Evolving motility of active droplets is captured by a self-repelling random walk model
arxiv(2024)
摘要
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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