Simulation and
Experimental Studies of
Biomechanics at the
Micro-Scale
Elizabeth Nettleton, Chemistry
University of South Dakota
Faculty Mentor: Dr. William Tang and
Graduate Student Mentor: Gloria Yang
Biomedical Engineering
University of California, Irvine
-1-
Abstract
Micro-structured biomechanical devices, specifically sensors, offer great potential in the
medical field. For instance, bone strain sensors may possess the capability to provide
information on disease progression in the cases of bone tumors, osteoporosis, and the like.
Prosthetic heart valves offer a new life to patients, but as the valves age, they weaken,
threatening a fatal breakage. By adhering a micro-sensor for measuring strain to the
prosthetic tissue, one could potentially track the weakening of the valve and consequently
design a better prosthesis based upon acquired real-time data. Multiple Biomedical
Engineering labs have worked on projects involving bone strain sensors, but the heart valve
project is unique. This Micro-Biomechanics Lab currently pursues both projects. They are
multi-faceted problems involving issues such as device characterization using computer
modeling and physical experiments and the testing of biological adhesives and their effects
on the biological tissue. Also, further modeling can predict the tolerable level of heat
dissipated from the sensor electronics to avoid thermal damage to surrounding tissue. Use of
the MEMS module of COMSOL Multiphysics provided simulations modeling the spring
constants of the cantilever sensors which will ultimately be used to map the compliances of
the heart valve. A Wheatstone bridge provided the means by which to calibrate the fabricated
device by tracking the relative change in resistance with respect to the cantilever deflections.
Later, the simulated values will be compared to data collected taking physical measurements
with the fabricated device. Ongoing work involves the evaluation biological adhesives,
modeling the resonant frequencies of the device, and performing COMSOL simulations to
map the heat transfer from the sensor into biological tissues.
-2-
Key Terms
Bioadhesive
Biomechanics
Cantilever Sensor
Piezoresitivity
Strain Gauge
Figure 1: Cantilever Sensor
Figure 2: Strain Gauge
Wheatstone Bridge
Introduction
Biomechanics, the application of physics, engineering, and the like to describe the body in
motion, offers multiple applications in the healthcare world. Two such applications lie in
the use of strain sensors to map heart valve compliances and measure strain in bone. With
the successful mapping of a heart valve, a more complete understanding of the valve will be
reached, and researchers may be able to complete projects such as designing a better
prosthesis. Bone strain sensors offer great hope in the tracking of disease progression. For
instance, bone tumors and osteoporosis lead to a weakened skeletal system, and consequently,
more strain exists in affected bones. Thus, by measuring the strain a diseased bone
experiences, one may be able to predict the disease’s progression and the bone’s progress in
healing and regeneration.
This lab has previously designed two sensors, a cantilever sensor and a strain gauge, to
perform the heart tissue mapping and the bone strain measurements, respectively. See
Figures 1 and 2 (above).
Both sensors are constructed from a piezoresistive material, gold,
such that their resistances change as the device experiences strain.
The lab has evaluated its
strain gauge and published material based upon its success (Yang et al., 2004 and Yang et al.,
-3-
2005). The work with the heart valve, however, is new and the sensor is still in its
development and characterization stages. Previous studies have begun to explore the
characterization of aortic heart valve tissue in the hopes of designing a better prosthesis
(Mirnajafi et al., 2003 and Mirnajafi et al., 2004).
This lab hopes to further the research
presented in these studies.
Specifically, this project will consist of three primary and distinct sections.
The first will
focus on simulations using the COMSOL Multiphysics modeling program.
The program
will be used to model the spring constant and resonant frequencies of the cantilever sensor
within the cantilever device. Previous studies have shown that implanted devices have the
capability of causing a non-negligible temperature rise in the body (Lazzi 2005). Therefore,
COMSOL will also be used to ensure that the strain gauge sensor, when implanted in
biological tissue, will not cause the tissue temperature to rise above a safe level.
This lab
has estimated that thermal damage will occur with a temperature increase above 1 °C.
The second portion of this study will focus on utilizing a probe station to characterize the
cantilever device. The device’s change in resistance with sequential cantilever beam
displacement will be monitored. Two different methods of measurement will be
studied—the first using a digital multi-meter to measure changing resistance directly, the
second utilizing a Wheatstone bridge to indirectly measure the changing resistance.
In a
Wheatstone bridge, four resistors are used—three known, and one, the cantilever, is not.
A
changing peak to peak voltage through the circuit is caused as the cantilever is displaced.
This voltage change will be related to changing resistance. Following the data collection
will be analysis. Each data set will be graphed and several best-fit lines will be generated in
-4-
Excel.
Both the methods and analysis techniques will be compared in order to determine the
most effective method.
Finally, the third part of the experiment will focus on the research and evaluation of
bioadhesives. The adhesives will be used to bond the devices to biological tissue and
therefore much be biocompatible and allow faithful transmission of surface tension to the
sensor. This data, as well as all other findings of this project, will be used in the future to
assist the Tang Bio-Micromechanics lab in their design and implementation of
micro-structured sensors.
Methods and Materials
COMSOL Simulations
Computer simulations in this project were performed using COMSOL Multiphysics 3.2.
Several simulation types were addressed and some are still works in progress. The first
involved the modeling of the cantilever sensor of the heart valve device in order to find its
spring constant. The second also involves the modeling of the cantilever sensor, this time to
find its resonant frequency. The third and final simulation models a strain gauge implanted in
biological tissue, on bone beneath muscle. This simulation will be used to model the heat
transfer from the device to the tissue in order to ensure its safety.
The simulation used to find the spring constant of the cantilever sensor was performed in the
3D MEMS Module: Solid, Stress-Strain application. The beam was modeled from SU-8,
Young’s Modulus 4.02 GPa and Poisson’s Ration 0.11. Four beam lengths were modeled, 500
μm, 700μm, 1000μm, and 1200μm. Each length had a width of 108μm and thickness 15
μm. One end of the beam was fixed while masses varying in 2 mg increments from 1mg to
-5-
15 mg were applied to the opposite end. The forces generated by these masses resulted in a
maximum displacement
in each simulation (See
Figure 3). This
maximum displacement,
when used with the
force data, was used to
calculate the spring
constant of the
cantilever using the
following equation:
k
F
x
(1)
Where k is the calculated spring constant (N/m), F is the applied force (N), and x is the
maximum displacement (m). These simulated spring constants were then compared to
Figure 3; Force and Displacement
theoretical values calculated using the following equation:
Ewt 3
(2)
k
4l 3
Where k, again, is the spring constant, E is the effective Young’s modulus (see Equation (3))
of the material (Pa), w is the width of the beam (m), t is the thickness of the beam (m), and l
is its length (m).
E
Y
(3)
1 p2
Where E is the effective Young’s Modulus, Y is its bulk Young’s modulus and p is the
material’s Poisson’s ratio.
-6-
The second simulation was also modeled in the MEMS Module Solid, Stress-Strain
application and the modeled beam dimensions were the same. It was, however, drawn first in
2D and then
underwent a mesh
extrusion to create
the 3D model (See
Figure 4). An
eigenfrequency
analysis was then
Figure 4: Eigenfrequency Analysis
performed without
a load on the
cantilever. This
simulation requires much ongoing effort. Currently frequencies simulated by COMSOL do
not coincide with theoretical values generated by the following equations:
fn 
EI
m
2
2L
n2
(4)
 n  1.87510407, 4.69409113, 7.85475744, 10.99554073, 14.13716839, 20.4203521
Where f is the frequency, n is the mode, βis one of the given values, E is the Effective
Young’s Modulus (Pa), I is the moment of inertia, m is the mass (kg), and L is the length (m)
of the beam.
The final COMSOL model involves the modeling of the other device—the bone strain sensor.
This model requires the use of two 3D COMSOL applications: the MEMS module
Conductive Media DC application as well as the COMSOL Multiphysics module Heat
Transfer through Conduction application.
-7-
This model was drawn by creating a rectangle of length 0.1m, width 0.5m, and thickness
0.0281 m to represent a bone. On top of this bone the device was modeled. Its base is a
rectangle of Parylene C of length 3cm, width 2.8cm, and thickness 50μm. A gold coil of
thickness 200μm represented by a square with sides 25mm and centered square hole with
sides 7mm lies atop the Parylene C. Within the hole, a square gold sensor with sides 2mm
and thickness 0.1μm and a
square silicon chip with
sides 2mm and thickness
Figure 5: Bone Strain Sensor Model
250μm are located. Atop
the device, two rectangles
with lengths and widths
equivalent to the bone, and
thicknesses 0.015m and 0.0025m represent muscle and skin, respectively. See Figure 5 for a
representation of the model.
Figure 5: Heat Transfer Model
The following tables contain the physical properties imputed into COMSOL for each of the
materials at varying powers in order to complete the models:
Table 1
Constant Material Properties
Electrical Conductivity (S/m)
Thermal Conductivity (W/mK)
Density (kg/m^3)
Heat Capacity (J/(Kg°C)
Parylene C
2.00E-04
0.082
1289
711.79
Gold
4.10E+07
60
19320
128
Silicon
0.100
157
2329
700
Bone
0.0285
0.300
1810
1300
Muscle
0.5476
0.498
1040
3600
Skin
0.0371
0.42
1010
3500
-8-
Table 2
Varying Material Properties
Coil
Sensor
Heat
Power
Resistance Current Density Source Resistance
(mW)
(Ohm)
(A/m^2)
(W/m^3)
(Ohm)
10
8.68E-04
7.669E+05
1.563E+05
2160
Current
Density
(A/m^2)
8.784E+06
Chip
Heat Source Resistance
(W/m^3)
(Ohm)
1.667E+10
1.00E+08
Current
Density
(A/m^2)
1.155E+01
Heat
Source
(W/m^3)
3.333E+06
20
8.68E-04
1.085E+06
3.125E+05
2160
1.242E+07
3.333E+10
1.00E+08
1.633E+01
6.667E+06
30
8.68E-04
1.328E+06
4.688E+05
2160
1.521E+07
5.000E+10
1.00E+08
2.000E+01
1.000E+07
40
8.68E-04
1.534E+06
6.250E+05
2160
1.757E+07
6.667E+10
1.00E+08
2.309E+01
1.333E+07
50
8.68E-04
1.715E+06
7.813E+05
2160
1.964E+07
8.333E+10
1.00E+08
2.582E+01
1.667E+07
60
8.68E-04
1.878E+06
9.375E+05
2160
2.152E+07
1.000E+11
1.00E+08
2.828E+01
2.000E+07
70
8.68E-04
2.029E+06
1.094E+06
2160
2.324E+07
1.167E+11
1.00E+08
3.055E+01
2.333E+07
80
8.68E-04
2.169E+06
1.250E+06
2160
2.485E+07
1.333E+11
1.00E+08
3.266E+01
2.667E+07
90
8.68E-04
2.301E+06
1.406E+06
2160
2.635E+07
1.500E+11
1.00E+08
3.464E+01
3.000E+07
100
8.68E-04
2.425E+06
1.563E+06
2160
2.778E+07
1.667E+11
1.00E+08
3.651E+01
3.333E+07
This project, like the previous, is an ongoing endeavor. To finish the modeling, first the
Conductive Media DC model must be completed, and, from there, running the Heat Transfer
through Conductive application will be possible. If the surrounding biological tissue does not
experience a temperature increase greater than 1 °C, the device is safe for in vivo use.
Probe Station Measurements
In order to characterize our device and its response to cantilever displacement, several trials
utilizing two techniques were performed using a probe station. In both methods, the device
was placed on a stage and a microscope and probe were used to displace the cantilever beam
275 μm in 5 μm increments. After each displacement, measurements were obtained. The first
technique allowed one to measure the changing resistance of the device directly using a
digital multimeter. The multimeter connected directly to the device and as the beam of the
device was displaced, the multimeter provided information on the device’s changing
resistance, in ohms.
-9-
The second method necessitated the use of a Wheatstone bridge within a circuit and the
measurement of
changing peak to
BPF
peak voltage
following each
displacement. This
Figure 6: Wheatstone Bridge Circuit
apparatus consisted of far more components than did the previous. See Figure 6 for schematic
of the circuit used. It contained three discrete resistors (R1, R3, R4), a cantilever sensor (R2),
amplifiers (A), and a band pass filter (BPF).
In addition to the circuit, a power supply, function generator, oscilloscope, and digital
multimeter were also used. The digital multimeter was used to measure the initial resistance
of the device prior to each the trial. The power supply provided a potential of 15 Volts to the
circuit. The function generator output an alternating voltage with peak-to-peak voltage of 200
mV at a frequency of 1.00 KHz. The oscilloscope provided graphs of the input voltage and
the output peak-to-peak voltage, which increased as the cantilever sensor was displaced. The
following equation was then used to relate changes in peak to peak voltage to changing
resistance:
R 
(0.5  V
V)
(0.5  V )
V
R  R0 (5)
Where ΔR is the changing resistance, ΔV is the changing peak to peak voltage, V is the
input voltage, R is the resistance of the other three resistors and R0 is the initial resistance of
the cantilever beam.
- 10 -
Following eight trials from each technique, the data from each set was entered into Microsoft
Excel and graphs were generated graphing changing resistance against displacement. Several
lines of best fits were generated for both data sets—one that forced the line through zero, one
that did not, and one that did not include the first data point at all. The slopes of each of these
graphs were analyzed and the slopes were analyzed by calculating their average, extrema, and
standard deviation. In such a manner, the techniques could be compared in order to quantify
the device’s behavior as well determine the more precise method of data collection.
Adhesive Research and Testing
After researching several biological adhesives, multiple adhesive samples were requested.
Due to lack of Federal Drug Administration approval and other issues, some requested
adhesives were unavailable to
Table 3: Adhesive Requests
Adhesive Requests Status
research labs, but as of
present, two samples have
been obtained—Ethicon’s
Dermabond™ and BD
Biosciences’ Cell-Tak™.
Requests, both for samples
Company
Adhesive
Baxter Healthcare Corp
Tisseel VH Fibrin Sealant
BD Biosciences
Cell-Tak
Cohesion Technologies Inc
CoSeal Hyrdrogel
Cohesion Technologies Inc CoStasis Hemostat Spray
Cryolife Inc
Bioglue
Cryolife Inc
FibRX
Johnson & Johnson/Ethicon
Dermabond
Microval
Plasmaseal
GRFG/GRF
Plasmaseal
Response
Considering Purchase
Arrived-Awaiting Testing
Samples Unavailable
Samples Unavailable
Awaiting Board Decision
Samples Unavailable
Currently Testing
No FDA Approval
Unsuitable for Project
and for product prices, are still being sought in the cases of other potential bioadhesives. See
Table 3 for a summary of adhesive requests.
Dermabond™, one potentially effective bioadhesive, is currently being evaluated. According
to Ethicon representatives, Dermabond is one of two topical skin adhesives holding FDA
approval. It is an octylcyanoacrylate giving the adhesive the ability to flex with tissue
movement. Also because of its octyl molecular geometry, the adhesive does not dry
immediately. The user will have approximately 30 seconds of working time. Finally, the
- 11 -
adhesive will remain intact until the tissue to which it is attached begins to slough. All of
these traits make it a promising adhesive worthy of investigation.
Although Dermabond™ is currently the only adhesive on which tests are being performed,
several tests will be used to evaluate all received bioadhesives for their effectiveness. As for
current testing, Dermabond™ was used to adhere a prototype of the bone strain sensor
mounted on PDMS to a foam block. The block is being photographed and monitored for
seven days, the length of a typical experimental study. Other tests that will ultimately be
performed on this and other adhesives include testing the adhesion of a strain gauge to a bone
after soaking it in a saline-based solution and measuring changes in the mechanical properties
of heart valves after application..
Results
COMSOL Simulations
In the simulation of
COMSOL Simulation-Force vs Displacement
varying forces placed on
1.600E-07
the cantilever of the
were obtained. The
y = 1.0698x - 1E-11
y = 0.3668x + 3E-12
1.200E-07
Force (N)
device, displacements
y = 2.9288x + 6E-10
1.400E-07
y = 0.2122x + 3E-12
1.000E-07
8.000E-08
6.000E-08
4.000E-08
following graph (Figure 7)
2.000E-08
0.000E+00
generated in Excel plots
force versus displacement
0.000E+00
1.000E-07
2.000E-07
3.000E-07
4.000E-07
5.000E-07
6.000E-07
7.000E-07
Displacement (m)
500 Micrometers
700 Micrometers
1000 Micrometers
1200 Micrometers
Linear (1200 Micrometers)
Linear (1000 Micrometers)
Linear (1000 Micrometers)
Linear (700 Micrometers)
Linear (500 Micrometers)
and the resulting slopes
Figure 7: COMSOL Results
are equivalent to the spring constant of the device.
- 12 -
The theoretical spring constants generated by COMSOL were then compared to the
theoretical results obtained using the above Equation (1). The percent difference between the
two methods of obtaining
Table 4: Data Comparison
Comparing Theoretical and Calculated Spring Constants (N/m)
theoretical data was always less
Theoretical Value Simulated Value Percent Difference
than 1.311% (See Table 4).
The theoretical data obtained will
later be compared to the actual
500 μm
700 μm
1000 μm
1200 μm
2.967
1.081
0.371
0.215
2.9286
1.0698
0.3668
0.2122
1.303%
1.041%
1.139%
1.311%
properties of the device.
The COMSOL simulation addressing the resonant frequency of the cantilever within the
device as well as the one modeling the heat transfer from an implanted device into biological
tissues require further attention. Neither has produced acceptable results as of yet, but
through ongoing work in the laboratory, both should be completed soon. Similarly to the
theoretical spring constants, the resonant frequency results will ultimately be compared to the
actual properties of the device. Through the heat transfer model, the lab hopes to verify that
surrounding biological tissue will not suffer from thermal damage as a result of an implanted
device.
Probe Station Measurements
Using the digital multimeter, eight data sets were collected, measuring changing resistance
directly. The data sets were entered into Microsoft Excel and several graphs were generated
in Microsoft Excel plotting changing resistance against displacement. Then, lines of best fit
were generated three different ways. The first forced the line of best fit through zero while the
second did not, and the third eliminated the first data point completely (See Figures 8-10).
Analysis of the data was also obtained, finding the average, extrema, and standard deviation
of the slopes of the lines of best fit (See Table 5).
- 13 -
B4-Delta R (MM)
0.07
Table 5: Multimeter Data Analysis
Analysis of Delta R Graphs (Multimeter)
0.06
0.05
Delta R (ohms)
Graph: B4-Delta R: Forced Through Zero
Slopes
2.417E-04
2.333E-04
2.263E-04
2.160E-04
2.149E-04
2.210E-04
2.196E-04
2.005E-04
Average
Standard Deviation
Maximum
Minimum
2.217E-04
1.247E-05
2.417E-04
2.005E-04
0.04
0.03
0.02
0.01
0
0
50
100
150
200
250
300
Displacement (micrometers)
Linear (Trial One)
Linear (Trial Two)
Linear (Trial Three)
Linear (Trial Four)
Linear (Trial Five)
Linear (Trial Six)
Linear (Trial Seven)
Linear (Trial Eight)
Graph: B4-Delta R: Not Forced Through Zero
Figure 8: Forced through Zero
B4-Delta R (MM)
Slopes
0.07
2.215E-04
Average
2.130E-04
2.345E-04
Standard Deviation
1.431E-05
2.150E-04
2.133E-04
Maximum
Minimum
2.345E-04
1.836E-04
0.06
0.05
2.078E-04
Delta R (ohms)
0.04
2.130E-04
2.150E-04
1.836E-04
0.03
0.02
Graph: B4-Delta R: First Data Point Eliminated
0.01
0
0
Slopes
2.350E-04
2.131E-04
2.103E-04
2.122E-04
2.049E-04
2.097E-04
2.130E-04
Average
Standard Deviation
Maximum
Minimum
50
100
150
200
250
300
-0.01
2.093E-04
1.601E-05
2.350E-04
1.765E-04
Displacement (micrometers)
Linear (Trial One)
Linear (Trial Two)
Linear (Trial Three)
Linear (Trial Four)
Linear (Trial Five)
Linear (Trial Six)
Linear (Trial Seven)
Linear (Trial Eight)
Figure 9: Not Forced through Zero
B4-Delta R (MM)
1.765E-04
0.07
0.06
The Wheatstone bridge data was
Delta R (ohms)
0.05
evaluated in a similar manner, with one
exception. The data collected using the
0.04
0.03
0.02
circuit and Wheatstone bridge relayed
0.01
changing peak to peak voltage but in order
0
0
50
100
150
200
250
Displacement (micrometers)
to compare the two methods,
information on changing resistance was preferable.
- 14 -
Linear (Trial One)
Linear (Trial Two)
Linear (Trial Three)
Linear (Trial Four)
Linear (Trial Five)
Linear (Trial Six)
Linear (Trial Seven)
Linear (Trial Eight)
Figure 10: Eliminate 1st Data Point
300
Thus, all data had to be converted using the above Equation 5 before graphing it in Excel,
adding lines of best fit (See Figures 11-13), and analyzing the slopes (See Table 6).
B4- Delta R (Wheatstone Bridge)
Table 6: Wheatstone Bridge
Data Analysis
Graph: B4-Delta R: Forced
Through Zero
Delta R
Analysis of Delta R Graphs
(Wheatstone Bridge)
0
. 1
2
0
0
0
. 1
0
0
0
0
. 0
8
0
0
0
. 0
6
0
0
0
. 0
4
0
0
0
. 0
2
0
0
Slopes
2.483E-04
2.291E-04
2.396E-04
3.095E-04
3.527E-04
3.202E-04
2.695E-04
2.633E-04
Average
Standard
Deviation
Maximum
Minimum
2.790E-04
4.372E-05
0.0000
0
3.527E-04
2.291E-04
5
0
1
0
0
1
5
0
2
0
0
2
5
0
3
0
0
Displacement
Linear (Trial 1)
Linear (Trial 2)
Linear (Trial 3)
Linear (Trial 4)
Linear (Trial 5)
Linear (Trial 6)
Linear (Trial 7)
Linear (Trial 8)
B4- Delta R (Wheatstone Bridge)
Figure 11: Forced Through Zero
Graph: B4-Delta R: Not Forced
Through Zero
0
. 1
0
0
0
0
. 0
8
0
0
0
. 0
6
0
0
0
. 0
4
0
0
0
. 0
2
0
0
0
. 0
0
0
0
Average
Standard
Deviation
2.590E-04
2.935E-04
Maximum
3.312E-04
2.101E-04
3.105E-04
3.312E-04
2.399E-04
2.567E-04
Minimum
1.922E-04
1.922E-04
Delta R
Slopes
2.378E-04
4.889E-05
0
1
5
0
2
0
0
2
5
0
3
0
0
Average
Standard
Deviation
Maximum
Minimum
2.506E-04
5.267E-05
3.357E-04
1.768E-04
Linear (Trial 1)
Linear (Trial 2)
Linear (Trial 3)
Linear (Trial 4)
Linear (Trial 5)
Linear (Trial 6)
Linear (Trial 7)
Linear (Trial 8)
3
0
0
B4- Delta R (Wheatstone Bridge)
0
. 1
0
0
0
0
. 0
9
0
0
0
. 0
8
0
0
0
. 0
7
0
0
0
. 0
6
0
0
0
. 0
5
0
0
0
. 0
4
0
0
3.357E-04
0
. 0
3
0
0
2.275E-04
0
. 0
2
0
0
0
. 0
1
0
0
2.539E-04
0
Figure 12: Not Forced Through Zero
Delta R
1.977E-04
2.869E-04
2.928E-04
1
Displacement
Slopes
1.768E-04
0
-0.0200
Graph: B4-Delta R: First Data
Point Eliminated
2.335E-04
5
0
0.0000
0
5
0
1
0
0
1
5
0
2
0
0
2
5
0
Displacement
Linear (Trial 1)
Linear (Trial 2)
Linear (Trial 3)
Linear (Trial 4)
Linear (Trial 5)
Linear (Trial 6)
Linear (Trial 7)
Linear (Trial 8)
Figure 10: Eliminate 1st Data Point
- 15 -
Adhesive Research and Testing
As of yet, the adherence of the sensor prototype to the foam block with Dermabond™
appears fairly strong strong. Over the course of seven days it visibly loosened only slightly.
When force was applied, the strain sensor could be pulled off, but would not have fallen off
of its own accord. Further data evaluating the adherence integrity from this and other tests
will be forthcoming in the cases of Dermabond™, Cell-Tak™ , and other adhesives yet to be
obtained.
Discussion
Overall, this project can be considered fairly successful. Although a significant number of
tangible results may not have been produced, progress certainly did occur in each of the three
overarching divisions of the study—COMSOL simulations, probe station measurements, and
adhesive research and testing.
By comparing theoretical spring constant data to spring constants found using COMSOL it
was proven that successful simulations modeling the displacement caused by applied forces
were generated. This data provides mean by which the device can later be evaluated. By
comparing actual data to these theoretical data, inherent error will be assessed. Unfortunately,
similar data could not be produced using COMSOL to find the resonant frequency of the
device. Successful simulation did not occur, and although assistance from product support
was sought, the matter requires further research in order to produce meaningful results.
Finally, the simulations modeling heat transfer from the bone strain sensor into biological
tissue also require further time. Generating the geometry and finding material properties,
significant portions of the simulation, were completed, but the project was concluded before
the simulations could be completed. Because determining the degree of potential thermal
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damage as a result of implanted devices is essential, other researchers in the laboratory will
likely finish the work this study began.
The probe station measurements portion of the experiment produced a bit of useful
information. First, it can be noted that there exists a correlation in the data produced by the
digital multimeter and Wheatstone bridge regarding resistance changes as a result of
cantilever displacement. The values are similar. It can also be noted that the standard
deviations of the results produced by the multimeter are significantly lower than those
produced by the Wheatstone bridge, indicating a problem in the procedure. One possible
cause of this elevated standard deviation is the fact that an initial resistance measurement was
not taken before each data set in the Wheatstone bridge measurements. Instead, the initial
resistances in the multimeter trials were simply averaged and this value was assumed to be
the initial resistance of the device in the Wheatstone bridge studies. In the future, this lab will
perform similar studies modifying the procedure such that an initial resistance measurement
is taken. Lastly, through the probe station data analysis, it was discovered that in both data
sets the standard deviation for the slopes produced for the line of best fit forced through zero
is significantly lower than the other options. In accordance with this finding, future probe
station data produced by this lab will likely be analyzed in this manner only.
By researching bioadhesives, this study first provided the lab with products which to pursue
as well as requested samples and price information. This information will be valuable in the
future. Additionally, initial testing on Dermabond™ indicates that it may be a potential
bioadhesive in future studies. Further testing is necessary on this and other bioadhesives,
however, in order to ensure this statement.
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Again, this study produced results that will contribute to the ultimate goals of this laboratory.
Through COMSOL modeling, it provided a way in which future devices may be evaluated as
well as began models that can be completed for future use. Through measurements made
using the probe station and consequent data analysis, it discovered the need for procedure
refinement in addition to providing information on effective ways to evaluate future data.
Finally, in the adhesive evaluation and research, the study provided information to the lab
which will be utilized both in future product requests and testing. All of these results
contribute to the larger projects currently being pursued by the Tang Micro-Biomechanics
Laboratory.
Acknowledgements
I would like to express my gratitude to my mentor, William Tang, graduate student, Gloria
Yang, and the Tang Lab as a whole for your roles in this project. I could not have completed
anything without your guidance and understanding. Also, thank you to the National Science
Foundation, the University of California, Irvine, and the UROP-IMSURE program for
supporting this project.
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