Nanodiamond Seeding of QCM for Biosensor Applications
Student: Andre Schexnider
Mentor: Adarsh Radadia
Introduction
Infections in United States from food-borne pathogens, Salmonella, E. coli, Campylobacter, and
Listeria caused more than 3 billion dollars of economic damage in 2010 [1]. Food supplies must
therefore be carefully monitored. One approach to such monitoring uses highly sensitive,
selective, and stable sensors that can work continuously without constant removal and analysis.
Quick detection is important. If the bacteria are detected early enough, the supply lines can be
stopped immediately and the amount of infections will decrease. Biosensors that have been
developed to detect bacteria have used antibodies, aptamers, and synthetic molecules to capture
the bacteria. The captured bacteria can then detected by either the change in mass, electronic
structure, or thickness of the capturing film [2].
Quartz Crystal Microbalances (QCM) can detect mass changes. Quartz crystals have a natural
frequency, and frequency changes can be directly correlated to its mass [2]. However, the typical
surfaces for QCM are gold, platinum, silicon dioxide, and silicon nitride, which can lead to
unstable surfaces in liquid environment on prolonged exposure [4]. Radadia et al. have shown
that ultrananocrystalline diamond (UNCD) surfaces have extended stability which makes this
surface promising in the creation of longer lasting biosensors [3].
The aim of this project is to utilize the high stability of diamond and combine with the sensing
ability of QCM, to create a real-time, highly selective biosensor that has a reasonable lifetime.
Growth of UNCD is first accompanied by seeding of the surface in which small nanoparticles of
diamond are sonicated on to the surface. These seeds are sites of nucleation. The assumption is
that the seeding process will generate an evenly distributed surface of nanodiamond particles,
where the antibodies would then be attached.
Methods
The sample was seeded with 3 concentrations of a mixture with ratios of 1:1, 1:3 and 1:5 slurry
to methanol. The sonication time was varied at 5, 15, and 30 minutes. These samples were
analyzed using AFM and SEM. Figure 1 shows the morphology of the diamond on the surface
of the sample that was sonicated for 15 minutes in a 1:3 solution.
Results and Conclusion
The results demonstrated that the surfaces were coated with UNCD (Figure 1) and that the
resulting surface roughness depended on sonication time and the ratio of diamond/DMSO slurry
to methanol.
1
Figure 1: (Left) Scanning electron microscope image of a nodiamond-coated surface. (Right)
Dependence of surface roughness on slurry content and sonication time.
References
[1] U. ERS. (2011). Foodborne Illness Cost Calculator.
[2] M. Liss, B. Petersen, H. Wolf, and E. Prohaska, "An aptamer-based quartz crystal protein
biosensor," Anal Chem, vol. 74, pp. 4488-95, 2002.
[3] A. D. Radadia, C. J. Stavis, R. Carr, H. Zeng, W. P. King, J. A. Carlisle, et al., "Control of
Nanoscale Environment to Improve Stability of Immobilized Proteins on Diamond
Surfaces," Adv Funct Mater, vol. 21, pp. 1040-1050, 2011.
2
Effect of Particle Clustering and Geometry on Joule Heating
in Therapeutic Nanoparticles
Student: Nicholas K. Rader
Mentor: Dentcho Genov
Introduction:
Gold nanoparticles, in the form of nanorods, exposed to 800 nm wavelength light, neutralize
cancer cells without a biomarker [1]. In addition, immunotargeted nanoshells have been used to
find and destroy breast carcinoma cells that overexpress HER2, a relevant cancer biomarker [2].
Gold nanoparticles have been applied to cancer imaging, spectroscopic detection, and
photothermal therapy [3]. Surface plasmon resonance leads to intense electromagnetic fields on
the particle surface that enhance absorption and scattering [3]. When gold nanoparticles undergo
coupled surface plasmon resonance, they create strong electric fields which enhance imaging of
cancerous cells and distinguish between malignant and nonmalignat cells [4]. Silver
nanoparticles have also been studied in antiviral therapy, specifically, with the HIV-1 virus. The
nanoparticles bind to the virus specifically in the spectrum of 1-10 nm making its interaction
with the virus size-dependent [5].
Metal nanoparticles interact with each other uniquely. This unique interaction results in the
exponential distance decay and the interesting “universal” scaling behavior of interparticle
plasmon coupling [6]. It is possible to derive a “plasmon ruler equation” that estimates the
interparticle seperation between gold nanospheres in a biological system from the observed
fractional shift of the plasmon band. This equation allows metal nanoparticles to be optimized in
chemical and biological imaging, sensing, and therapeutics.
Methods
In the present work, we used COMSOL to generate a geometric model of gold nanospheres and
to solve Maxwell’s equations and Gauss’s laws to calculate surface plasmon resonance. From
these simulations, we derived the resistive heating as a function of wavelength for circular
spheres and elliptical spheres.
Results
Figure 2 displays the resistive heating as a function of wavelength for each spherical geometry,
and Figure 3 shows the resisitive heating as a function of wavelength for the elliptical spheres.
The combination of three spheres had the highest peak of 1.5 Joules at 360 nm, while one sphere
had the second highest peak of 0.42 Joules at 390 nm. The largest heating for ellipsoids occurred
with single particles (1.15 Joules at 4.27 nm), followed closely by four particles (0.93 Joules at
502 nm).
3
Figure 2: Resistive heating for different geometries of nanospheres. Red (one sphere), Green (two
spheres), Blue (three spheres), Purple (four spheres).
Figure 3: Red (one ellipsoid), Green (two ellipsoids), Blue (three ellipsoids), Purple (four ellipsoids)
Conclusion
Complex geomtries were as efficient at absorbing light through its surface plasmon resonance as
simple geometries. However, the simpler geometries led to resistive heating that was more
wavelength-specific.
References
[1] X. Huang, I.H. El-Sayed, W. Qian, and M.A. El-Sayed, “ Cancer Cell Imaging and
Photothermal Therapy in the Near-Infrared Region by Using Gold Nanorods,” J. Am. Chem.
Soc., vol. 128, pp. 2115-2120, 2006
4
[2] C.Loo, A. Lowery, N. Halas, J. West, and R. Drezek, “Immunotargeted Nanoshells for
Integrated Cancer Imaging and Therapy,” Nano Lett., vol. 5, pp. 709-711, 2005
[3] X. Huang and M.A. El-Sayed, “Gold Nanoparticles: Optical properties and implementations
in cancer diagnosis and photothermal therapy,” J. Advanced Research, vol. 1, pp.13-28, Jan.
2010
[4] J.C. Kah, K.W. Kho, C.G. Lee, C. James, R. Sheppard, Z.X. Shen, K.C. Soo, and M.C.
Olivo, “Early diagnosis of oral cancer based on the surface plasmon resonance of gold
nanoparticles,” Int. J. Nanomedicine, vol. 2, pp. 785-798, 2007
[5] J.L. Elechiguerra, J.L. Burt, J.R. Morones, A. Camacho-Bragado, X. Gao, H.H. Lara, and
M.J. Yamacan, “Interaction of silver nanoparticles with HIV-1,” J. Nanobiotechnology, vol.
3, 2005
[6] P.K. Jain, W. Huang, and M. A. El-Sayed, “On the Universal Scaling Behavior of the
Distance Decay of Plasmon Coupling in Metal Nanoparticles Pairs: A Plasmon Ruler
Equation,” Nano Lett., vol. 7, pp. 2080-2088, 2007
5
Modeling glioma multicellular spheroids with novel particle-based growth generation by
random walk model
Student: Benjamin Cote
Mentor: Mark DeCoster
Introduction
Glial cells are highly invasive, migratory cancer cells that follow well understood patterns of
general cell migration (Frieboes 2007, Mehta 2012, Swanson 2003). Computational models that
predict their behavior can be useful to fundamental research, diagnosis, and treatment.
Computational models, based on first order diffusion equations, have been developed to predict
the migratory behavior of these cells (Frieboes 2007). However, differences have been observed
between the results of these models and the spheroid shapes that are obtained in vitro (Mehta
2012). An alternative method, random walk modeling (RWM) is applied in this study. The
tumor shapes from this method are compared to those obtained through diffusion-based models
and to those obtained in vitro.
Methods
The RWM program was implemented in MATLAB. A three-dimensional space is divided into
small cubic volumes, where the height, width, and depth of each volume is one cell diameter.
The program initializes each spheroid’s size and location, and growth rate data are used to
determine the time for each cell division cycle. Each cell is considered to be in one of the small
cubic volumes. At each cell division, a new cell is generated, and its position is randomly
selected.
A growth cycle runs through six directions in a bound matrix to track the spheroids in digital
space and ensures that cells only grow once in a doubling through a second matrix. Three growth
cycles occur in the program when any spheroid is requested. An initializing cycle places the
single-celled locations of seeded spheroids. The monitored cycle collects data requested by the
user on each doubling. A death cycle determines whether the nutrients available to the cell are
low enough to cause cell death. Available nutrients are deduced from the distance of the cell to
the culture medium.
The desired number of cells was altered with each test. Measurements chosen for the experiment
were determined by parameters that could be measured experimentally or from the literature, and
one spheroid was set with each test. The measured outputs include the total number of cells, and
the diameters and areas of the formed spheroids.
Data extracted are the change in cell numbers with respect to doublings, change in cell growth,
and the sphericity of each tumor. The negative control is experimentally formed tumors in vivo
from literature, and the positive control is experimentally formed spheroids using previous
models. ANOVA testing was used for statistical analysis.
6
Results
Figure 4 shows a simulated tumor with a final cell count of 10,000. The top-left, top-right, and
bottom-left panels are projections along the 𝑧, 𝑥, and 𝑦, coordinates, respectively. The bottomright panel is an isometric view. No necrotic core was obtained for this small number of cells.
Figure 4: Spheroid with a final cell number of 10,000. The top left, top right, and bottom left panels
show projections along the 𝑧, 𝑥, and 𝑦 axes, respectively. The bottom right panel is the isometric view.
Scale bars are 50 microns. No necrotic core was formed in a spheroid of this size based on the algorithm
used to develop dead cells.
Figure 5 and Figure 6 show the tumor geometries for 100,000 cells and 1,000,000 cells,
respectively. The shape remains generally spherical, and a necrotic core forms. In each figure,
Panel A shows the entire tumor, and Panel B shows the necrotic core only.
7
Figure 5: Spheroid with final cell number of 100,000. The panels correspond to the same projection
directions as the corresponding panels in Figure 1. A is the whole spheroid, with 100 m scale bars; B is
the necrotic core with 50 m scale bars.
B
Figure 6: Spheroid with final cell number 1,000,000. The panels correspond to the same projection
directions as the corresponding panels in Figure 1. A is the whole spheroid, with 200 m scale bars; B is
the necrotic core with 100 m scale bars.
The desired cell counts used are 10,000, 100,000, 1,000,000, and 2,000,000. The spheroid
containing 2,000,000 cells produced the most data and was used for further analysis. ANOVA
testing of the data had a p-value of zero when tested with respect to doublings between trials, and
the p-value of the data when tested with respect to trials between doublings was one. Alpha was
0.05. The average sphericity of the images was .94 with standard deviation of .01.
Discussion
The sizes of the simulated spheroids agreed with those simulated by Frieboes (2007) and
Swanson (2003), but our simulations produced more spherical shapes. The spherical shapes
agree with in vivo data by Sánchez (2001).
8
The program can be made more, accurate in terms of cell numbers, by factoring in the addition
of nutrients This improvement will assist in developing reliable necrotic cores from the
simulation. Diffusion equations can be examined to provide nutrient formation in the model as
demonstrated in the literature (Mehta 2012, Venkatasubramanian 2006). Despite the simplicity
of the algorithm, the simulation requires substantial processor time (e.g. 17 hours per simulation
on an PC). However, the initial coding was not designed for speed, and small improvements can
radically improve efficiency.
Conclusion
Simulated spheroids show strong relationships with tumors and diffusion model spheroids. The
model is still relatively simple, but requires a large amount of processor time for a single
simulation. Even with limitations, the method has potential to become more reliable with further
research.
References
1. Frieboes H., Lowengrub J., Wise, S. Zheng X., Macklin P., Bearer E., Cristinia V. 2007
Computer simulation of glioma growth and morphology, NeuroImage 37 S59–S70.
2. Mehta G., Hsiao A., Ingram M., Luker G., Takayama S., 2012. Opportunities and
challenges for use of tumor spheroids as models to test drug delivery and efficacy.
Journal of Controlled Release.
3. Sánchez C, de Ceballos M., del Pulgar T., Rueda D., Corbacho C., Velasco G., GalveRopherh I., Huffman J., Ramón S., Guzmán M., 2001. Inhibition of Glioma Growth in
vivo by selective activation of the CB2 cannabinoid receptor. Cancer Research 61, 57845789.
4. Swanson K., Bridge C., Murray J.D., Alvord E., 2003. Virtual and real brain tumors:
using modeling to quantify glioma growth and invasion. Journal of Neurological
Sciences. 216, 1-10.
5. Venkatasubramanian R., Henson M., Forbes N., 2006. Incorporating energy metabolism
into a growth model of multicellular tumor spheroids. Journal of Theoretical Biology.
242, 440-453.
9
Microfluidic Design and Platelet Adhesion on Charged Substrates
Students: Josuha Kays and Max Henry
Mentor: Steven A. Jones
Introduction
Thrombosis-related diseases such as Deep Vein Thrombosis (DVT) and Pulmonary Embolisms
(PE) cause between 350,000-600,000 hospitalizations a year, including at least 100,000 deaths.1
The formation of a thrombus begins when the endothelium is damaged and blood is exposed to
both collagen and tissue factor released from the deteriorating endothelium cells. This exposure
activates platelets in the blood, and activation causes the platelets to stick and form a clot with
fibrinogen fibers.2 Platelet activation occurs quickly through a positive feedback mechanism that
causes other platelets to activate, while also being limited by platelet inhibitors such as Nitric
Oxide (NO) or L-arginine.3 Together, the interactions between the positive feedback and
negative feedback responses allow localized clotting to occur without coagulating all blood in
the body. However, if a thrombus forms and breaks off from the endothelium into the blood
stream, DVTs and PEs can occur and be life-threatening. Prevention of thrombosis depends on
understanding blood coagulation mechanisms and platelet adhesion dynamics. Though research
has been conducted on much of the above mechanisms in thrombus formation, there is still a
great need for a physical model of platelet adhesion in human blood vessels.
While much work has examined the relationship between a platelet’s local chemical environment
and its activation, it is expected that platelet activation will depend also on the history of the
platelet’s environment. In addition, it is expected that platelet activation and adhesion
downstream of a region of platelet activation will be enhanced by the agents that activated
platelets release. Experiments were therefore performed in which platelet-rich plasma flowed
over an interface between two different surfaces, to determine whether the direction of flow, and
hence a change in upstream/downstream surface configuration, altered the adhesion.
Methods
The microchannel design is shown in Figure 7. The LbL process is used to coat a glass slide
with adhesion proteins. The slide is then covered with a rubber gasket that provides cutouts that
act as flow channels. The inlets and outlets are provided through threaded holes that are drilled
in the upper Plexiglas cover. Flow is generated with a syringe pump. The entire assembly was
held together with a nylon spring clamp.
10
Figure 7: Microchannel design. A glass slide is covered with a silicone gasket into which channels are
cut. A Plexiglas cover interfaces the two inlets and two outlets for flow.
Dynamic LbL was conducted according to Lopez’s protocol4 for the first five polyelectrolyte
bilayers of poly(diallyldimethylammonium chloride) (PDDA) and poly(styrene sulfonate) (PSS).
This was followed by three bilayers of PDDA alternated with fibrinogen, where the solutiosn
were passed through the half covered silicone mask at 120 ml/hr. Between layers, the channels
were rinsed with phosphate-buffered saline for five minutes. Citrated whole bovine blood was
spun at 1000 g for 60 minutes in a Hermle Labnet Z 323 centrifuge with Histopaque 1083
gradient buffer. This PRP was diluted to 50% in PBS solution (pH 7.5) and 1 ml of filtered
acridine orange solution (1 mg/ml) was added, taking care to avoid light exposure. The full
length channel mask was then placed on the glass slide, and the pre-stained PRP was pushed
through at 20 ml/hr for 10 minutes, after which a 5% solution of glutaraldehyde in PBS was used
to fix the cells for 10 min, though this was later reduced to 2.5% after knowledge of
glutaraldehyde’s autofluorescence and ability to crosslink with fibrinogen was discovered. PBS
was used to rinse for 5 minutes and the slides were allowed to air dry at room temperature. All
slides were placed in aluminum foil-covered Petri dishes and refrigerated between 4-8° C when
not in use. In this setup, 4 slides (8 channels) were run with the PRP contacting the fibrinogen
side first, while another 4 slides had the PRP contacting the PDDA side first.
Slides were imaged in both FITC and TRITC filtered light with a 10x objective Olympus
microscope. Approximately 15 photos could be taken of each half of the channels, though care
was taken to avoid false positive “drying rings” and the inlets and outlets of the channels. Images
of the negative control – a layered slide before exposure to PRP – were also taken. The images
were converted to gray scale then processed through a threshold to remove background
fluorescence. These images were than analyzed with MATLAB to calculate percent surface
coverage and each half channel was averaged together. Individual slide averages and the sum of
slide averages were compared between fibrinogen and PDDA. Data collected for both directions
of flow was corrected by removing the background fluorescence as determined by the closedshutter control shots. Data points above and below three standard deviations were removed.
11
Results
Atomic force microscopy was used to examine the interface between the fibrinogen and PDDA
coating. The interface is readily visible in Figure 8 as a sudden change in the surface height.
Figure 8: AFM Hybrid d-LbL Fibrinogen/PDDA interface.
Figure 9 shows the percentage of surface covered by platelets (represented by Tritc
fluorescence), averaged over individual slides, for the case where flow crosses the fibrinogen
half of the slide first and then the PDDA half. Figure 10 shows the percentage of surface cover
for flow in the opposite direction.
Figure 9: Platelet surface coverage on individual slides for flow passing from fibrinogen to PDDA
12
Figure 10: Platelet surface coverage on individual slides for flow passing from PDDA to fibrinogen.
Figure 11 shows the average percent surface area coverage over all slides. Although a slight
decrease in adhesion is seen on the PDDA surface, as viewed through the Tritc filter, the
difference was not statistically significant.
2
Fibrinogen to PDDA
Fibrinogen
PDDA
3
Surface Area Coverage
(%)
Surface Area Coverage
(%)
3
2
1
PDDA to Fibrinogen
Fibrinogen
PDDA
1
0
0
TRITC
FITC
TRITC
FITC
Figure 11: Surface coverage averaged over all slides. Left: fibrinogen to PDDA. Right: PDDA
to fibrinogen.
Discussion
Although the mean value for surface coverage was smaller for PDDA than for fibrinogen, the
difference was not statistically significant. The lack of significance was unexpected because
fibrinogen is highly thrombogenic, yet PDDA’s thrombogenicity has not been noted in literature;
Jirouskova et al. stated that polymer charge has no effect on adhesion and generally causes mild
activation.5 However, this statement conflicts with previous work that indicated positively
charged vessel walls induce coagulation, and negatively charged walls prevent coagulation.6. It
is possible that other proteins in the PRP adsorbed to the PDDA surface, thus platelet adhesion
13
was not to PDDA but to an adsorbed surface on the PDDA. It is also possible that the Plexiglas
surface contributed to platelet activation7. It may be useful to coat the Plexiglas in future studies.
An F test demonstrated an increase variability between the forward and reverse flow slides. No
previous research has yielded this result.
Conclusion
The AFM results demonstrated that the method for construction of the two-component channel
surface was successful. No significant difference was found in platelet adhesion between the
two surfaces. Improvements in the experimental setup include further refinements to reduce
leakage, recalcification of the blood, and use of (negatively charged) PSS instead of (positively
charged) PDDA. Finally, tests should be conducted to see what (if any) proteins are adsorbing to
the biomaterial surfaces (PDDA or PSS).
References
1. Galson, S.K. “The Surgeon General’s Call to Action to Prevent Deep Vein Thrombosis
and Pulmonary Embolism,”
http://www.surgeongeneral.gov/library/calls/deepvein/index.html
2. Z.M. Ruggeri, (1997) "Mechanisms initiating platelet thrombus formation." Thromb
Haemost, vol. 78, no. 1, pp. 611-616, July 1997
3. Eshaq, R. (2006) “The Effect of the Local Concentrations of Nitric Oxide and ADP on
Platelet Adhesion and Thrombus Formation.” 2006, Master of Science Engineering
Thesis. Louisiana Tech University, Ruston, LA.
4. Lopez, J (2010) “An Improved Layer-By-Layer Self-Assembly Technique to Generate
Biointerfaces for Platelet Adhesion Studies: Dynamic LbL.” Dissertation, Doctor of
Philosophy, College of Engineering and Science, Louisiana Tech University.
5. Jirouskova, M., Bartunkova, J., and Smetana, K. (1997) "Comparative Study of Human
Monocyte and Platelet Adhesion to Hydrogels in Vitro – Effect of Polymer Structure."
Journal of Materials Science: Materials in Medicine.
6. Sawyer, P. and Srinivasan, S. (1972) “The role of electrochemical surface properties in
thrombosis at vascular interfaces: cumulative experience of studies in animals and man.”
Bull N Y Acad Med. 1972 February; 48(2): 235–256. (1997): SpringerLink.
7. Wang, Y. et al. "Effects of the Chemical Structure and the Surface Properties of
Polymeric Biomaterials on Their Biocompatibility." SpringerLink. Springer Science
Business Media, Aug. 2005.
14
The Effect of Growth Factors on Osteoblast Maturation in
HNT-Loaded Alginate Hydrogels
Student: Kanesha Hines
Mentor: David K. Mills
Introduction
Hydrogels are hydrated polymer materials that form beads under defined conditions [1]. They
can be synthetic or natural and are used to mimic the extracellular matrix in the body. They are
made up of hydrophilic polymer chains that crosslink to form the beads. Hydrogels are
biocompatible and easy to manufacture. Their composition is easily controlled and manipulated.
Alginate hydrogel is often used because of its low toxicity and its ability to gel. Its medical
applications include drug delivery, drug stabilization, and cell encapsulation. Hydrogels can be
loaded with cells and implanted in wounds to decrease healing time.
Halloysite nanotubes (HNTs) are used as nanocontainers for sustained drug release. HNTs are
hollow tubes made from the naturally occurring aluminosilicate. They can be loaded with a
diverse set of growth factors for sustained release [2]. In this study, Bone Morphogenic Proteins
(BMPs) 4 and 6 were loaded into halloysite nanotubes. BMPs are proteins that stimulate ectopic
bone growth and are members of the Transforming Growth Factor Beta (TGF-ß) superfamily.
BMP-4 is a protein that stimulates ectodermal tissue differentiation. BMP 6 induces osteoblast
differentiation in mesenchymal stem cells and subsequent osteogenesis. The effects of these
agents on osteoblasts, cells responsible for bone tissue formation during fracture repair, are
examined.
Methods
Preparation of the Alginate Beads
All of the hydrogels were made with 2% w/v sodium alginate and 1% w/v HNTs loaded with
BMP in saline. The HNTs were sterilized with 70% ethanol and vacuum loaded with BMP under
sterile conditions. The BMP 4 solution was prepared with 100 mM acetic acid. 100 ml was
removed and put into the BMP-4-labeled tubes and filled with 900 ml of water. 20 ml of the
1000 ml solution was then moved to another tube and 980 ml of water was added. For BMP-6, a
20 mM acetic acid solution was made. 20 µl was removed from the solution and put into the
BMP 6 tube and then transferred to another tube and 980 µl of water was added. For the BMP-4
and 6 tubes, 0.05 g of BMP 4 and BMP 6 doped HNTs were used
The final composition of each alginate bead was 0.2 g of alginate plus 0.1 g BMP doped HNTs
in 10 ml of NaCl. The osteoblasts were then added to each BMP solution. A 1% w/v calcium
chloride solution was made. 3- 12 well plates were labeled with the date of fixing and which
BMP solution was present. The calcium chloride solution was added to every well and the BMP
solutions were loaded into 27 g syringe and 6 drops were added to each well. The beads formed
immediately upon contact with the calcium chloride. The beads were left to solidify for 15
minutes. The calcium chloride was then removed by pipette and replaced with DMEM medium.
15
The plates were then placed in the incubator and cultured for 28 days with samples fixed on days
7, 14, 21, and 28.
Fixing the Samples
To fix the samples, 3 freezing tubes, 3 large vials, and 9 (3 for each plate) medium vials were
needed. Each tube was labeled with the assay and fixing date. The medium was removed from
all the well of the labeled fixing date and 2 ml of medium was placed in each of the BMP-4,
BMP-6, and BMP-4 and 6 tubes for freezing. Three beads from each BMP solution were placed
in large tubes with sodium citrate to dissolve the beads. The rest of the beads were placed in the
medium tubes based on BMP solution with ethanol. The beads were left in the ethanol for 10
minutes and then the ethanol was poured off. The freezing tubes were placed in the −80°C
freezer and the medium and large vials were placed in the 4 °C refrigerator. This process was
completed on day 7, 14, 21, and 28.
Staining
Fixed samples were stained for brightfield microscopy. The beads were placed in three, new 12well plates for the staining. Samples were stained with Von Kossa, Alcian Blue, and Picrosirus
Red following standard staining procedures. After staining the beads were cut with a blade, they
were mounted on microscopic slides. Mounting media and a cover slip was placed on each slide
and each slide was observed and photographed using an Olympus BX51 microscope for analysis.
Results
Von Kossa staining demonstrates the degree of mineralization. For all three combinations,
mineralization was low at Day 7 and increased at Days 14, 21, and 28, with nearly the entire
field mineralized at Day 28. BMP 4 showed the most mineralization, followed by BMP 6. In the
combination of BMP 4 and 6 there appeared to be a delay in the mineralization, but it followed
the same pattern of increasing over time.
Picrosirus red is an indicator of collagen. For each combination, the intensity of the stain
increased initially, but then decreased. The peak occurred at Day 14 for BMP 4 and at Day 21
for BMP 6 (Figure 12) and the BMP 4/BMP 6 combination.
Figure 12: Picosirius read staining for BMP 6. Left: Day 7. Right: Day 28.
16
Alcian blue stains acid glycosaminoglycans. At Day 7, staining was modest for BMP 4, stronger
for BMP 6, and intense for the BMP 4/BMP 6 combination. In all cases, staining increased at
Day 14. It peaked at Day 21 for BMP 4 and at Day 14 for BMP 6, the BMP 4/BMP 6
combination.
Discussion
The delay in mineralization for the combination of BMP 4/BMP 6 may occur because 4 and 6
provide different, potentially conflicting cell signals.
The picrosirus red staining suggest that collagen increases initially but becomes mineralized at
the later days, causing the decrease of the staining intensity. In BMP 4/BMP 6, delayed
mineralization leads to a decrease of stain intensity. Thus, the decreased staining intensity for
picosirius red mirrors the increased intensity for Von Kossa. The sulfated proteins that are
present in collagen explain the decreases in alcian blue intensity in all 3 combinations.
Conclusion
All growth factor combinations enhanced osteoblast differentiation, functionality, and
mineralization. BMP 4 led to the largest amount of bone mineralization. Future studies should
use ELISA to identity and quantify bone protein. Additionally, we need to find an alternative
method for the microscopy of hydrogels. The thickness of the gels degraded the microscopic
images.
References
1. Drury, Jeanie L. Mooney, David J. “Hydrogels for Tissue Engineering: Scaffold Design
Variables and Applications” Biomaterials. Vol 24. 2003 Pg. 4337-4351.
2. Ruiling Qi , Rui Guo , Mingwu Shen , Xueyan Cao , Leqiang Zhang , Jiajia Xu ,
Jianyong Yu and Xiangyang Shi, “Electrospun poly(lactic-co-glycolic acid)/halloysite
nanotube composite nanofibers for drug encapsulation and sustained release,” J. Mater.
Chem., 20, 10622-10629, 2010.
17
Effects of Triton X-100, pH, and Ball Milling on Piezoelectric Nanocoating
Student: Ivy Alexander
Mentor: Chad O’Neal
Introduction
Lead zirconate titanate (PZT) is a piezoelectric material that is frequently used for sensors. The
material produces an electric charge when it is stressed. Microcantilevers are frequently-used
components of sensors, where the bending of the cantilever translates to an angular change,
which can be detected optically. Alternatively, cantilevers can be coated with PZT in
nanoparticle form to provide a charged-based signal. The coating process requires the
nanoparticles to be dispersed in a solvent, and the quality of the coating depends on the content
and properties of that solvent. Without dispersants, PZT nanoparticles agglomerate in the
solution, leaving them clumped through Van der Waals forces and unevenly distributed .
Dispersant induces electrostatic forces around the particles that counteract Van der Waals forces.
Ball milling can produce smaller particles that improve the stability of the PZT particles in
solution. Smaller particles reduce the effects of gravitational forces and Van der Waals forces
while the improving electrostatic forces.
This study examines the effect of dispersant concentration, pH, and ball milling on PZT
nanocoatings.
Methods
The PZT powder was baked at 110 °C for 10 minutes to evaporate any water that could interfere
with the dispersant’s effectiveness, combined with the X-100 dispersant, and then added to the
solvent, a mock sol-gel solution of 4.405 mL 1-butanol and 0.595 mL propylene glycol . The
final dispersant concentrations were 2, 4, 6, 8, and 10 mg/mL. The thickness of the coating
layer, Hx, was then measured at 0.5, 1, 2, 4, 6, and 12 hours. After mixing, the colloidal mixture
of power, dispersant, and solvent settled from a top layer within a cuvette, and the height of the
lower layer (Hx) was measured as a function of time as a measure of the mixture’s stability.
Results
Effect of X-100 Surfactant Concentration
Figure 13 shows the normalized layer thickness for three concentrations of X-100 dispersant.
The thickness generally decreased (degraded) with time, and the most stable layer was obtained
for the highest dispersant concentration.
18
1
Hx/H0
0.8
2.0 g/ml
1.0 g/ml
0.5 g/ml
0.6
0.4
0.2
0
0
1
2
3
4
5
6
Time (Hours)
Figure 13: Normalized coating thickness as a function of time for three concentrations of X-100.
Effect of pH
The pH affects the electrostatic forces between particles. The pH was increased with 0.1 m/L
NaOH or decreased with 1 m/L HCl to obtain a range from 3 to 11. The height ratios at 4 and 6
hours are presented as a function of pH in Figure 14. Higher and lower pH values tended to
generate more stable layers, although the pH of 11 led to more degradation after 4 and 6 hours.
1
4 Hours
6 Hours
Hx/H0
0.95
0.9
0.85
0.8
0
2
4
6
8
10
12
pH
Figure 14: Height ratio as a function of pH.
Balling/DLS/SEM
The solvent, dispersant, and HCl were prepared in the ball milling canister, and the powders
were then added. Particle size was measured with direct light scattering at 0. 2, 4, 6, 8, 12, 18,
19
24, and 48 hours. Samples were taken from the bottom, wall, and edge of the canister to
examine the effect of canister position on particle size. SEM images were also taken.
Various balling times points were taken some with and other without ultrasonication. Based on
these readings, the average particle size, without any ball milling, fits into with the 200-800 nm
range. Trial 1 the particle size at hours appears to be in accurate as the average is almost 800 nm
while it is closer to 500 nm. This graph is based on all positionings (bottom, wall, edge)
averaged to together to look at trend between the two trials. Due the first reading that appear to
be almost opposite of each. Based on position trial one increased in particle size after 2 hours
due to such a lower starting particle size. Then almost staying the same size after 4 hr (expect for
the bottom). All decrease in particle size at 6 hr. Compared to trial 2 the opposite trends can be
seen at hours and 6 hr.
Ball milling did not consistently reduce particle size. It is suspected that the solution became
unstable during ball milling resulting in particle agglomeration. This theory is back by a study
done by Tralpho [1] which showed that excessive milling times led to aggregation of the
particles. Our SEM images demonstrated this phenomenon (Figure 15).
Figure 15: SEM images taken after 0 hours (left) and 6 hours (right) of ball milling.
Aggregation is observed at 6 hours.
Conclusions
Triton X-100 improved the stability of PZT solutions. Acidic and basic solutions tended to be
more stable. Ball milling did not consistently reduce the effective particle size because it tended
to lead to aggregation.
References
[1]
N. Traiphol, "Effects of ball milling time and dispersant concentration on properties of a
lead," Journal of Ceramic Processing Research. vol. 8, No. 2, pp. 137-141, 2007.
20
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Modified Silver Nanoparticles for Bacterial Infection Treatments
Students: Megan Livingston and Christopher Ramirez
Mentors: Dennis P. O’Neal and Sven Eklund
Introduction
Silver can act as an antimicrobial agent (Percival, 2005; Guzman, 2008). It has been infused into
polymers and surfaces to prevent the bacterial adhesion and colonization of implantable medical
devices (Furno, 2004). Silver nanoparticles (AgNPs) have been proposed as a direct treatment
for infection (Jena, 2012). For this application, the nanoparticles must be sized such that they are
cleared rapidly enough to prevent toxicity, yet slowly enough to provide time to affect
pathogens. AgNPs must also be modified with compounds, such as sodium dodecyl sulfate
(SDS) (Tillman, 2004), to prevent rapid degradation. This project investigated the manufacture,
identification, modification, and stability of modified AgNPs and examine their potential
oligodynamic effect on yeast cell cultures.
Methods
To generate the nanoparticles, 60 mL of 1 mM silver nitrate was mixed with 0.035g of SDS and
then reduced with 180 mL of 2 mM sodium borohydride (>99% purity, Sigma Aldrich) under 1
minute of heavy stirring. The suspension was then allowed to sit overnight, and was then
purified with a 10,000 mW dialysis cassette (Thermo Scientific). The DI water was replaced
every 3 hours, and then allowed to dialyze overnight. AgNPs were concentrated via a rotory
evaporator to bring the concentration to ~1 mM of Ag for 200. For the yeast cell cultures, the
solution was only purified and not concentrated.
To determine stability in the yeast cell media, a solution of 50% SDS AgNP and 50% media was
made and a UV-Vis spectrophotometer (Evolution 60, Thermo Scientific) was used to analyze
the nanoparticles. The growth media used was YPD (1% yeast extract, 2% glucose, 2% peptone)
with a pH of ~6.5-7.0.
To examine oligodynamics in yeast cell cultures, 100 µL saturated culture (Candida albicans
strain 3153) was added to 50 ml YPD. When the optical density was ~0, ten vials were each
filled with 4 mL of yeast cell culture solution. Five control vials were filled with a PBS solution
containing the same %wt of SDS as the original solution (~1.5 mg for 10 mL of PBS) in amounts
of 50, 100, 200, 500, and 1000 μL, respectively. The remaining five vials were filled with the
purified AgNP solution with the same volumes. Vials were placed in a shaker (Max Q 4000,
Barnstead) at 37 ⁰C for 2 hours. One mL of solution was then extracted from each of the ten
vials to measure the optical density. Another set of extractions and measurements was also taken
after 4 hours.
Results
SEM Images of SDS-AgNPs
The SEM images in Figure 1 confirm the synthesis of AgNPs of somewhat uniform distribution
of diameters between 5 and 15 nm (Figure 16). The small particle size is likely the result of
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SDS acting as a physical barrier to agglomeration of the particles to larger sizes. It is proposed
that NP size distribution might be controllable through changes in SDS concentration.
Figure 16: Scanning electron micrographs of AgNPs on a silicon substrate.
SDS-AgNP Stability in Media Over Time
The UV-Vis spectral data in Figure 17 demonstrates the stability of AgNPs in media over the
course of an hour. The height of the peak is directly related to the quantity of nanoparticles that
absorb light at that wavelength. The height of the peak declines at a decreasing rate, indicating
that in media, the SDS-AgNPs remain relatively stable for clinically relevant times.
1.4
Absorbance
1.2
0 min
30 min
60 min
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
Wavelength (nm)
Figure 17: Spectra of SDS AgNPs in media showing the stability with time
SDS-AgNP Stability in Phosphate Buffer Saline Solution
The spectral data in Figure 18 show the effect of phosphate buffered saline (PBS) on the SDSAgNP solution. Absorbance peaks at higher wavelengths are indicative of larger nanoparticles,
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while the height of the peak indicates quantity. At 0 hours, peaks exist for nanoparticles at 400
nm and 650 nm. The larger nanoparticles fall out of solution more rapidly, so the 650 nm peak
disappears. However, the smaller, more stable nanoparticles persist for a longer period of time,
even after exposure to a physiologic pH.
0.7
Absorbance
0.6
0 Hours
1 Hour
2 Hours
3 Hours
4 Hours
0.5
0.4
0.3
0.2
0.1
0
0
200
400
600
800
Wavelength (nm)
Figure 18: UV-Vis spectra of Ag-SDS NPs in PBS over four hours
Oligodynamics in Yeast Cell Cultures
Figure 19 shows the optical density for Candida albicans strain 3153 after 4 hours, as a function
of the added volume of AgNP solution. Higher optical densities indicate more bacteria. For the
control cases, an equal amount of polymer solution was added. The purified SDS-AgNP
solutions yielded lower optical densities than their control counterparts over four hours. This
indicates that aside from the SDS modifier, the Ag present in the solution may have yielded an
oligodynamic effect by lessening bacterial growth.
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0.8
Control Growth Ave.
Optical Density
0.7
AgNP Growth Ave.
0.6
0.5
0.4
0.3
0.2
0.1
0
1
2
3
4
5
Volume (L)
Figure 19: Averages of SDS-AgNPs and SDS-Control Optical Densities for Total Growth Time
Conclusion
SDS-AgNPs were stable in the culture media and in PBS, and they yielded an oligodynamic
effect at a physiologic pH in a protein-rich environment. However, the silver nanoparticles were
smaller than desired, and the effect of SDS concentration on particle size should be further
examined. The stability of the particles in blood must also be directly evaluated. Also, the
toxicity of SDS on humans needs to be examined.
Bibliography
Furno, Franck, Kelly Morely, Ben Wong, Barry Sharp, et al. "Silver nanoparticles and polymeric
medical devices: a new approach to prevention of infection?" Journal of Antimicrobial
Chemotherapy, 54, 6. <http://jac.oxfordjournals.org/content/54/6/1019.short>.
Guzman, Maribel, Jean Dille, and Stephan Godet. "Synthesis of Silver Nanoparticles by
Chemical Reduction Method and Their Antibacterial Activity," World Academy of Science,
Engineering and Technology, 43.
Jena, Prajna, Mohanty Soumitra, et al. "Toxicity and antibacterial assessment of chitosan coated
silver nanoparticles on human pathogens and macrophage cells," International Journal of
Nanomedicine, 2012, 7
Percival, S.L., P.G. Bowler, and D. Russell. "Bacterial Resistance to Silver in Wound
Care," Journal of Hospital Infection.
Tillmann, Patricia. Stability of Silver Nanoparticles in Aqueous and Organic Media. Howard
University, 2004. <http://www.nnin.org/doc/2004NNINreuTillmann.pdf>.
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