Software
Development of software for the present system involves the following modules configuring analog
and digital blocks as peripherals inside PSoC, initialization of LCD, starting ADC, reading 14 bit data
signals, measurement and maintenance of temperature at 37 0C, measurement and monitoring of
Changes in Photodiode output, starting the motor when the reagent is mixed with sample, stopping the
motor when clot is formed, recording the clot time in seconds from the timer, calculating and
displaying the results on LCD. The Flow chart for performing the above is given in the flowchart
(Fig. 3).
Fig. 3. Flow chart.
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6. Results and Discussion
The Table 1 shows the Prothrombin time measured for 20 Patients by using the developed instrument.
The Values are compared with the two other Standard Instruments URIT-160 and COAG 120. It can
be seen that the values are very close to the values obtained with Standard Instruments. The Normal
Value for PT is 10 to 14 seconds. It is observed that the Patients having PT above these values are
suffered from internal bleeding due to diseases like Diabetes, Cancer or chronic hepatitis. MNPT is
estimated to be 13.5 for the developed Instrument. ISI for the particular lot of the reagent is given as
1.6 (Reagent Manufacture’s data). These values are used to calculate R and INR. APTT and calculated
R values are measured for 10 samples and compared with the values of the standard instruments as
shown in Table 2.
Table 1. Prothrombin Test results compared with standard instruments.
Sample
PT Sec
Developed
Plasmatrol
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
Patient 6
Patient 7
Patient 8
Patient 9
Patient 10
Patient 11
Patient 12
Patient 13
Patient 14
Patient 15
Patient 16
Patient 17
Patient 18
Patient 19
10.2
13.2
45.3
13.1
7.5
13.2
87.4
12.6
10.8
54.8
14.5
13.4
14.2
12.6
32.2
28.3
13
10.8
11.4
15.5
URIT
160
10.9
12.1
41.2
12.9
7.5
13.8
87
13.2
11.3
55.1
15.3
13.6
13.5
13.7
30
30.7
14.5
12.6
11.7
14.1
R
COAG
120
10.6
12.8
43.8
13.3
6.9
14.3
92
12.9
11.2
54.7
15.6
14.2
14
14.1
33.2
31.2
13.2
10.6
12.9
14.9
Developed
1
1.294
4.441
1.284
0.735
1.294
8.569
1.235
1.059
5.373
1.422
1.314
1.392
1.235
3.157
2.775
1.275
1.059
1.118
1.52
URIT
160
1.069
1.186
4.039
1.265
0.735
1.353
8.529
1.294
1.108
5.402
1.5
1.333
1.324
1.343
2.941
3.01
1.422
1.235
1.147
1.382
INR
COAG
120
1.039
1.255
4.294
1.304
0.676
1.402
9.02
1.265
1.098
5.363
1.529
1.392
1.373
1.382
3.255
3.059
1.294
1.039
1.265
1.461
Developed
1
1.51
10.863
1.492
0.611
1.51
31.095
1.402
1.096
14.735
1.756
1.548
1.698
1.402
6.293
5.119
1.475
1.096
1.195
1,954
URIT
160
1.113
1.314
9.333
1.457
0.611
1.622
30.863
1.51
1.178
14.862
1.913
1.584
1.567
1.603
5.618
5.831
1.756
1.402
1.245
1.678
COAG
120
1.063
1.438
10.294
1.529
0.534
1.717
33.754
1.457
1.161
14.691
1.973
1.698
1.661
1.678
6.608
5.983
1.51
1.063
1.457
1.834
Table. 2. Test results of PT and APTT of PLASMATROL with Developed Instrument.
PT Sec
13.8
14.0
13.1
12.9
13.5
13.8
13.8
14.1
13.2
13.9
R
1.022
1.037
0.97
0.956
1
1.022
1.022
1.044
0.978
1.03
INR
1.035
1.06
0.952
0.931
1
1.035
1.035
1.071
0.965
1.048
APTT Sec
34.1
33.8
33.1
33.0
32.9
34.5
32.8
34.0
33.4
33.5
R
1.033
1.024
1.003
1
0.997
1.045
0.994
1.03
1.012
1.015
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7. Validation of Developed Instrument
The developed Instrument is validated using Quality control for its known values and ranges.
PLASMATROL H-I normal plasma control is used to check the PT and APTT values on the
developed Instrument. It was observed that the values are very close to the target and well within the
establish range. Table 3 shows the target and ranges for APTT and PT for plasmatrol control Lot
no.309106. Table 4 shows the PT and APTT values obtained with Developed Instrument best agrees
with the Quality control targets assuring the validation of the developed instrument.
Table 3. PLASMATROL H-I normal control data.
LOT No
309106
PT
Mean Sec
13.5
APTT
Range Sec
10.5-16.5
Mean Sec
33.0
Range Sec
31-47
Table 4. Activated Partial Thromboplastin Time in Seconds by Using the developed Instrument
and Standard Instruments.
Sample
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
Patient 6
Patient 7
Patient 8
Patient 9
Plasmatrol
Developed
URIT 160
COAG120
32.8
33.1
71.8
28.9
35.6
31.5
29.6
25.4
30.1
37.5
32.3
33
70.9
29.1
34.8
31.5
28.9
26
30.7
36.7
31.1
32.6
70
28.5
35.1
31
29.2
26.8
31.3
38.9
Developed
0.959
0.968
2.099
0.845
1.041
0.921
0.865
0.743
0.88
1.096
R
URIT160
0.944
0.965
2.073
0.851
1.018
0.921
0.845
0.76
0.898
1.073
COAG120
0.909
0.953
2.047
0.833
1.026
0.906
0.854
0.784
0.915
1.137
7.1. Precision and Accuracy
The test results of Quality control PLASMATROL as shown in Table 4 explain a good precision and
accuracy of the developed instrument. The S.D is 0.41 for PT (Sec) and 0.57 for APTT (Sec). The %
error between observed values and the Target value as per table 4 is less than 1 %.
7.2. Statistical Study- PT
The statistical studies prove that the results obtained by using the developed instrument highly
correlate with the results obtained using the standard Instruments. The Plotted Regression lines
between PT results of developed and standard instruments in Fig 4 (a) and 4(b) approximates the line
of Equality which confirms that, the developed Instrument is best fit with the standard Instruments. It
is arrived by calculating the Karl Pearson’s Coefficient of R and INR which shows the good agreement
between the developed Instrument and the Standard Instruments. Fig. 4(c) and 4(d) shows linear
regression analysis of R and Figs. 4(e) and 4(f) of that of INR as compared with standard instruments.
The correlation between the Developed instrument and the standard ones were observed to be of 99 %.
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Table 4 gives APTT measured for 10 Patients by using the designed Instrument. The values are
compared with that of Standard Instruments. Values of corresponding Karl’s pearson Coefficient R are
calculated The Normal values of APTT vary from 25-35 seconds. The coefficient R is calculated.
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 4. Linear Regression PT and INR curves.
7.3. Statistical Study- APTT
The linear regression study of compared values between the developed Instrument with the standard
instrument shows a good correlation of 99 %. Figs. 5(a) and 5(b) shows the regression line for APTT
and 5(c) and 5(d) that of R as compared with standard instruments. MNAPTT is estimated to be
34.2 which is used to calculate R, Karl’s pearson Coefficient. It’s observed that the slope of linear
regression is close to 1.
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(a)
(b)
(c)
(d)
Fig. 5. Linear Regression APPT curve.
7.4. Interpretation on Results
Interpretation of INR results may give a clear picture of the patient’s condition. INR level above 5
indicates that there is a high chance of bleeding, In cases where the INR is 5 or less there is a high
chance of having a clot [16]. Normal range for a healthy person is 0.9–2.0. For people on warfarin
(anticoagulant) therapy INR falls between 2.5–5.0 [17]. Warfarin is effectively used in cases of arterial
fibrillations [18]. INR arrived from PT measurement provides an excellent monitoring of warfarin
dosage [10]. From the tables 3 patients 2 and 6, showing higher INR were reported to be under
anticoagulant therapy against blood clot disease conditions like Clot in cardiac vessels, Thrombosis,
Thromboembolism or deep vein thrombosis (DVT). Patients 14 and 15 were reported with internal
bleeding due to liver dysfunction. An estimation of R and INR enables the physician in deciding the
anticoagulant dosage and further course of therapy.
8. Conclusion
The Instrument for Blood coagulation measurement is designed and developed which plays a vital role
in bio medical Instrumentation. The value of PT and APTT measurements are compared with the
standard instruments URIT-160 and COAG-120. It is found that the values are well suited with that of
above said Standard Instruments. Moreover the system is easy to operate and does not require any
skilled persons. This blood clotting time machine is low cost, portable and user friendly diagnostic tool
for physicians. The present instrument developed can perform tests PT and APTT. However the
software can be extended for performing the other Blood clotting factor assays.
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References
[1]. James P. Riddel Jr., Bradley E. Aouizerat, Christine Miaskowski, David P. Lillicrap, Theories of Blood
Coagulation, Journal of Pediatric Oncology Nursing, 24, 3, May 2007, pp. 123-131.
[2]. Nigel Mackman, Rachel E. Tilley, and Nigel S. Key, Role of the Extrinsic Pathway of Blood Coagulation
in Hemostasis and Thrombosis, Arteriosclerosis, Thrombosis, and Vascular Biology, Lippincott Williams &
Wilkins, USA, 2007, pp. 1687.
[3]. Robert Rodvien, and C. Harold Mielke, JR., Role of Platelets in Hemostasis and Thrombosis, San
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[4]. Tzong-Jih Cheng, Hsien-Chang Chang, Tsun-Mei Lin, Biosensors & Bioelectronics, 13, 1998, pp. 147-156.
[5]. L. Theodorakis, E. Papadopoulos, S. Katsaragakisa, C. S. Karagiannib, E. Hristoforou, On the response of a
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[6]. Colman R. W., Surfaces in mediated defense reactions: The plasma contact activation system, J Clin
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[7]. Davies G.C., Sobel M., Salzman E.W., Elevated plasma fibrinopeptide A and Thromboxane B2 levels
during cardiopulmonary bypass, Circulation, 61, 1980, pp. 808.
[8]. Woodman R.C., Harker L.A., Bleeding complications associated with cardiopulmonary bypass, Blood,
1990, 76, pp. 1680-97.
[9]. Czer L.S.C., Mediastinal bleeding after cardiac surgery: etiologies, diagnostic considerations, and blood
conservation methods, J. Cardiothorac Anesth, 1989, 3, pp. 760-75.
[10].Hyunjung Lim, Jeonghun Nam, Yongjin Lee, Shubin Xue, Seok Chung and Sehyun Shin, Blood
Coagulation Study Using Light-Transmission Method, MicroTAS 2010, The Netherlands, October 3-7,
2010.
[11].Si photodiode S1336-8BQ data sheet: http://www.hamamatsu.com, August 2009.
[12].CY8C28433-24PVXI product datasheet, http://www.cypress.com
[13].Reagent pack insert of LIQUIPLASTIN (Tulip Diagnostics). Cephaloplastin reagent for partial thromboplastin time (APTT) determination using ellagic acid, as an activator. Available online at:
http://www.tulipgroup.com/Tulip_New/html/product_specs/43_liquiplastin_x.htm
[14].Reagent pack insert of LIQUICIELIN-E (Tulip Diagnostics). Available online at:
http://www.tulipgroup.com/Tulip_New/html/product_specs/44_liquicelin_x. htm
[15].Connie L. Davis, Wayne L. Chandler, J. Am. Soc. Nephrol., 1995, 6, pp. 1250-1255.
[16].Marie B. Walker, Clot Care: Understanding the PT-INR Test, Clot Care Online Resource, Dvt awareness,
January, 2004, http://www.clotcare.org
[17].J. L. van Rijn, N. A. Schmidt and W. P. Rutten, Correction of instrument- and reagent-based differences in
determination of the International Normalized Ratio (INR) for monitoring anticoagulant therapy, Clinical
Chemistry, Vol. 35, No. 5 May 1989, pp. 840-843.
[18].Warfarin Therapy Management in Adults, Guidelines & Protocols Advisory Committee Ministry of Health,
British, Colambia, http://www.BCGuidelines.ca
__________________
2012 Copyright ©, International Frequency Sensor Association (IFSA). All rights reserved.
(http://www.sensorsportal.com)
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Sensors & Transducers
ISSN 1726-5479
© 2012 by IFSA
http://www.sensorsportal.com
L-Asparaginase Extracted From Capsicum annum L
and Development of Asparagine Biosensor for Leukemia
Kuldeep KUMAR and Shefali WALIA
Department of Biotechnology, M.M. Modi College, Patiala-147 001 Punjab, India
Tel.: +91-9876089356, fax: +91-175-2212049
E-mail: kuldeepbio@rediffmail.com
Received: 26 June 2012 /Accepted: 21 September 2012 /Published: 28 September 2012
Abstract: Green chillies (Capsicum annum L.) contain appreciable amount of L-asparaginase enzyme
and the present work aims at the development of asparagine biosensor for leukemia. It is a novel and
diagnostic plant based biosensor for monitoring asparagine levels acute lymphoblastic leukemia (ALL)
samples. Different immobilization techniques and response time studies have been carried out to
improve the stability of enzyme by these methods. Phenol Red indicator has been coimmobilized with
plant asparaginase and color visualization approach has been optimized for various asparagine ranges.
For quantitative analysis, immobilized biocomponent is coupled to Ion Sensing Electrode (ISE) of a
potentiometeric transducer. The detection limit of asparagine achieved with immobilization techniques
such as gelatin, polyacrylamide gel, agar and calcium alginate beads method is 10-1 –10-9M. Also,
these techniques have been applied for the detection of asparagine in normal (10-4M) and leukemia
blood serum samples (10-2M). Copyright © 2012 IFSA.
Keywords: Capsicum annum, L-asparaginase, Leukemia, Biosensor, Immobilized.
1. Introduction
The enzyme L-asparaginase is widely used as antitumoral agent for the treatment of acute
lymphoblastic leukemia (ALL) [1]. L-asparaginase is the first enzyme which is to be studied in human
beings because of its antitumour activity [2]. Tumor cells need L-asparagine for their growth because
they lack the enzyme, asparagine synthetase that synthesizes this amino acid [3] and in the presence of
L-asparaginase, tumor cells cannot survive due to unavailability of important growth factor [4]. Lang
was the first person to detect asparaginase activity in beef tissues [5]. Kidd reported antitumour
properties of Guinea pig serum, which was later attributed to asparaginase activity [6-7]. Thus ALL
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and lymphosarcoma are effectively treated by L- asparaginase and has been clinically accepted as
antitumour agent [8].
The enzyme L-asparaginase is produced by number of microorganisms such as Erwinia cartovora [9],
E. coli [10], Candida utilis [11], and Thermus thermophilus [12]. Among various plants species,
production of L asparaginase has been reported. Green chillies (Capsicum annum L.) contained
L- asparaginase enzyme, its extraction partial purification and properties of L- asparaginase from green
chillies has been reported [13]. Withania somnifera was identified as potential source of
L-asparaginase due to high specific activity [14]. The presence of an amidase in barley roots capable of
hydrolyzing L-asparagine [15]. L-asparaginase was also produced in large amount from root nodules
of soyabean [16]. Pisum sativum contain appreciable amount of L-asparaginase and was also detected
in young leaves [17]. L-aparaginase activity was also detected from two more plants Lupin arboreus
and L.angustiplius [18].
For the fabrication of biosensor, an online gas analyzer for automated enzymatic analysis with
potentiometric ammonia detection has been developed [19]. E. coli K-12 asparaginase based
L- asparagine biosensor has been developed to detect asparagine levels in normal and leukemia blood
samples [20]. An amperometric biosensor based on spinach (Spinacia aleracea) tissue homogenate
was developed for determination of urinary oxalate [21]. L- asparaginase was immobilized and used
with an ammonium selective electrode (ISE) to develop enzymatic biosensor. Garlic tissue electrode is
used to determine L-asparagine where garlic tissue cells were employed for conversion of
L-asparagine into ammonia and ammonium gas electrode was used as detector [22]. A thermostable
recombinant asparaginase from Archaeoglobus fulgidus was cloned and expressed in E.coli as a fusion
protein and the immobilized enzyme was used with an ammonium selective electrode to develop a
biosensor for L-asparaginase [23]. A garlic (allium sativum L.) peroxidase biosensor for hydrogen
peroxidase monitoring, which was immobilized on chitosan matrix was formed [24]. Petunia punctata,
Alternanthera sessilis & Amoora chittagonga extracts showed cytotoxicity screening against three
pancreatic cancer lines-adenocarcinoma cell line Panc-1, Mia-Pacca-2, capan-1 using label free
biosensor assay [25]. Amperometric oxalate biosensor based on sorghum leaf oxalate oxidase,
immobilized on carboxylated multi-walled carbon nano tubes and conducting polymers, polyaniline
composite film was constructed [26]. The current study presents the development of a novel,
diagnostic and cost-effective plant L-asparaginase based biosensor for leukemia blood samples.
2. Experimental
2.1. Biological Materials and Reagents
All the chemicals and reagent used in the study were of analytical grade. The transducer is a benchtop
potentiometer (Cyberscan 2500) in conjunction with an NH4+ ion selective electrode (ISE Code
No. EC-NH4-03) that detects the electrode potential developed across the membrane of the electrode
when it comes in contact with NH4+ ions.
2.2. Crude Extract Preparation
The enzyme L-asparaginase was extracted from green chillies (Capsicum annum L.). Fresh green
chillies (about 250 g) were homogenized with three volumes of 0.15 Μ KCl buffer solution and
centrifuged at 4 0C. The supernatant was separated out and the pellet formed was dissolved and reextracted with 0.15 Μ KCl buffer solution [13]. The obtained supernatant was designated as crude
extract (L-asparaginase) and further for biosensor applications, it was coimmobilized with phenol red
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indicator (HiMedia Laboratories Pvt. Ltd., India.) that changes color from red to violet (red color was
due to phenol red indicator). When the enzyme L-asparaginase come in contact with substrate
(asparagine), deamination reaction occur in which asparagine break down into aspartic acid and NH4+
ions and color change response was observed. Asparagine concentrations ranging from 10-9-10-1 M
were studied by color visualization approach and with potentiometeric transducer. Immobilization
techniques for the biosensor fabrication are as follow.
2.3. Gelatin Method
2 gm of gelatin was dissolved in water by heating it properly. After cooling to 35-40 0C, 20 µl
enzymes (0.5U) were co-immobilized with 10 µl phenol red indicator. 2 ml of hardening solution was
added comprising of 4ml formaldehyde, 6 ml water and 10 ml ethanol. It was allowed to freeze at
-20 0C for 4 hour to facilitate the gel formation. Then gel was warmed to room temperature and cut
into square blocks of 1.0  1.0 cm [27]. Put these blocks into varying concentration of L-asparagine
and the response time was noted for change in color of blocks from partly orange to dark purple. For
quantitative analysis, NH4+ Ion Sensing Electrode (ISE) is used.
2.4. Polyacrylamide Method
A 10 % acrylamide and bis-acrylamide solution (9 % acrylamide and 1% bis- acrylamide) was
prepared in 0.1 M phosphate buffer (pH 7.0). 20 µl of enzyme (0.5U) was coimmobilized with 10 µl
phenol red indicator in acrylamide solution. To the above solution 0.5 gm of ammonium per sulphate
was added. 50 ml TEMED was added and contents were stirred gently and the solution was poured
into petriplate. After it was solidified the gel was cut into square blocks of 1.0  1.0 cm [28]. The
pieces of gel were taken in different concentration of L-asparagine and the color change was noted
down. Then the pieces of gel were then put into varying concentrations of L-asparagine and note the
reading with NH4+ ISE.
2.5. Agar Method
The agar solution of 4 % concentration was heated to liquefy the agar and allowed to cool at 45-50 0C.
20 μl enzyme (0.5U) and 10 μl phenol red indicator was added to the solution. The contents were
stirred gently, poured it into petriplate and allowed it to solidify. The gel was then cut into square
cakes of 1.0  1.0 cm with the help of knife or spatula [29]. Then cakes were put into varying
concentration of L-asparagine and the response time was noted for change in color of cakes from
partly orange to dark purple. Detection limit of L-asparagine achieved was 10-9 – 10-1 M. NH4+ Ion
Sensing Electrode (ISE) is used for quantitative analysis.
2.6. Calcium Alginate Beads
It was carried out by Sodium alginate CaCl2 technique. Slurry of 3 % sodium alginate with 20 l of the
enzyme solution (0.5U) was formed and 10 l of phenol red indicator were added to this slurry. This
solution was then poured drop wise through a glass syringe into a beaker containing 0.075 M chilled
CaCl2 with gentle stirring on a magnetic stirrer. Orange color beads (partly orange color of the beads
was due to phenol red indicator) were made with the help of 2.5 ml syringe without needle [30]. The
beaker was kept for half an hour for hardening of the beads. Beads were then washed with distilled
water for further use. The beads were put into varying concentrations of L-asparagine (10-9-10-1 M)
solutions. The response time for change in color of beads from partly orange to bright purple was
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 192-200
noted. For quantitative analysis, the beads were then put into varying concentrations of L-asparagine
was noted with NH4+ Ion Sensing Electrode (ISE) of a potentiometeric transducer.
2.7. Monitoring of Asparagine Levels in normal and Leukemia Blood Serum Samples
The beads of calcium alginate were put into normal and leukemia blood samples. Response time for
color change of beads till purple color appears was noted. The asparagine levels in both the samples
was monitored by relating the response time for change in color of beads of both the samples with the
response time for change in color of beads with concentration levels from 10-9 – 10-1 M of asparagine.
2.8. Check the Reliability of the NH4+ISE
To check the reliability of the ISE, calculation of ∆ mV and response times for change in color was
studied by formula:
1/2x +1/2y ═ X,
where x ═ Serum sample and y ═ Synthetic sample of L-asparagine.
2.9. Storage Stability
To know the storage stability of biocomponent i.e. gelatin gel blocks, polyacrylamide gel, agar cakes
and calcium alginate beads were wrapped in a Whattmann filter paper soaked in CaCl2 and were kept
in refrigerator. The activities of immobilized biocomponents were checked.
4. Results and Discussion
4.1. Gelatin Method
For gelatin method, visual color change was observed. The comparison of color of gel blocks before
and after the reaction (see Fig. 1). Detection limit of asparagine achieved was 10-9 – 10-1 M. For
concentration level of 10-1 M L-asparagine, response time and mV reading detected was 22 seconds,
-9
-2
-135.2 and for the concentration level of 10 -10 M L-asparagine, response time detected was in the
range of 10-20 seconds (Table 1). Response time decreased with decreased in concentration of
asparagine indicating more of NH4+ ion produced after hydrolysis.
Fig. 1. Comparison of Color of gel blocks (Before and after the reaction).
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Table 1. The mV readings with NH4+ ISE and response time.
Concentration of Lasparagine, (Molar)
10-1
10-2
10-3
10-4
10-5
10-6
10-7
10-8
10-9
Potential Value,
(mV)
-135.2
-147.7
-159.9
-170.2
-179.1
-183.7
-195.6
-204.2
-210.8
Response Time,
(s)
21.6
20.0
17.9
16.5
15.6
14.0
12.3
11.3
10.0
4.2. Polyacrylamide Method
Detection limit of L-asparagine achieved was 10-9 – 10-1 M. For concentration level of 10-1 M
L-asparagine, response time and mV reading detected was 20 seconds, -147.7 and for the concentration
level of 10-9-10-2 M L-asparagine, response time detected was in the range of 10-19 seconds (Table 2).
Response time decreased with decreased in concentration of L-asparagine indicating more of NH4+ ion
produced after hydrolysis (see Fig. 2).
Fig. 2. Comparison of Color of gel pieces (Before and after the reaction).
Table 2. The mV readings with NH4+ ISE and response time.
Concentration of Lasparagine, (Molar)
10-1
10-2
10-3
10-4
10-5
10-6
10-7
10-8
10-9
Potential Value,
(mV)
-147.7
-156.6
-170.5
-178.0
-183.9
-188.2
-204.2
-207.5
-210.8
Response Time,
(s)
20.0
18.7
16.6
15.3
13.9
13.1
11.3
10.5
10.0
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4.3. Agar Method
Detection limit of L-asparagine achieved was 10-9 – 10-1 M. For concentration level of 10-1 M
asparagine, response time and mV reading detected 14 seconds, -180 and for the concentration level of
10-9-10-2 M, L-asparagine response time detected was in the range of 7-13 seconds (Table 3). With
decrease in concentration of asparagine, the response time for color change increases (see Fig. 3).
Fig. 3. Comparison of Color of gel blocks.
Table 3. The mV readings with NH4+ ISE and response time.
Concentration of Lasparagine, (Molar)
10-1
10-2
10-3
10-4
10-5
10-6
10-7
10-8
10-9
Potential Value,
(mV)
-180.0
-196.5
-204.0
-208.7
-212.4
-217.1
-220.3
-226.7
-230.2
Response Time,
(s)
14.2
12.5
11.2
11.0
9.8
8.8
8.5
8.0
7.5
4.4. Calcium Alginate Beads
For calcium alginate beads, visual color change was observed. The comparison of color of beads
before and after the reaction (see Fig. 4). Detection limit of L-asparagine achieved was 10-9 – 10-1 M.
For concentration level of 10-1 M L-asparagine, response time and mV reading detected was
12 seconds, -195.5 and for the concentration level of 10-9-10-2 M L-asparagine, response time detected
was in the range of 7-11 seconds (Table 4). This is directly related to decrease in NH4+ produced after
the reaction due to increasingly lesser concentration of the reactant. In the comparison of all
immobilized techniques, calcium alginate beads method is fast time response and more stable.
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Sensors & Transducers Journal, Vol. 144, Issue 9, September 2012, pp. 192-200
Fig. 4. Color of beads before and after the reaction.
Table 4. The mV readings with NH4+ ISE and response time.
Concentration of Lasparagine, (Molar)
10-1
10-2
10-3
10-4
10-5
10-6
10-7
10-8
10-9
Potential Value,
(mV)
-195.5
-204.6
-208.0
-213.7
-216.1
-218.0
-223.5
-230.9
-235.0
Response Time,
(s)
12.3
11.2
10.5
9.4
9.0
8.5
7.8
7.5
7.1
4.5. Testing Asparagine Levels in Normal and Leukemia Blood Serum Sample by Calcium
Alginate Beads
Response time for change in color of the beads was 9.4 seconds for the normal blood serum sample
and the asparagine concentration level was in the range of 10-4M. Response time for change in color of
the beads was 11.2 seconds for the leukemia blood serum sample and the asparagine concentration
level was 10-2 M. Thus, asparagine levels were found to be high in leukemia blood than normal blood.
4.6. Reliability Check for the Constructed Biosensor
The response times for color change 10-2 M and 10-4 M was done to check reliability of the developed
biosensor. Hence, the developed biosensor is quite reliable and comparable. Hence, visualization
approach and ISE transducer coupling can be opted for monitoring L-asparagine concentration in
blood samples of leukemia and normal samples.
4.7. Storage Stability of the Biocomponent
The biocomponent was found to be active. Gelatin gel blocks, Polyacrylamide gel pieces, agar cakes
and calcium alginate beads were found to be stable for a long time i.e. more than fifteen days, one
month, fifteen days and four months respectively.
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5. Conclusion
The developed biosensor using various immobilization techniques was able to detect asparagine levels
from 10-9-10-1 M and further it was used for detection of asparagine levels in normal and leukemia
blood serum samples. In comparison with the asparagine biosensors developed by Fraticelli and
Meyerhoff and Wang, asparagine range of 10-9 M could be detected while the earlier efforts could
detect levels up to only 10-5 M [19, 23]. Moreover, the unit of enzyme used for the detection of
asparagine levels is about 0.5 U. Thus, rapid detection of asparagine concentrations by minute
quantities of enzyme (20 µl) is possible that makes the biosensor extremely cost-effective. Thus, the
developed biosensor is novel, diagnostic, very rapid, easy to use, inexpensive, portable and capable of
nanolevel asparagine detection.
Acknowledgement
The authors wish to thank Modi Education Society and Dr. Satish K. Bhardwaj, Principal, M.M. Modi
College, Patiala for encouragements.
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