Data‐Driven Intelligent Manipulation of Particles in Microfluidics

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.

Objekt anzeigen

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Data‐Driven Intelligent Manipulation of Particles in Microfluidics ; day:20 ; month:12 ; year:2022 ; extent:8 ; Advanced science ; (20.12.2022) (gesamt 8)

Beteiligte, Orts- und Zeitangaben

• Urheber: Fang, Wen‐Zhen ; Xiong, Tongzhao ; Pak, On Shun ; Zhu, Lailai

Weitere Informationen

• DOI: 10.1002/advs.202205382
• URN: urn:nbn:de:101:1-2022122114082014820275
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.