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Issue 43, 2019
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Acoustic fields and microfluidic patterning around embedded micro-structures subject to surface acoustic waves

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Abstract

Recent research has shown that interactions between acoustic waves and microfluidic channels can generate microscale interference patterns with the application of a traveling surface acoustic wave (SAW), effectively creating standing wave patterns with a traveling wave. Forces arising from this interference can be utilized for precise manipulation of micron-sized particles and biological cells. The patterns that have been produced with this method, however, have been limited to straight lines and grids from flat channel walls, and where the spacing resulting from this interference has not previously been comprehensively explored. In this work we examine the interaction between both straight and curved channel interfaces with a SAW to derive geometrically deduced analytical models. These models predict the acoustic force-field periodicity near a channel interface as a function of its orientation to an underlying SAW, and are validated with experimental and simulation results. Notably, the spacing is larger for flat walls than for curved ones and is dependent on the ratio of sound speeds in the substrate and fluid. Generating these force-field gradients with only travelling waves has wide applications in acoustofluidic systems, where channel interfaces can potentially support a range of patterning, concentration, focusing and separation activities by creating locally defined acoustic forces.

Graphical abstract: Acoustic fields and microfluidic patterning around embedded micro-structures subject to surface acoustic waves

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Publication details

The article was received on 09 May 2019, accepted on 17 Oct 2019 and first published on 21 Oct 2019


Article type: Paper
DOI: 10.1039/C9SM00946A
Soft Matter, 2019,15, 8691-8705

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    Acoustic fields and microfluidic patterning around embedded micro-structures subject to surface acoustic waves

    D. J. Collins, R. O’Rorke, A. Neild, J. Han and Y. Ai, Soft Matter, 2019, 15, 8691
    DOI: 10.1039/C9SM00946A

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