This paper deals with a new analytical model for microfluidic passive mixers. Two common approaches already exist for such a purpose. On the one hand, the resolution of the advection-diffusion-reaction equation (ADRE) is the first one and the closest to physics. However, ADRE is a partial differential equation that requires finite element simulations. On the other hand, analytical models based on the analogy between microfluidics and electronics have already been established. However, they rely on the assumption of homogeneous fluids, which means that the mixer is supposed to be long enough to obtain a perfect mixture at the output. In this paper, we derive an analytical model from the ADRE under several assumptions. Then we integrate these equations within the electronic-equivalent models. The resulting models computed the relationship between pressure and flow rate in the microfluidic circuit but also takes the concentration gradients that can appear in the direction perpendicular to the channel into account. The model is compared with the finite element simulation performed with COMSOL Multiphysics in several study cases. We estimate that the global error introduced by our model compared to the finite element simulation is less than 5% in every use case. In counterparts, the cost in terms of computational resources is drastically reduced. The analytical model can be implemented in a large range of modelling and simulation languages, including SPICE and hardware description language such as Verilog-AMS. This feature is very interesting in the context of the in silico prototyping of large-scale microfluidic devices or multi-physics devices involving microfluidic circuits, e.g. lab-on-chips.
Citation: Alexi Bonament, Alexis Prel, Jean-Michel Sallese, Christophe Lallement, Morgan Madec. Analytic modelling of passive microfluidic mixers[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3892-3908. doi: 10.3934/mbe.2022179
This paper deals with a new analytical model for microfluidic passive mixers. Two common approaches already exist for such a purpose. On the one hand, the resolution of the advection-diffusion-reaction equation (ADRE) is the first one and the closest to physics. However, ADRE is a partial differential equation that requires finite element simulations. On the other hand, analytical models based on the analogy between microfluidics and electronics have already been established. However, they rely on the assumption of homogeneous fluids, which means that the mixer is supposed to be long enough to obtain a perfect mixture at the output. In this paper, we derive an analytical model from the ADRE under several assumptions. Then we integrate these equations within the electronic-equivalent models. The resulting models computed the relationship between pressure and flow rate in the microfluidic circuit but also takes the concentration gradients that can appear in the direction perpendicular to the channel into account. The model is compared with the finite element simulation performed with COMSOL Multiphysics in several study cases. We estimate that the global error introduced by our model compared to the finite element simulation is less than 5% in every use case. In counterparts, the cost in terms of computational resources is drastically reduced. The analytical model can be implemented in a large range of modelling and simulation languages, including SPICE and hardware description language such as Verilog-AMS. This feature is very interesting in the context of the in silico prototyping of large-scale microfluidic devices or multi-physics devices involving microfluidic circuits, e.g. lab-on-chips.
[1] | P. Cui, S. Wang, Application of microfluidic chip technology in pharmaceutical analysis: A review, J. Pharmaceut. Ana., 9 (2019), 238–247. https://doi.org/10.1016/j.jpha.2018.12.001 doi: 10.1016/j.jpha.2018.12.001 |
[2] | M. Tokeshi, Applications of microfluidic systems in biology and medicine, Springer, (2019). https://doi-org/10.1007/978-981-13-6229-3 |
[3] | S. Li, Z. Ma, Z. Cao, L. Pan, Y. Shi, Advanced wearable microfluidic sensors for healthcare monitoring, Small, 16 (2020), 1903822. https://doi.org/10.1002/smll.201903822 doi: 10.1002/smll.201903822 |
[4] | M. Yew, Y. Ren, K. S. Koh, C. Sun, C. Snape, A review of state-of-the-art microfluidic technologies for environmental applications: Detection and remediation, Global Challenges, 3 (2019), 1800060. https://doi.org/10.1002/gch2.201800060 doi: 10.1002/gch2.201800060 |
[5] | U. Hashim, P. N. A. Diyana, T. Adam, Numerical simulation of Microfluidic devices, 2012 10th IEEE International Conference on Semiconductor Electronics (ICSE), (2012), 26–29. https://doi.org/10.1109/SMElec.2012.6417083 |
[6] | D. Erickson, Towards numerical prototyping of labs-on-chip: Modeling for integrated microfluidic devices, Microfluid Nanofluid, 1 (2005), 301–318. https://doi-org/10.1007/s10404-005-0041-z |
[7] | P. Hadikhani, S. Majidi, A. Afshari, Numerical simulation of droplet formation in different microfluidic devices, P. I. Mech. Eng. C-J Mec., 234 (2020), 3776–3788. https://doi.org/10.1177/0954406220916480 doi: 10.1177/0954406220916480 |
[8] | J. Wang, V. G. J. Rodgers, P. Brisk, W. H. Grover, Instantaneous simulation of fluids and particles in complex microfluidic devices. PLOS ONE, 12 (2017), e0189429. https://doi.org/10.1371/journal.pone.0189429 doi: 10.1371/journal.pone.0189429 |
[9] | R. Qiao, N. R. Aluru, A compact model for electroosmotic flows in microfluidic devices, J. Micromec.h Microeng., 12 (2002), 625–635. |
[10] | W. Jeon, C. B. Shin, Design and simulation of passive mixing in microfluidic systems with geometric variations, CAN. J. Chem. Eng., 152 (2009), 575–582. https://doi.org/10.1016/j.cej.2009.05.035 doi: 10.1016/j.cej.2009.05.035 |
[11] | A. Maha, D. O. Barrett, D. E. Nikitopoulos, S. A. Soper, M. C. Murphy, Simulation and design of micromixers for microfluidic devices, P. SoC Photo-Opt. Ins., International Society for Optics and Photonics, (2003), 183–193. https://doi.org/10.1117/12.530788 |
[12] | J. Koo, C. Kleinstreuer, Liquid flow in microchannels: Experimental observations and computational analyses of microfluidics effects, J. Micromech. Microeng., 13 (2003), 568–579. https://doi.org/10.1088/0960-1317/13/5/307 doi: 10.1088/0960-1317/13/5/307 |
[13] | C. Y. Lee, W. T. Wang, C. C. Liu, L. M. Fu, Passive mixers in microfluidic systems: A review, CAN. J. Chem. Eng., 288 (2016), 146–160. https://doi.org/10.1016/j.cej.2015.10.122 doi: 10.1016/j.cej.2015.10.122 |
[14] | Y. K. Suh, S. Kang, A Review on Mixing in Microfluidics, Micromachines, 1 (2010), 82–111. https://doi.org/10.3390/mi1030082 doi: 10.3390/mi1030082 |
[15] | P. Tabeling, Introduction to Microfluidics, Oxford University Press, USA, 2005. |
[16] | W. Hundsdorfer, J. Verwer, Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations, Springer-Verlag Berlin Heidelberg, 2003. |
[17] | I. M. Hsing, R. Srinivasan, M. P. Harold, K. F. Jensen, M. A. Schmidt, Finite element simulation strategies for microfluidic devices with chemical reactions, Proceedings of International Solid State Sensors and Actuators Conference (Transducers '97), 2 (1997), 1015–1018. https://doi.org/10.1109/SENSOR.1997.635357 doi: 10.1109/SENSOR.1997.635357 |
[18] | Comsol, COMSOL Multiphysics® Modeling Software, 2019. |
[19] | Microfluidics Software—For Simulating Microfluidics Devices. Available from: https://www.comsol.fr/microfluidics-module |
[20] | C. Prud'homme, V. Chabannes, V. Doyeux, M. Ismail, A. Samake, G. Pena, Feel++: A computational framework for Galerkin Methods and Advanced Numerical Methods, ESAIM Proceed., 38 (2012), 429–455. https://doi.org/10.1051/proc/201238024 doi: 10.1051/proc/201238024 |
[21] | N. Zaidon, A. N. Nordin, A. F. Ismail, Modelling of microfluidics network using electric circuits, 2015 IEEE Regional Symposium on Micro and Nanoelectronics (RSM), (2015), 1–4. https://doi.org/10.1109/RSM.2015.7354954 |
[22] | K. W. Oh, K. Lee, B. Ahn, E. P. Furlani, Design of pressure-driven microfluidic networks using electric circuit analogy, Lab Chip, 12 (2012), 515–545. https://doi.org/10.1039/C2LC20799K doi: 10.1039/C2LC20799K |
[23] | M. H. V. Werts, V. Raimbault, R. Texier-Picard, R. Poizat, O. Français, L. Griscomab, et al., Quantitative full-colour transmitted light microscopy and dyes for concentration mapping and measurement of diffusion coefficients in microfluidic architectures, Lab Chip, 12 (2012), 808–820. https://doi.org/10.1039/c2lc20889j doi: 10.1039/c2lc20889j |
[24] | F. A. Perdigones, A. Luque, J. M. Quero, Correspondence between electronics and fluids in mems: designing microfluidic systems using electronics, IEEE Indust. Electron. Magaz., 8 (2014), 6–17. https://doi.org/10.1109/MIE.2014.2318062 doi: 10.1109/MIE.2014.2318062 |
[25] | H. Xie, X. Zhao, H. Yang, Experimental and numerical study on a planar passive micromixer with semicircle mixing elements, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (2010), 1013–1016. https://doi.org/10.1109/AIM.2010.5695817 |
[26] | H. L. The, H. L. Thanh, T. Dong, Q. B. Ta, N. Tran-Minh, F. Karlsen, An effective passive micromixer with shifted trapezoidal blades using wide Reynolds number range, Chem. Eng. Res. Des., 93 (2015), 1–11. https://doi.org/10.1016/j.cherd.2014.12.003 doi: 10.1016/j.cherd.2014.12.003 |
[27] | K. Kundert, O. Zinke, The Designer's Guide to Verilog-AMS, Springer US, 2004. https://doi.org/10.1109/BMAS.2009.5338896 |
[28] | Y. Zeng, F. Azizi, C. H. Mastrangelo, Behavioral modeling of solute tracking in microfluidics, 2009 IEEE Behavioral Modeling and Simulation Workshop, (2009), 1–6. https://doi.org/10.1109/BMAS.2009.5338896 |
[29] | A. Voig, J. Schreiter, P. Frank, C. Pini, C. Mayr, A. Richter, Method for the computer-aided schematic design and simulation of hydrogel-based microfluidic systems, IEEE T Comput. Aid. D. 39 (2010), 1635–1648. https://doi.org/10.1109/TCAD.2019.2925354 doi: 10.1109/TCAD.2019.2925354 |
[30] | Y. Gendrault, M. Madec, C. Lallemen, J. Haiech, Modeling biology with HDL languages: A first step toward a genetic design automation tool inspired from microelectronics, IEEE T Bio-med. Eng. 61 (2014), 1231–1240. https://doi.org/10.1109/TBME.2014.2298559 doi: 10.1109/TBME.2014.2298559 |
[31] | M. Madec, L. Hebrard, J. B. Kammerer, A. Bonament, E. Rosati, C. Lallement, Multiphysics simulation of biosensors involving 3d biological reaction-diffusion phenomena in a standard circuit EDA environment, IEEE T. Circuits-I Regular Papers, (2019), 1–10. https://doi.org/10.1109/TCSI.2018.2885223 |
[32] | A. Bonament, A. Prel, J. M. Sallese, M. Madec, C. Lallement, Compact model for continuous microfluidic mixer, 2020 27th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), (2020), 35–39. https://doi.org/10.23919/MIXDES49814.2020.9155997 |