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

SUBMITTER: Fang WZ 

PROVIDER: S-EPMC9929134 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature