Please use this identifier to cite or link to this item: https://doi.org/10.1002/advs.202205382
Title: Data-Driven Intelligent Manipulation of Particles in Microfluidics
Authors: Wen-Zhen Fang
Tongzhao Xiong 
On Shun Pak
Lailai Zhu 
Issue Date: 20-Dec-2022
Citation: Wen-Zhen Fang, Tongzhao Xiong, On Shun Pak, Lailai Zhu (2022-12-20). Data-Driven Intelligent Manipulation of Particles in Microfluidics. Advanced Science 10 : 2205382. ScholarBank@NUS Repository. https://doi.org/10.1002/advs.202205382
Rights: CC0 1.0 Universal
Abstract: Automated manipulation of small particles using external (e.g., magnetic, electric and acoustic) fields has been an emerging technique widely used in different areas. The manipulation typically necessitates a reduced-order physical model characterizing the field-driven motion of particles in a complex environment. Such models are available only for highly idealized settings but are absent for a general scenario of particle manipulation typically involving complex nonlinear processes, which has limited its application. In this work, the authors present a data-driven architecture for controlling particles in microfluidics based on hydrodynamic manipulation. The architecture replaces the difficult-to-derive model by a generally trainable artificial neural network to describe the kinematics of particles, and subsequently identifies the optimal operations to manipulate particles. The authors successfully demonstrate a diverse set of particle manipulations in a numerically emulated microfluidic chamber, including targeted assembly of particles and subsequent navigation of the assembled cluster, simultaneous path planning for multiple particles, and steering one particle through obstacles. The approach achieves both spatial and temporal controllability of high precision for these settings. This achievement revolutionizes automated particle manipulation, showing the potential of data-driven approaches and machine learning in improving microfluidic technologies for enhanced flexibility and intelligence.
Source Title: Advanced Science
URI: https://scholarbank.nus.edu.sg/handle/10635/249691
ISSN: 2198-3844
DOI: 10.1002/advs.202205382
Rights: CC0 1.0 Universal
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