Evolving motility of active droplets is captured by a self-repelling random walk model
Soft Condensed Matter
2024-05-17 v1
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.
Keywords
Cite
@article{arxiv.2405.09636,
title = {Evolving motility of active droplets is captured by a self-repelling random walk model},
author = {Wenjun Chen and Adrien Izzet and Ruben Zakine and Eric Clément and Eric Vanden-Eijnden and Jasna Brujic},
journal= {arXiv preprint arXiv:2405.09636},
year = {2024}
}