A Tale of Two Processes: Simulation vs Experimentation


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BioMEMS Microfluidics Lab Analysis using COMSOL

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A Tale of Two Processes: Simulation vs Experimentation

  1. 1. Bioengineering 494Microfluidic Micromixing.A Tale of Two Processes:Simulation v. ExperimentUniversity of Illinois - ChicagoKathleen BroughtonSpring 2011
  2. 2. I. Introduction Biological MicroElectroMechanical Systems (BioMEMS) began to spark interest in theresearch community not long after MEMS [1]. In 2003, the BioMEMS industry had projectedrevenues 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 flowat the micron scale. Throughout the spring semester, the microfluidic class provided anopportunity to learn about the theory and practical application of microfluidic BioMEMSdevices. In theory classes, a focus was paid to the reviewing the engineering and physicsfundamentals related to MEMS / BioMEMS. In practical application, the course focus was ondesigning and running simulation models of a microfluidic micromixer and then converting,building and testing a micromixer in a laboratory setting. These applications, simulations andexperiments, were then comparatively analyzed. This paper will outline the course andmicrofluidic micromixer project in terms of theory, simulation, fabrication, experimentation andcomparative data analysis.II. Background a. Microfluidics Microfluidics is based on the technology application “of systems that process or manipulatesmall ( liters) amounts of fluids, using channels with dimension of tens tohundreds of micrometers” [2]. BioMEMS microfluidics has four umbrella tiers that contribute towhy it is pursued in research: molecular analysis, biodefense, molecular biology andmicroelectronics and each tier has a particular function based on a unique design consisting ofstructures such as valves, mixers and pumps [2]. Although the field has many successes in theresearch 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 uniqueeffects of a mixer on a micro scale “is that fluid properties become increasingly controlled byviscous forces rather than inertial forces” [3]. This means that flow is laminar, rather thanturbulent, with Reynolds numbers typically and mixing occurs through diffusion, insteadof a convective, process [3]. Another characteristic of the micromixer is that the “channel walls 1
  3. 3. impart shear forces on the contained fluid, so under applied hydrodynamic pressure a parabolicvelocity profile is established over the cross-section with fluid velocity zero at channel walls andmaximum at the center” [3]. This also impacts the final mixing of the fluids. In response to thefluid dynamic principles of micromixers, a number of mixer designs have been experimentedwith 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 acomputer lab and fabricated in a laboratory experiment capacity, whereas a linescan analysiswould 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 ComsolMultiphysics 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 by200 microns width and height of 50 microns. An additional caveat was that no dimension withinthe micromixer could be less than 50 microns, which was due to fabrication limitations based onthe 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 runtheoretical trials for different micromixers. The program allows a person to have endlessopportunities to design and run quick analysis to determine if the design meets the goals anddesired outcome. The request pertaining to simulations was to (1) learn the software, (2) practicecreating a few different designs, (3) after choosing one design, simulate five variations to thedesign and decide on one final design to fabricate. To learn and practice creating differentdesigns 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 thesimulation to actual experimental results. b. Experimental i. Fabrication The fabrication used for the micromixers is based on a general photolithography techniquefollowed by MEMS scientists. In this process, SU 2050 is first spun onto a silicon wafer chip tocreate an approximately 50 micron high layer. The chip is then placed on a hot plate at 65 °C for 2
  4. 4. three minutes, 95 °C for six and a half minutes, 65 °C for one minute and left to cool at roomtemperature for at least five minutes. The chip is then exposed to a 175 mJ/cm2 UV light with amask designed to the micromixer specification. The chip then undergoes a hard back at 65 °C forone and a half minutes, 95 °C for six and a half minutes, 65 °C for one minute and then left tocool at room temperature. The chip is placed in a developer to remove exposed resist and thenblown dry with a nitrogen gas. The wafer chip is them placed in a petri dish and a PDMS mixthat has been degassed is poured over the chip. The mix is a curing agent and PDMS in a 1:10ratio. The dish is placed on a hot plate at 75 °C for two hours and then left to polymerize at roomtemperature. 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 anionization process. The mixer outlets are then punched out and the mixer is ready for tested. Thisoverall process stated is generalized and varied techniques are used by different labs andinvestigators, 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 afluorescence technique. In this technique, one inlet is filled with DI water only while the otherinlet is filled with a fluorescein in DI water. The mixing of the two fluids was observed throughan inverted epifluorescence microscope equipped with a camera. Three different flow rates (.1cc, .0096 cc, and .009 cc) were sampled with both beads flowing through and not flowingthrough the fluorescein / DI water mix. Pictures were taken for all six experimental models at thechannel inlet, middle section, and outlet. A line scan was performed at the experimental outletfor each flow rate and computed on the software program SlideBook. The channel heights at theinlet, middle section, and outlet were also taken through use of an interferometer to bettercharacterize 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, rangingfrom cost-effective and time-effective modeling to gaining experience on 2-D / 3-D technicaldesign software. The experiments allowed one to practice BioMEMS laboratory work and havean opportunity to compare simulation to experimental results. 3
  5. 5. a. Simulation Simulations were plentiful for this project, since the only commitment was time and creativeproblem solving. Initial designs were created based on the literature review of previousmicromixers. These designs included variations to the shape of the mixer and the flow pattern ofthe fluid, variations to cut-out patterns or posts within the channel, as well as modifications to theinlet patterns. In this case, based on the computer ram, the more successful simulation runs werebased on design simplicity. After having an opportunity to experiment with many overall design themes, it wasdetermined 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 discoveran optimal mixer profile. The variations included the width of the zigzag arrays, the channelwidth, 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 Bshows 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 trapezoidalzigzag design used in this project. Through use of the Comsol Multiphysics software, an analysis of the overall mixing wasanalyzed by using a linescan approach at the channel‟s output, which is 200 mm from themixer‟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 2Athat the narrower the base length was, the better the mixing capability. In the inlet variationmodels, depicted in figure 2B, a surprising result occurred since the only modification to thedesigns was the method of fluid in-flow. Here, the optimal design produced nearly perfectmixing through use of two independent in-flow channels, with each inlet at the same dimensionof the mixer channel (50 microns). Simultaneously, while the same channel width of the mixerwas used for a Y and T inlet design yielded fair mixing, a double channel width dimension forthe Y and T inlet produced little mixing at the outlet (representative by the concentration profilestaken at the outlet, shown in figure 2). These peculiar results may have occurred throughvariations to the meshing of the simulation models or because such results were due to thegenuine variation results that may occur through variations to micromixer design. 4
  6. 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 440Slope of EntireLine Scan 0.42 0.36 0.16 0.11 0.01 0.07 0.07 0.08 0.1ConcentrationMaximum 0.811766 0.778091 0.622774 0.608961 0.514792 0.539295 0.548043 0.558572 0.570564ConcentrationMinimum 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. 5
  7. 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 wellknown to the BioMEMS research community. Through use of an interferometer the height of thefabricated micromixer channel can be found. In this case, the height at the inlet channel was38.0 microns, the middle section was 39.8 microns, and the outlet section was 38.9 microns. Themost useful reading is at the outlet since this number is necessary to normalize the fluorescentdye line scan data used for analyzing the data. The experiments conducted on the micromixerwere based on three flow rates ((.1 cc, .0096 cc, and .009 cc) and the use of beads or no beadsflowing 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 beadswere included demonstrate that the mixer had vortex-like characteristics. In other words, itappears that when the fluid flows past an angular curve the fluid underwent a rotational spin butrecovered (and avoided undergoing better mixing) in the next, opposite direction, spin. From the experiments, data was recorded at the channel outlet that depicted fluorescentintensity for the experiments that did not use beads in the fluorescent dye. These numbers werethen normalized and compared to the simulation models. Normalization is a process to compare 6
  8. 8. different output numbers on the same scaling factor. In this case, the data was set for acomparable range from zero to one. To have comparable results through normalization, thefollowing two equations were applied: The first equation, which provides value for pixel intensity is necessary because the line scantakes a three-dimensional reading of the intensity and thus the data number must be divided bythe 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 thefluorescent intensity across the outlet channel, as shown in the figure 3C graph. Using excel, theslopes 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 velocitiesthat were different from the original simulations, the simulations were redone with the variedinlet flow rates. Figure 3D shows the simulation line scan results and figure 3E shows thesimulation profiles for these varied inlet flow rates. The line scan results of the concentrationprofiles, here, also required normalization since the data range for the concentration profiles wasgreater for one for each velocity rate in order for the Comsol Multiphysics to analyze thesimulated micromixer. It is shown in figure 3D that the fast flow rate does not depict the samecurvature as the slow and medium rates; this may be due to meshing problems, despite thesimulations being rerun on multiple instances. This inconsistency, however, did affect the slopeof the concentration density curve to the extent that the data was invalid. Rather, the simulationline 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 outletconcentration profiles are comparatively evaluated. 7
  9. 9. Figure 3: (A, B) Concentration Profiles at the inlet, middle, and output (left to right) of fast, medium and slow flowrates (top to bottom), (C, D) Experimental and Simulation Results of Data Normalized under the same flow rateconditions, (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 pointsnormalized. 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 0.0274Table 2: Simulation and Experiment concentration and slope values for comparable velocities. 8
  10. 10. V. Discussion a. Performance Based on the simulation and experimental output data normalized, the slopes can becompared. Figure 4A shows the slopes values for the simulation and experimental trials of thethree velocity speeds. These figures depict the consistency and expected outcomes in both thesimulation and the experimental processes, with the exception of the slope curve of thesimulation fast fluid flow model. The slopes can then be compared to determine the percent errorof the models, as shown in figure 4B. The data points used to determine the slopes of the outputwas based on the linear region of the data and the formula used to define the percent error isbased on the equation: Based on the percent error formula, the percent error for the fast, medium and slow flow ratesare 33.12%, 16.50% and 32.85%, respectively. Based on the slope curves of the simulationmodeling, it appears that the fast flow rate was modeled with greater error than the slow ormedium flow rates. If the fast simulation model would have produced a slope curve similarly tothe middle and slow flow rates, there likely would have been a smaller percent error for the fastsimulation model. This was the one aspect of the comparison that questions validity of thecomparisons 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 outletsof the micromixer, (B) Percent error of the simulation to the experiment models‟ slopes. 9
  11. 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 thirtypercent is based on error in the simulation modeling. The simulation modeling software was easyto manipulate through variance to simple factors, such as the meshing or minor modifications tothe 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 threedifferent 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 indetermining if a micromixer is performing well. It does serve well as a reference to assist inpredicting how well experimentation may perform. In this course the opportunity to learnComsol Multiphysics and model micromixers was provided. There was a good amount offreedom in designing the mixers and, in my case, a Double T Inlet trapezoid zigzag design waschosen. This design was fabricated through a photolithography process and experiments wereperformed on the fabricated micromixer with three different flow rates and the use of beads andno beads was tried for the fluorescent dye mix. The fluorescent dye reading was taken at theoutlet position of the experimental rates and the data was normalized. Simulations were alsoremodeled to match the flow rates of the experiment and the line scan readings at the output werenormalized so all data could be compared on the same scale. Based on the curves of theconcentration rates, the slopes for the simulation and experimental models ranged between .0184and .0308. The percent error between the simulation and the experimental data points was alsocalculated with an error approximately at thirty percent. The resulting error likely occurred froman inconsistency in the meshing of the simulation models. This project was very useful to practice both simulation modeling and the fabricationexperimentation process. The data analysis was also useful in generalized engineering termssince data needed to be normalized for proper analysis. In conclusion, the project incorporatedthe critical aspect of modern research – simulation, experimentation and comparable analysis.The project was useful to learn more about BioMEMS and also to practice core engineeringskills, ranging from computer modeling and simulation analysis to device fabrication. 10
  12. 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. 11