Literature DB >> 28713477

MOPSA: A microfluidics-optimized particle simulation algorithm.

Junchao Wang1, Victor G J Rodgers1, Philip Brisk2, William H Grover1.   

Abstract

Computer simulation plays a growing role in the design of microfluidic chips. However, the particle tracers in some existing commercial computational fluid dynamics software are not well suited for accurately simulating the trajectories of particles such as cells, microbeads, and droplets in microfluidic systems. To address this issue, we present a microfluidics-optimized particle simulation algorithm (MOPSA) that simulates the trajectories of cells, droplets, and other particles in microfluidic chips with more lifelike results than particle tracers in existing commercial software. When calculating the velocity of a particle, MOPSA treats the particle as a two-dimensional rigid circular object instead of a single point. MOPSA also checks for unrealistic interactions between particles and channel walls and applies an empirical correcting function to eliminate these errors. To validate the performance of MOPSA, we used it to simulate a variety of important features of microfluidic devices like channel intersections and deterministic lateral displacement (DLD) particle sorter chips. MOPSA successfully predicted that different particle sizes will have different trajectories in six published DLD experiments from three research groups; these DLD chips were used to sort a variety of different cells, particles, and droplets. While some of these particles are not actually rigid or spherical, MOPSA's approximation of these particles as rigid spheres nonetheless resulted in lifelike simulations of the behaviors of these particles (at least for the particle sizes and types shown here). In contrast, existing commercial software failed to replicate these experiments. Finally, to demonstrate that MOPSA can be extended to simulate other properties of particles, we added support for simulating particle density to MOPSA and then used MOPSA to simulate the operation of a microfluidic chip capable of sorting cells by their density. By enabling researchers to accurately simulate the behavior of some types of particles in microfluidic chips before fabricating the chips, MOPSA should accelerate the development of new microfluidic devices for important applications.

Year:  2017        PMID: 28713477      PMCID: PMC5484639          DOI: 10.1063/1.4989860

Source DB:  PubMed          Journal:  Biomicrofluidics        ISSN: 1932-1058            Impact factor:   2.800


  22 in total

1.  Continuous particle separation through deterministic lateral displacement.

Authors:  Lotien Richard Huang; Edward C Cox; Robert H Austin; James C Sturm
Journal:  Science       Date:  2004-05-14       Impact factor: 47.728

2.  Critical particle size for fractionation by deterministic lateral displacement.

Authors:  David W Inglis; John A Davis; Robert H Austin; James C Sturm
Journal:  Lab Chip       Date:  2006-03-17       Impact factor: 6.799

3.  Gravity-driven microfluidic particle sorting device with hydrodynamic separation amplification.

Authors:  Dongeun Huh; Joong Hwan Bahng; Yibo Ling; Hsien-Hung Wei; Oliver D Kripfgans; J Brian Fowlkes; James B Grotberg; Shuichi Takayama
Journal:  Anal Chem       Date:  2007-02-15       Impact factor: 6.986

4.  Effects of shear rate on propagation of blood clotting determined using microfluidics and numerical simulations.

Authors:  Matthew K Runyon; Christian J Kastrup; Bethany L Johnson-Kerner; Thuong G Van Ha; Rustem F Ismagilov
Journal:  J Am Chem Soc       Date:  2008-02-27       Impact factor: 15.419

5.  Measuring single-cell density.

Authors:  William H Grover; Andrea K Bryan; Monica Diez-Silva; Subra Suresh; John M Higgins; Scott R Manalis
Journal:  Proc Natl Acad Sci U S A       Date:  2011-06-20       Impact factor: 11.205

6.  Enhanced cell sorting and manipulation with combined optical tweezer and microfluidic chip technologies.

Authors:  Xiaolin Wang; Shuxun Chen; Marco Kong; Zuankai Wang; Kevin D Costa; Ronald A Li; Dong Sun
Journal:  Lab Chip       Date:  2011-09-14       Impact factor: 6.799

7.  Label-free density difference amplification-based cell sorting.

Authors:  Jihwan Song; Minsun Song; Taewook Kang; Dongchoul Kim; Luke P Lee
Journal:  Biomicrofluidics       Date:  2014-11-26       Impact factor: 2.800

Review 8.  Microfluidic cell sorting: a review of the advances in the separation of cells from debulking to rare cell isolation.

Authors:  C Wyatt Shields; Catherine D Reyes; Gabriel P López
Journal:  Lab Chip       Date:  2015-03-07       Impact factor: 6.799

9.  Droplet size based separation by deterministic lateral displacement-separating droplets by cell--induced shrinking.

Authors:  Haakan N Joensson; Mathias Uhlén; Helene Andersson Svahn
Journal:  Lab Chip       Date:  2011-02-14       Impact factor: 6.799

10.  Sorting cells by their dynamical properties.

Authors:  Ewan Henry; Stefan H Holm; Zunmin Zhang; Jason P Beech; Jonas O Tegenfeldt; Dmitry A Fedosov; Gerhard Gompper
Journal:  Sci Rep       Date:  2016-10-06       Impact factor: 4.379

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  1 in total

Review 1.  Machine learning for microfluidic design and control.

Authors:  David McIntyre; Ali Lashkaripour; Polly Fordyce; Douglas Densmore
Journal:  Lab Chip       Date:  2022-08-09       Impact factor: 7.517

  1 in total

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