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自排斥随机游走模型捕捉到活性液滴不断演变的运动性。

Evolving Motility of Active Droplets Is Captured by a Self-Repelling Random Walk Model.

作者信息

Chen Wenjun, Izzet Adrien, Zakine Ruben, Clément Eric, Vanden-Eijnden Eric, Brujic Jasna

机构信息

New York University, Center for Soft Matter Research, Physics Department, New York, New York 10003, USA.

Université Paris-Saclay, INRAE, AgroParisTech, UMR SayFood, 91120 Palaiseau, France.

出版信息

Phys Rev Lett. 2025 Jan 10;134(1):018301. doi: 10.1103/PhysRevLett.134.018301.

Abstract

In living matter, concentration gradients of nutrients carve the motility of microorganisms in a heterogeneous environment. Here, we use swimming droplets as a model system to study how swimmer-trail interactions guide locomotion. Combining experiments and theory, we show that our non-Markovian droplet model quantitatively captures droplet motility. The two fit parameters provide the first estimate of the effective temperature arising from hydrodynamic flows and the coupling strength of the propulsion force. This framework is general and explains memory effects, droplet hovering, and enhanced collective motion.

摘要

在生物体内,营养物质的浓度梯度决定了微生物在异质环境中的运动。在此,我们使用游动液滴作为模型系统来研究游动者-轨迹相互作用如何引导运动。结合实验与理论,我们表明我们的非马尔可夫液滴模型能够定量地捕捉液滴的运动。这两个拟合参数首次估计了由流体动力流产生的有效温度以及推进力的耦合强度。该框架具有普遍性,解释了记忆效应、液滴悬停和增强的集体运动。

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