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arXiv · 2405.09636

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

Wenjun Chen·Adrien Izzet·Ruben Zakine·Eric Clément·Eric Vanden-Eijnden·Jasna Brujic
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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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Wenjun Chen, Adrien Izzet, Ruben Zakine, Eric Clément, Eric Vanden-Eijnden, Jasna Brujic. 2024-05-15. Evolving motility of active droplets is captured by a self-repelling random walk model. https://arxiv.org/abs/2405.09636

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