Evolving motility of active droplets is captured by a self-repelling random walk model

TL;DR

This paper combines experiments and a non-Markovian self-repelling random walk model to accurately describe the evolving motility of active droplets, revealing insights into their memory effects, interactions, and collective behavior.

Contribution

It introduces a novel non-Markovian model for active droplet motility that captures complex behaviors and quantifies underlying physical parameters from experimental data.

Findings

Model accurately predicts droplet trajectories

Estimates effective temperature and propulsion coupling

Explains memory effects and collective diffusion phenomena

Abstract

Swimming droplets are a class of active particles whose motility changes as a function of time due to shrinkage and self-avoidance of their trail. Here we combine experiments and theory to show that our non-Markovian droplet (NMD) model, akin to a true self-avoiding walk [1], quantitatively captures droplet motion. We thus estimate the effective temperature arising from hydrodynamic flows and the coupling strength of the propulsion force as a function of fuel concentration. This framework explains a broad range of phenomena, including memory effects, solute-mediated interactions, droplet hovering above the surface, and enhanced collective diffusion.