Optical Micromanipulations based on Model Predictive Control of Thermoviscous Flows

TL;DR

This paper introduces a model predictive control method using thermoviscous flow models to achieve high-precision, stable optical micromanipulation of particles, overcoming challenges like hydrodynamic coupling and instabilities.

Contribution

It presents a stochastic optimization approach based on an analytical thermoviscous flow model for precise, rule-free particle manipulation in crowded environments.

Findings

Achieves sub-micrometer alignment accuracy.

Avoids hydrodynamic instabilities during manipulation.

Utilizes flow decay for refined positioning.

Abstract

High-precision micromanipulation techniques, including optical tweezers and hydrodynamic trapping, have garnered wide-spread interest. Recent advances in optofluidic multiplexed assembly and microrobotics demonstrate significant progress, particularly by iteratively applying laser-induced, localized flow fields to manipulate microparticles in viscous solutions. However, these approaches still face challenges such as undesired hydrodynamic coupling and instabilities when multiple particles are brought into close proximity. By leveraging an analytical model of thermoviscous flows, this work introduces a stochastic optimization approach that selects flow fields for precise particle arrangement without relying on rule-based heuristics. Through minimizing a comprehensive objective function, the method achieves sub-micrometer alignment accuracy even in a crowded setting, avoiding instabilities driven by undesired coupling or particle collisions. An autonomously emerging "action at a distance" strategy - placing the laser scan path farther from the manipulated particles over time - exploits the $1/r^2$ decay of thermoviscous flow to refine positioning. Overall, objective function-based model predictive control enhances the versatility of automated optofluidic manipulations, opening new avenues in assembly, micromanufacturing, robotics, and life sciences.