Issue 20, 2022

Metachronal patterns by magnetically-programmable artificial cilia surfaces for low Reynolds number fluid transport and mixing

Abstract

Motile cilia can produce net fluid flows at low Reynolds number because of their asymmetric motion and metachrony of collective beating. Mimicking this with artificial cilia can find application in microfluidic devices for fluid transport and mixing. Here, we study the metachronal beating of nonidentical, magnetically-programmed artificial cilia whose individual non-reciprocal motion and collective metachronal beating pattern can be independently controlled. We use a finite element method that accounts for magnetic forces, cilia deformation and fluid flow in a fully coupled manner. Mimicking biological cilia, we study magnetic cilia subject to a full range of metachronal driving patterns, including antiplectic, symplectic, laeoplectic and diaplectic waves. We analyse the induced primary flow, secondary flow and mixing rate as a function of the phase lag between cilia and explore the underlying physical mechanism. Our results show that shielding effects between neighboring cilia lead to a primary flow that is larger for antiplectic than for symplectic metachronal waves. The secondary flow can be fully explained by the propagation direction of the metachronal wave. Finally, we show that the mixing rate can be strongly enhanced by laeoplectic and diaplectic metachrony resulting in large velocity gradients and vortex-like flow patterns.

Graphical abstract: Metachronal patterns by magnetically-programmable artificial cilia surfaces for low Reynolds number fluid transport and mixing

Supplementary files

Article information

Article type
Paper
Submitted
25 Nov 2021
Accepted
05 Apr 2022
First published
08 Apr 2022
This article is Open Access
Creative Commons BY-NC license

Soft Matter, 2022,18, 3902-3909

Metachronal patterns by magnetically-programmable artificial cilia surfaces for low Reynolds number fluid transport and mixing

R. Zhang, J. D. Toonder and P. R. Onck, Soft Matter, 2022, 18, 3902 DOI: 10.1039/D1SM01680F

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