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.

Automated electrokinetic stretcher for manipulating nanomaterials

“…We highlight that there is much flexibility in extending the abilities of the platform, including the integration of data-driven manipulation approaches. 41 One possibility we envision exploring is the adaptation of the platform for sorting purposes. We can trap and observe the objects at the stagnation point, then depending on the observed properties, direct the object into a specified outlet.…”

Optically Programmable Living Microrouter in Vivo

“…The existing microrouting techniques include the hydrodynamic strategy, magnetic tweezer strategy, optical tweezer strategy, and optically actuated hydrodynamic manipulation strategy. By integrating the nonlinear feedback control [ 34 ] or trainable artificial neural network, [ 35 ] the hydrodynamic strategy could achieve the diversified routing while impose no specific constraints on the targets’ physicochemical property and minimize the possible damages to the biological specimens. However, they are usually operated at a large scale, and challenging to be implanted in vivo due to the required connection with microfluidic devices.…”

Acoustic Manipulation of Particles in Microfluidic Chips with an Adaptive Controller that Models Acoustic Fields

“…This means that the model has clear interpretability and can be compared to experimentally observe acoustic field shapes, like we have done. This contrasts with black‐box models, such as neural networks, [ 42,54,55 ] which can be difficult to interpret and therefore it is difficult to understand why the controller acts the way it does. However, some caution in interpreting the models is in order: any other phenomena transporting the particles, such as acoustic streaming [ 56–58 ] or fluid flows, [ 59 ] will affect the modeling results.…”