2015
DOI: 10.1016/j.medengphy.2015.06.004
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Breakdown of deterministic lateral displacement efficiency for non-dilute suspensions: A numerical study

Abstract: We investigate the effect of particle volume fraction on the efficiency of deterministic lateral displacement (DLD) devices. DLD is a popular passive sorting technique for microfluidic applications. Yet, it has been designed for treating dilute suspensions, and its efficiency for denser samples is not well known. We perform 3D simulations based on the immersed-boundary, lattice-Boltzmann and finite-element methods to model the flow of red blood cells (RBCs) in different DLD devices. We quantify the DLD efficie… Show more

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Cited by 21 publications
(40 citation statements)
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“…We have also performed simulations with dense RBC suspensions for various capillary numbers, viscosity contrasts and row-shift fractions. Our findings agree well with the numerical [57] and experimental [26] studies:…”
Section: Resultssupporting
confidence: 92%
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“…We have also performed simulations with dense RBC suspensions for various capillary numbers, viscosity contrasts and row-shift fractions. Our findings agree well with the numerical [57] and experimental [26] studies:…”
Section: Resultssupporting
confidence: 92%
“…So it is difficult to separate small rigid particles or stiff cells from dense suspensions because those would zig-zag, too. This result agrees with the numerical [57] and experimental [26] studies which use shallow devices (See Section 3.4).…”
Section: Contributionssupporting
confidence: 90%
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“…Performing simulations of cell sorting in a whole device is computationally very expensive even for a single simulation, let alone for optimization. In order to evade the computational cost the numerical studies for the simulation of DLD reduced the simulation domain to a single pillar by assuming periodic boundary conditions [22,30,38,39,42]. Such boundary conditions are tantamount to imposing artificial vertical pressure difference to enforce no net vertical flow [5,42].…”
Section: Dld Modelmentioning
confidence: 99%