System-level Modeling
The system-level model representation is based on geometrical and functional hierarchy. A complex system can be decomposed into a set of commonly used components of simple geometries, each with an associated function (e.g. mixing or separation). This decomposition enables derivation of a closed-form parameterized model. The components and their models can be reused in a top-down manner to represent various designs using different topologies, component sizes and material properties. Then the schematic representation is used for iterative performance evaluation, uncertainty quantification, and design optimization.
The applications of our system-level models mainly focus on microfluidic lab on chip networks involving electrokinetic and pressure driven flow, liquid filling, electrophoresis, mixing, homogeneous and surface-based reaction and others:
Electrophoresis
Serpentine-shaped Microchip Electrophoresis
Spiral-shaped Microchip Electrophoresis
iMSEL has developed system-level models for analyzing the dispersion of electrophoretic transport of charged analyte molecules in a general-shaped microchannel. The models are based on the method of moments to describe analyte dispersion (including both the skew and broadening of the band) and hold for analyte bands of virtually arbitrary initial shape, and offer orders-of-magnitude improvement in computational efficiency over full numerical simulations. The model is used to perform a systematic parametric study of serpentine channels consisting of a pair of complementary turn microchannels. The results indicate that dispersion in a particular turn can contribute to either an increase or decrease of the overall band broadening. The efficiency and accuracy of the model is further demonstrated by its application to general-shaped channels that occur in practice.
Serpentine &Spiral-shaped Microchip Electrophoresis
Publications:
Y. Wang, Q. Lin* and T. Mukherjee, “System-Oriented Dispersion Models of General-Shaped Electrophoresis Microchannels”, Lab on chip, 2004, Vol. 4, pp. 453-463. (Hot Article)
Y. Wang, Q. Lin* and T. Mukherjee, “A Model for Joule Heating-Induced Dispersion in Microchip Electrophoresis”, Lab on chip, 2004, Vol.4 pp. 625-631.25.
A.S. Bedekar, Y. Wang, S. Krishnamoorthy*, S.S. Siddhaye and S. Sundaram, “System-Level Simulation of Flow-Induced Dispersion in Lab-on-a-Chip Systems”, IEEE Trans. CAD., 2006, Vol. 2, pp. 294-304.
Y. Wang*, Q. Lin and T. Mukherjee, “Models for Joule Heating Dispersion in Complex Electrophoretic Separation Microchannels”, 2004 ASME International Mechanical Engineering Congress and Exposition, No. 60970, 2004.
Y. Wang*, Q. Lin and T. Mukherjee, “Composable System Simulation of Dispersion in Complex Electrophoretic Separation Microchips”, 7th International Conference on Modeling and Simulation of Microsystems, pp. 59-62, 2004.
Y. Wang*, Q. Lin and T. Mukherjee, “Analytical Dispersion Models for Efficient Simulation of Complex Microchip Electrophoresis Systems”, The 7th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS), pp. 135-138, 2003.
Y. Wang*, Q. Lin and T. Mukherjee, “Universal Joule Heating Model in Electrophoretic Separation Microchips”, The 6th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS), pp. 82-84, 2002.
Y. Wang*, Q. Lin, J. Hoburg and T. Mukherjee, “Modeling of Joule Heating in Electrophoretic Separation Microchips”, 5th International Conference on Modeling and Simulation of Microsystems, pp. 80-83, 2002.
Mixing
iMSEL has also developed a hierarchical model for the efficient and accurate simulations of laminar diffusion-based electrokinetic passive micromixers by representing them as a system of mixing components of relatively simple geometry. Parameterized and analytical models for constituent component are obtained, which are valid for general sample concentration profiles and arbitrary flow ratios at the component inlet. A lumped-parameter, system-level model is constructed by an appropriate set of continuous parameters at the component interface to link adjacent components. The system-level model, which simultaneously computes electric circuitry and sample concentration distributions in the entire micromixer network, agrees with numerical and experimental results, and offers orders-of-magnitude improvements in computational efficiency over full numerical simulations.
Multiplex Mixing Network
A particular sub-domain of mixing network is the concentration gradient generation that has recently gained significant attraction in cellular assay and high-through screening. iMSEL has investigated a systematic modeling methodology for microfluidic concentration gradient generators. The generator is decomposed into a system of microfluidic elements with relatively simple geometries, and parameterized models for such components are analytically derived and are applicable to concentration gradient generators that rely on either complete or partial mixing (the latter renders most electric analogy-based methods invalid). The system model is verified by numerical analysis and experimental data and accurately captures the overall effects of network topologies, component sizes, flow rates, and reservoir sample concentrations on the generation of sample concentration gradient. This modeling methodology has been used to design novel and compact microfluidic devices able to create concentration gradients of complex shapes by juxtaposing simple constituent profiles along the channel width.
Concentration Gradient Generator
Publications:
A. S. Bedekar*, Y. Wang, S. S. Siddhaye, S. Krishnamoorthy, and S. F. Malin, "Design Software for Application-Specific Microfluidic Devices," Clinical Chemistry, Vol. 53, pp. 2023-2026, 2007.
Y. Wang, Q. Lin and T. Mukherjee, “Composable Behavioral Models and Schematic-Based Simulation of Electrokinetic Lab-on-a-Chip Systems”, IEEE Trans. CAD., 2006, Vol. 2, pp.258-273.
Y. Wang, Q. Lin* and T. Mukherjee, “A model for laminar diffusion-based complex electrokinetic passive micromixers”, Lab on chip, 2005, Vol. 5, pp. 877-887.
Y. Wang*, Q. Lin and T. Mukherjee, “System Simulations of Complex Electrokinetic Passive Micromixers”, 8th International Conference on Modeling and Simulation of Microsystems, pp. 579-582, 2005.
Y. Wang*, Q. Lin and T. Mukherjee, “Applications of Behavioral Modeling and Simulation on Lab-on-a-chip: Micro-Mixer and Separation System”, 2004 IEEE International Behavioral Modeling and Simulation Conference, pp. 1-6, 2004.
Y. Wang*, Q. Lin and T. Mukherjee, “Analytical Models for Complex Electrokinetic Passive Micromixers”, The 8th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS) , pp. 596-598, 2004.
Parallel Electrokinetic Mixing Network
Publications:
Z. Zhou, Y. Wang*, T. Mukherjee, Q. Lin*, “Generation of Complex Concentration Profiles by Partial Diffusive Mixing in Multi-stream Laminar Flow”, Lab on Chip, Vol. 9, pp. 1439-1448, 2009.
Y. Wang, Q. Lin* and T. Mukherjee, “Systematic Modeling and Design of Microfluidic Concentration Gradient Generators”, Journal of Micromechanics and Microengineering, Vol. 16, pp. 2128-2137, 2006.
Z. Zhou, Y. Wang, T. Mukherjee, Q. Lin*. “Design Synthesis and Experimental Validation of Microfluidic Concentration Gradient Generators”. IEEE MEMS’2008, pp. 579-582, 2008.
Y. Wang, Q. Lin* and T. Mukherjee, “System-Level Modeling and Design of Microfluidic Concentration Gradient Generators”. 1st IEEE International Conference on Nano/Micro Engineered and Molecular Systems, pp. 1368-1373, 2006.
Biochemical Assay
iMSEL has presented a “mixed-methodology” based system-level modeling and simulation for biochemical assays in Lab-on-a-Chip (LoC) devices. The methodology uses a combination of numerical schemes and analytical approaches to simulate biological and physicochemical processes, specifically, an integral approach for fluid flow and electric field, Method of Lines (MOL) and two-compartment models for biochemical reactions, and Fourier series-based model for analyte mixing. The solution procedure begins with decomposing the lab-on-a-chip device into a system of inter-connected components (e.g., channels and junctions) and the models are solved in a network fashion. Models are developed to accurately capture the multi-physics (e.g., flow, mixing, and reaction) behavior of individual components. The assembly of the components is facilitated via exchange of fluid flux and Fourier series coefficients (or average concentration) of analytes between various components, which enables network solution of the models. The system models are validated against both experimental and numerical models on various biochemical assays (e.g. immunoassays and enzymatic reactions), showing significant computational speedup (100 -10,000-fold depending on the assay) without appreciably compromising accuracy (< 10% error relative to numerical analysis).
Enzymatics Microfluidic Network
System Level Simulation Algorithm
Y-type Competitive Immunoassay
Enzymatics Microfluidic Network
High-through Network for Kinetics Analysis
Publications:
Y. Wang*, Aditya S. Bedekar, S. Krishnamoorthy, Sachin S. Siddhaye, and Shivshankar, “System-Level Modeling and Simulation of Biochemical Assays in Lab-on-a-Chip Devices”, Microfluidics and Nanofluidics, Vol. 3, pp. 307-322, 2007.
Y. Wang*, R. Magargle, Q. Lin, J.F. Hoburg and T. Mukherjee, “System-Oriented Modeling and Simulation of Biofluidic Lab-on-a-chip”, The 13th International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers’05), pp. 1280-1283, 2005.
Y. Wang*, A.S. Bedekar, S. Krishnamoorthy et. al. “Mixed methodology-based system level simulation of biochemical assays in integrated microfluidic systems”, 9th International Conference on Modeling and Simulation of Microsystems, pp. 546-549, 2006.
Liquid Filling
Liquid filling in microfluidic channels is a complex process that depends on a variety of geometric, operating, and material parameters such as microchannel geometry, flow velocity/pressure, liquid surface tension, and contact angle of channel surface. Accurate analysis of the filling process can provide key insights into the filling time, air bubble trapping, and dead zone formation, and help evaluate tradeoffs among the various design parameters and lead to optimal chip design. iMSEL has developed a parameterized dynamic model for the system-level analysis of liquid filling in 3D microfluidic networks based on the system decomposition approach, which tracks the liquid front in the microchannels. The principle of mass conservation at the junction is used to link the fluidic parameters in the microchannels emanating from the junction. The models are used to simulate the transient liquid filling process in a variety of microfluidic constructs and in a multiplexer, representing a complex microfluidic network. The accuracy (relative error less than 7%) and orders-of-magnitude speedup (30,000X – 4,000,000X) of our system-level models are verified by comparison against 3D high-fidelity numerical studies. Our findings clearly establish the utility of our models and simulation methodology for fast, reliable analysis of liquid filling to guide the design optimization of complex microfluidic networks.
Publications:
H. Song, Y. Wang*, K. Pant, “System-level simulation of liquid filling in microfluidic chips”, Biomicrofluidics, Vol. 5, 024107, 2011.
H. Song, Y. Wang*, K. Pant, "System-level Simulation of Liquid Filling in Microfluidic Chips", Lab on a Chip World Congress, 2010.