Data-Driven Intelligent Manipulation of Particles in Microfluidics

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.

Automated summary

Automated manipulation of small particles using external fields is an emerging technique widely used in different areas. Existing reduced-order physical models for particle manipulation are limited to highly idealized settings and do not account for complex nonlinear processes. In this study, the authors propose a data-driven architecture based on hydrodynamic manipulation and artificial neural networks to achieve precise and flexible control of particles in microfluidic systems. They successfully demonstrate various advanced manipulations, revolutionizing automated particle manipulation and showcasing the potential of data-driven approaches in improving microfluidic technologies.