BioMEMS Microfluidics Lab Analysis using COMSOL

1. Bioengineering 494
Microfluidic Micromixing.
A Tale of Two Processes:
Simulation v. Experiment
University of Illinois - Chicago
Kathleen Broughton
Spring 2011

2. I. Introduction
Biological MicroElectroMechanical Systems (BioMEMS) began to spark interest in the
research community not long after MEMS [1]. In 2003, the BioMEMS industry had projected
revenues of $850 million, which was expected to grow to over $1Billion by 2006 [1].
Microfluidics is a type of BioMEMS device primarily used to organize and measure liquid flow
at the micron scale. Throughout the spring semester, the microfluidic class provided an
opportunity to learn about the theory and practical application of microfluidic BioMEMS
devices. In theory classes, a focus was paid to the reviewing the engineering and physics
fundamentals related to MEMS / BioMEMS. In practical application, the course focus was on
designing and running simulation models of a microfluidic micromixer and then converting,
building and testing a micromixer in a laboratory setting. These applications, simulations and
experiments, were then comparatively analyzed. This paper will outline the course and
microfluidic micromixer project in terms of theory, simulation, fabrication, experimentation and
comparative data analysis.
II. Background
a. Microfluidics
Microfluidics is based on the technology application “of systems that process or manipulate
small ( liters) amounts of fluids, using channels with dimension of tens to
hundreds of micrometers” [2]. BioMEMS microfluidics has four umbrella tiers that contribute to
why it is pursued in research: molecular analysis, biodefense, molecular biology and
microelectronics and each tier has a particular function based on a unique design consisting of
structures such as valves, mixers and pumps [2]. Although the field has many successes in the
research community, the new challenge for researchers is i how to transform more of these
„microfactories‟ into tangible, commercial products.
b. Micromixers
Microfluidic systems have a unique environment with the mixing of fluids. One of the unique
effects of a mixer on a micro scale “is that fluid properties become increasingly controlled by
viscous forces rather than inertial forces” [3]. This means that flow is laminar, rather than
turbulent, with Reynolds numbers typically and mixing occurs through diffusion, instead
of a convective, process [3]. Another characteristic of the micromixer is that the “channel walls
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3. impart shear forces on the contained fluid, so under applied hydrodynamic pressure a parabolic
velocity profile is established over the cross-section with fluid velocity zero at channel walls and
maximum at the center” [3]. This also impacts the final mixing of the fluids. In response to the
fluid dynamic principles of micromixers, a number of mixer designs have been experimented
with in order to find optimized mixing end-product.
III. Methods
In this project, the ultimate goal was to design a micromixer that could be simulated in a
computer lab and fabricated in a laboratory experiment capacity, whereas a linescan analysis
would be performed at similar outlet positions and quantitatively compared against each other.
Fringe goals from the project ranged from learning and understanding how to use Comsol
Multiphysics as well as properly analyzing data between a simulation and an experiment.
Parameters used for the micromixer design included a mixer length of five millimeters by
200 microns width and height of 50 microns. An additional caveat was that no dimension within
the micromixer could be less than 50 microns, which was due to fabrication limitations based on
the materials and process used to fabricate the experimental design micromixer.
a. Simulation
Comsol Multiphysics is a simulation software program one can use to design and run
theoretical trials for different micromixers. The program allows a person to have endless
opportunities to design and run quick analysis to determine if the design meets the goals and
desired outcome. The request pertaining to simulations was to (1) learn the software, (2) practice
creating a few different designs, (3) after choosing one design, simulate five variations to the
design and decide on one final design to fabricate. To learn and practice creating different
designs for simulation, a lab manual was provided in the course to provide specific but general,
basic information of how to create a micromixer and gain data for analysis in comparing the
simulation to actual experimental results.
b. Experimental
i. Fabrication
The fabrication used for the micromixers is based on a general photolithography technique
followed by MEMS scientists. In this process, SU 2050 is first spun onto a silicon wafer chip to
create an approximately 50 micron high layer. The chip is then placed on a hot plate at 65 °C for
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4. three minutes, 95 °C for six and a half minutes, 65 °C for one minute and left to cool at room
temperature for at least five minutes. The chip is then exposed to a 175 mJ/cm2 UV light with a
mask designed to the micromixer specification. The chip then undergoes a hard back at 65 °C for
one and a half minutes, 95 °C for six and a half minutes, 65 °C for one minute and then left to
cool at room temperature. The chip is placed in a developer to remove exposed resist and then
blown dry with a nitrogen gas. The wafer chip is them placed in a petri dish and a PDMS mix
that has been degassed is poured over the chip. The mix is a curing agent and PDMS in a 1:10
ratio. The dish is placed on a hot plate at 75 °C for two hours and then left to polymerize at room
temperature. The micromixer PDMS design is then cut out from dish and placed on a clean slide.
The slide is prepared through alcohol cleaning. The PDMS is sealed to the slide through an
ionization process. The mixer outlets are then punched out and the mixer is ready for tested. This
overall process stated is generalized and varied techniques are used by different labs and
investigators, which is contingent on the mixer type and the desired outcome for the mixer.
There are many examples that provide additional information about MEMS fabrication [4].
ii. Characterization
The experimental set-up for characterizing the actual micromixer device is based on a
fluorescence technique. In this technique, one inlet is filled with DI water only while the other
inlet is filled with a fluorescein in DI water. The mixing of the two fluids was observed through
an inverted epifluorescence microscope equipped with a camera. Three different flow rates (.1
cc, .0096 cc, and .009 cc) were sampled with both beads flowing through and not flowing
through the fluorescein / DI water mix. Pictures were taken for all six experimental models at the
channel inlet, middle section, and outlet. A line scan was performed at the experimental outlet
for each flow rate and computed on the software program SlideBook. The channel heights at the
inlet, middle section, and outlet were also taken through use of an interferometer to better
characterize the experimental data accurately when evaluated against the simulation findings.
IV. Results
The overall findings from this project were plentiful. The simulation has many perks, ranging
from cost-effective and time-effective modeling to gaining experience on 2-D / 3-D technical
design software. The experiments allowed one to practice BioMEMS laboratory work and have
an opportunity to compare simulation to experimental results.
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5. a. Simulation
Simulations were plentiful for this project, since the only commitment was time and creative
problem solving. Initial designs were created based on the literature review of previous
micromixers. These designs included variations to the shape of the mixer and the flow pattern of
the fluid, variations to cut-out patterns or posts within the channel, as well as modifications to the
inlet patterns. In this case, based on the computer ram, the more successful simulation runs were
based on design simplicity.
After having an opportunity to experiment with many overall design themes, it was
determined that a simple trapezoidal zigzag design would lead to an outcome of nearly “perfect”
mixing in the simulation modeling. There were many different variations to sample to discover
an optimal mixer profile. The variations included the width of the zigzag arrays, the channel
width, angle of the trapezoids, the length of the individual trapezoid bases, and the inlet profile.
In figure 1, shown below, figure A shows examples of the initial micromixers designs, figure B
shows the exact specifications for the trapezoidal zigzag design chosen for this experiment,
figure C-F shows the various results of the velocity and concentration profiles for the trapezoidal
zigzag design used in this project.
Through use of the Comsol Multiphysics software, an analysis of the overall mixing was
analyzed by using a linescan approach at the channel‟s output, which is 200 mm from the
mixer‟s channel input. The initial concentrations were analyzed with a velocity flow rate of
.0001 mm/s for the base length variations and the inlet design variations. It is shown in figure 2A
that the narrower the base length was, the better the mixing capability. In the inlet variation
models, depicted in figure 2B, a surprising result occurred since the only modification to the
designs was the method of fluid in-flow. Here, the optimal design produced nearly perfect
mixing through use of two independent in-flow channels, with each inlet at the same dimension
of the mixer channel (50 microns). Simultaneously, while the same channel width of the mixer
was used for a Y and T inlet design yielded fair mixing, a double channel width dimension for
the Y and T inlet produced little mixing at the outlet (representative by the concentration profiles
taken at the outlet, shown in figure 2). These peculiar results may have occurred through
variations to the meshing of the simulation models or because such results were due to the
genuine variation results that may occur through variations to micromixer design.
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6. Figure 1: (A) Three example micromixer designs simulated on Comsol Multiphysics that demonstrate the design
variation such as cut-outs, posts, and inlet configuration, (B) Trapezoid Zigzag design specifications that were
utilized for fabricated micromixer, (C, D) Trapezoid Zigzag design with variation to base length which effects (C)
navies-stokes velocity profile and (D) concentration profiles, and (E,F) Trapezoid Zigzag design with variation to
inlet profiles which effects navies – stokes velocity profile and (F) concentration profiles.
Double Y Double T Y inlet T inlet Optimized 310 344 388 440
Slope of Entire
Line Scan 0.42 0.36 0.16 0.11 0.01 0.07 0.07 0.08 0.1
Concentration
Maximum 0.811766 0.778091 0.622774 0.608961 0.514792 0.539295 0.548043 0.558572 0.570564
Concentration
Minimum 0.155983 0.230352 0.378083 0.44569 0.492138 0.436624 0.437906 0.441099 0.415743
Table 1: Slope and concentration minimum and maximum points for the various micromixer variations computed on
Comsol Multiphysics based on the line scan readouts.
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7. Figure 2: (A, B) Concentration percent of outlet line scan with base length variation and inlet profile variation, (C)
3D modeling of “Double T” micromixer profile, (D) 3D modeling of pressure at the outlet of micromixer.
a. Experimental
The fabrication of the micromixer was completed without a hitch, since the procedure is well
known to the BioMEMS research community. Through use of an interferometer the height of the
fabricated micromixer channel can be found. In this case, the height at the inlet channel was
38.0 microns, the middle section was 39.8 microns, and the outlet section was 38.9 microns. The
most useful reading is at the outlet since this number is necessary to normalize the fluorescent
dye line scan data used for analyzing the data. The experiments conducted on the micromixer
were based on three flow rates ((.1 cc, .0096 cc, and .009 cc) and the use of beads or no beads
flowing through the fluorescent dye mix were sampled. Pictures were taken at the mixer inlet,
middle section, and outlet. The pictures, shown in Figure 3A for no beads and 3B when beads
were included demonstrate that the mixer had vortex-like characteristics. In other words, it
appears that when the fluid flows past an angular curve the fluid underwent a rotational spin but
recovered (and avoided undergoing better mixing) in the next, opposite direction, spin.
From the experiments, data was recorded at the channel outlet that depicted fluorescent
intensity for the experiments that did not use beads in the fluorescent dye. These numbers were
then normalized and compared to the simulation models. Normalization is a process to compare
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8. different output numbers on the same scaling factor. In this case, the data was set for a
comparable range from zero to one. To have comparable results through normalization, the
following two equations were applied:
The first equation, which provides value for pixel intensity is necessary because the line scan
takes a three-dimensional reading of the intensity and thus the data number must be divided by
the channel height * width. The normalized data point equation, the second equation listed,
provides the formula to create a normalized set of points set between zero and one.
The normalized data for the experimental values yields a very consistent result with the
fluorescent intensity across the outlet channel, as shown in the figure 3C graph. Using excel, the
slopes of the experiment data curves were calculated at .0308, .0297, and .0274, for the fast,
medium and slow velocity rates, respectively.
Since the original simulations used one velocity and the experiments used three velocities
that were different from the original simulations, the simulations were redone with the varied
inlet flow rates. Figure 3D shows the simulation line scan results and figure 3E shows the
simulation profiles for these varied inlet flow rates. The line scan results of the concentration
profiles, here, also required normalization since the data range for the concentration profiles was
greater for one for each velocity rate in order for the Comsol Multiphysics to analyze the
simulated micromixer. It is shown in figure 3D that the fast flow rate does not depict the same
curvature as the slow and medium rates; this may be due to meshing problems, despite the
simulations being rerun on multiple instances. This inconsistency, however, did affect the slope
of the concentration density curve to the extent that the data was invalid. Rather, the simulation
line scan slope yielded slopes of .02, .0248, and .0184 for fast, medium, and slow velocity rates,
respectively. With the normalization of both the experiment and simulation data, the outlet
concentration profiles are comparatively evaluated.
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9. Figure 3: (A, B) Concentration Profiles at the inlet, middle, and output (left to right) of fast, medium and slow flow
rates (top to bottom), (C, D) Experimental and Simulation Results of Data Normalized under the same flow rate
conditions, (E) Simulation profiles of velocity and concentration for comparable fast, medium and slow flow rates
(top to bottom) used in experiments, (F) comparable simulation and experimental concentration data points
normalized.
Simulation Experimental
Fast Medium Slow Fast Medium Slow
Concentration Minimum 0.065665 0 0.005645 0.007432 0.01291 0.023519
Concentration Maximum 0.975295 1 0.642179 0.867346 0.923039 0.844172
Slope of Concentration Curve 0.02 0.0248 0.0184 0.0308 .0297 0.0274
Table 2: Simulation and Experiment concentration and slope values for comparable velocities.
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10. V. Discussion
a. Performance
Based on the simulation and experimental output data normalized, the slopes can be
compared. Figure 4A shows the slopes values for the simulation and experimental trials of the
three velocity speeds. These figures depict the consistency and expected outcomes in both the
simulation and the experimental processes, with the exception of the slope curve of the
simulation fast fluid flow model. The slopes can then be compared to determine the percent error
of the models, as shown in figure 4B. The data points used to determine the slopes of the output
was based on the linear region of the data and the formula used to define the percent error is
based on the equation:
Based on the percent error formula, the percent error for the fast, medium and slow flow rates
are 33.12%, 16.50% and 32.85%, respectively. Based on the slope curves of the simulation
modeling, it appears that the fast flow rate was modeled with greater error than the slow or
medium flow rates. If the fast simulation model would have produced a slope curve similarly to
the middle and slow flow rates, there likely would have been a smaller percent error for the fast
simulation model. This was the one aspect of the comparison that questions validity of the
comparisons of simulation to experimental performances of the micromixer system.
Figure 4: (A) Bar graph depicting visually the slopes of the simulation and experimental concentrations at the outlets
of the micromixer, (B) Percent error of the simulation to the experiment models‟ slopes.
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11. There are a multitude of reasons why the percent error of simulation to experiment occurred.
As discussed previously, one of the prime reasons why the percent error hovered around thirty
percent is based on error in the simulation modeling. The simulation modeling software was easy
to manipulate through variance to simple factors, such as the meshing or minor modifications to
the inlets that should not affect the overall performance of an experimental micromixer.
Although these percent errors seem large, the errors are nearly consistent among the three
different flow rates. The overall performance of the simulation and experimental modeling were,
overall, successful and provided interesting data for analysis.
VI. Conclusion
The use of simulation modeling never serves as a substitute for experimental data in
determining if a micromixer is performing well. It does serve well as a reference to assist in
predicting how well experimentation may perform. In this course the opportunity to learn
Comsol Multiphysics and model micromixers was provided. There was a good amount of
freedom in designing the mixers and, in my case, a Double T Inlet trapezoid zigzag design was
chosen. This design was fabricated through a photolithography process and experiments were
performed on the fabricated micromixer with three different flow rates and the use of beads and
no beads was tried for the fluorescent dye mix. The fluorescent dye reading was taken at the
outlet position of the experimental rates and the data was normalized. Simulations were also
remodeled to match the flow rates of the experiment and the line scan readings at the output were
normalized so all data could be compared on the same scale. Based on the curves of the
concentration rates, the slopes for the simulation and experimental models ranged between .0184
and .0308. The percent error between the simulation and the experimental data points was also
calculated with an error approximately at thirty percent. The resulting error likely occurred from
an inconsistency in the meshing of the simulation models.
This project was very useful to practice both simulation modeling and the fabrication
experimentation process. The data analysis was also useful in generalized engineering terms
since data needed to be normalized for proper analysis. In conclusion, the project incorporated
the critical aspect of modern research – simulation, experimentation and comparable analysis.
The project was useful to learn more about BioMEMS and also to practice core engineering
skills, ranging from computer modeling and simulation analysis to device fabrication.
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12. VII. References
[1] S.S. Saliterman, “Fundamentals of BioMEMS and Medical Microdevices,” SPIIE – The
International Society for Optical Engineering, 2006.
[2] G. M. Whitesides, “The origins and the future of microfluidics,” Nature Insight, Vol. 442,
Issue 7101 pp.368 – 373 (2006).
[3] A. J. deMellow, “Control and detection of chemical reactions in microfluidic systems,”
Nature Insight, Vol. 442, Issue 7101 pp.374 – 380 (2006).
[4] J. Seymour et al., “Chemotatic Response of Marine Micro-organisms to MicroScale
Nutrient Layers,” Journal of Visualized Experiments, May 2007.
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