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

2024/05/15 by Wenjun Chen, Chen, Wenjun, Adrien Izzet +9 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #FOS: Physical sciences #Micro and Nano Robotics #Mobile Crowdsensing and Crowdsourcing #Modular Robots and Swarm Intelligence #Soft Condensed Matter (cond-mat.soft)

paper · pdf · doi:10.48550/arxiv.2405.09636

openalex publication_date 2024/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

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• Information bounds the robustness of self-organized systems
• Accelerated first-passage dynamics in a non-Markovian feedback Ornstein--Uhlenbeck process

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