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Issue 3, 2016
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Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting

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Abstract

High-speed sorting is an essential process in a number of clinical and research applications, where single cells, droplets and particles are segregated based on their properties in a continuous flow. With recent developments in the field of microscale actuation, there is increasing interest in replicating the functions available to conventional fluorescence activated cell sorting (FACS) flow cytometry in integrated on-chip systems, which have substantial advantages in cost and portability. Surface acoustic wave (SAW) devices are ideal for many acoustofluidic applications, and have been used to perform such sorting at rates on the order of kHz. Essential to the accuracy of this sorting, however, is the dimensions of the region over which sorting occurs, where a smaller sorting region can largely avoid inaccurate sorting across a range of sample concentrations. Here we demonstrate the use of flow focusing and a highly focused SAW generated by a high-frequency (386 MHz), 10 μm wavelength set of focused interdigital transducers (FIDTs) on a piezoelectric lithium niobate substrate, yielding an effective sorting region only ~25 μm wide, with sub-millisecond pulses generated at up to kHz rates. Furthermore, because of the use of high frequencies, actuation of particles as small as 2 μm can be realized. Such devices represent a substantial step forward in the evolution of highly localized forces for lab-on-a-chip microfluidic applications.

Graphical abstract: Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting

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Publication details

The article was received on 29 okt. 2015, accepted on 18 nóv. 2015 and first published on 18 nóv. 2015


Article type: Paper
DOI: 10.1039/C5LC01335F
Citation: Lab Chip, 2016,16, 471-479
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    Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting

    D. J. Collins, A. Neild and Y. Ai, Lab Chip, 2016, 16, 471
    DOI: 10.1039/C5LC01335F

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