Article: Sehgal, P; Kirby, BJ; “Separation of 300 and 100 nm Particles in Fabry-Perot Acoustofluidic Resonators”, Analytical Chemistry, 89 (22):12192-12200
Abstract: Separation of particles on the order of 100 nm with acoustophoresis has been challenging to date because of the competing natures of the acoustic radiation force and acoustic streaming on the particles. In this work, we present a surface acoustic wave (SAW)-based device that integrates a FabryPerot type acoustic resonator into a microfluidic channel to separate submicrometer particles. This configuration enhances the overall acoustic radiation force on the particles and thereby offers controlled manipulation of particles as small as 300 nm. Additionally, SAW-based excitation generates high-frequency acoustic waves in the system relative to bulk acoustic wave (BAW)-based actuation, which suppresses Rayleigh streaming effects on the submicrometer particles. We demonstrate a continuous-flow acoustophoretic separation of 300 and 100 nm particles in our device with a separation efficiency of 86.3%. We also present an analytical stochastic method to model the transport of submicrometer particles in the device and predict the migration trajectories as a function of acoustic and velocity potential field strengths. Our model incorporates particle diffusion, which is important for small particles, and successfully predicts the size-dependent separation modality of our system. This device can be used for several applications in microfluidics that require sorting of the submicrometer particles, and the analytical method can also be extended to predict the particle transport in other systems.
Funding Acknowledgement: Center on the Physics of Cancer Metabolism from the National Cancer Institute [1U54CA210184-01]; National Science Foundation [ECCS-1542081]
Funding Text: This work was supported by the Center on the Physics of Cancer Metabolism through Award Number 1U54CA210184-01 from the National Cancer Institute and performed in part at the Cornell Nanoscale Facility (CNF), which is supported by the National Science Foundation (Grant ECCS-1542081). We also acknowledge the Cornell SonicMEMS Lab and the Shuler Lab at Cornell.