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Modelling hybrid acoustofluidic devices for enhancing nano- and micro-particle manipulation in microfluidics

Wang, Hanlin, Yuan, Fan, Xie, Zhihua ORCID: https://orcid.org/0000-0002-5180-8427, Sun, Chao, Wu, Fangda, Mikhaylov, Roman, Shen, Minghong, Yang, Jian ORCID: https://orcid.org/0000-0003-2631-4553, Zhou, You ORCID: https://orcid.org/0000-0002-1743-1291, Liang, Dongfang, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Wu, Zhenlin, Yang, Zhiyong and Yang, Xin ORCID: https://orcid.org/0000-0002-8429-7598 2023. Modelling hybrid acoustofluidic devices for enhancing nano- and micro-particle manipulation in microfluidics. Applied Acoustics 205 , 109258. 10.1016/j.apacoust.2023.109258

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Abstract

Acoustofluidic techniques are increasingly used to manipulate nano- and micro-particles in microfluidics. A wide range of acoustofluidic devices consisting of microchannels and acoustic sources have been developed for applications in biochemistry and biomedicine. In this work, two hybrid acoustofluidic devices are developed and modelled, including a double surface acoustic wave (SAW) transducer and a PZT-SAW transducer. The numerical study to these devices demonstrates a higher acoustic pressure present in the microchannel resulting in larger particle velocities in migration to the pressure nodes. The amplitude of the acoustic pressure and the pattern of the pressure distribution can be controlled in the hybrid transducers. By sweeping the height and width of the microchannel, one can identify an optimum dimension to produce intensive acoustic pressure in the PZT-SAW transducer. The particle trajectories reveal that both the SAW-SAW and PZT-SAW configurations produce significantly higher acoustic pressure and particle velocity in the microchannel. This work provides new insights to design acoustofluidic devices with more than one SAW transducer to effectively manipulate micro- and nano-particles.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Medicine
Computer Science & Informatics
Publisher: Elsevier
ISSN: 0003-682X
Date of First Compliant Deposit: 10 February 2023
Date of Acceptance: 5 February 2023
Last Modified: 25 Jun 2024 19:00
URI: https://orca.cardiff.ac.uk/id/eprint/156880

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