Programmable Persistent Random Walks in Active Brownian Particles Govern Emergent Dynamics

TL;DR

This paper introduces a versatile experimental platform that encodes various programmable persistent random walks in active Brownian particles, enabling the study of emergent dynamics and organization in active matter.

Contribution

The authors develop a method combining light and magnetic control to encode multiple motion modes in ABPs, allowing on-demand switching and complex trajectory steering.

Findings

Enabled programmable Levy, run-and-tumble, self-avoiding, and Gaussian walks in ABPs.

Demonstrated on-demand switching between different motion modes within a single experiment.

Showed that propulsion modes affect clustering and collective behavior in active particles.

Abstract

Self-propelled particles serve as minimal models for emulating the dynamic self-organization of microorganisms, yet most synthetic systems remain limited to a single mode of motion, namely active Brownian particles (ABPs). Here, we present an experimental strategy to encode various persistent random walks in ABPs by combining light-modulated propulsion strength with magnetic control of propulsion direction. Our system enables programmable Levy walks with tunable step-length distributions, run-and-tumble dynamics, self-avoiding random walks, and Gaussian walks, with on-demand switching between motion modes within a single experiment. In addition, particles are steered along complex trajectories such as Fibonacci spirals and nested polygons. Beyond single-particle behavior, we show that propulsion modes influence clustering dynamics by comparing ABPs with chiral active particles undergoing circular motion. These results establish a versatile platform for investigating how encoded motion at the level of individual particles governs transport, search strategies, and emergent organization in active matter systems.