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arXiv 2602.04732cond-mat.softcond-mat.stat-mech
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在可调节网络上运动的活性粒子的输运性质

Transport Properties of Active Particles Moving on Adjustable Networks

Universidade Estadual Paulista(圣保罗州立大学);ICTP South American Institute for Fundamental Research(国际理论物理研究所南美基础研究院);Universidade de São Paulo(圣保罗大学);Heinrich-Heine-Universität Düsseldorf(杜塞尔多夫海因里希·海涅大学);Cornell University(康奈尔大学)

William G. C. Oropesa, P. de Castro, Hartmut Löwen, Danilo B. Liarte

AI总结:

本文提出活性粒子在可调节三角格子上运动的模型,通过轨迹阻塞机制研究输运性质,发现高持久性下轨迹阻塞导致扩散率单调增加,与体积排斥效应相反。

摘要:

活性自适应物质因其丰富且很大程度上未解释的动力学以及其与广泛合成和生物材料的相关性而引起了相当大的兴趣。这类系统的一个重要子类由活性粒子组成,这些粒子可以重塑它们在其中运动的网络。在这里,我们引入了一个最小但多功能的活性粒子在可调节网络上运动的模型。在该模型中,粒子沿着三角格子的链接进行离散的奔跑-翻滚运动,并留下一条暂时阻塞的链接轨迹。这些关闭的链接不能被其他粒子穿越,只有在特征愈合时间后才会重新打开。由此产生的轨迹介导的阻塞机制与更熟悉的相互作用(如排除体积效应)根本不同。在高持久性极限下,我们发现两种机制之间存在质的对比:而空间阻塞导致扩散率随持久性增加而降低,轨迹诱导的阻塞导致扩散率单调增加。我们表征了这一根本差异以及相关的、意想不到的输运性质,并讨论了我们的发现的潜在应用。

英文摘要:

Active adaptive matter has attracted considerable interest due to its rich, largely unexplained dynamics and its relevance to a wide range of synthetic and biological materials. An important subclass of such systems consists of active particles that can remodel the network in which they move. Here, we introduce a minimal yet versatile model of active particles moving on an adjustable network. In this model, particles undergo discrete run-and-tumble motion along the links of a triangular lattice and leave behind a trail of temporarily blocked links. These closed links cannot be traversed by other particles and reopen only after a characteristic healing time. The resulting trail-mediated blocking mechanism is fundamentally distinct from more familiar interactions such as excluded-volume effects. In the high-persistence limit, we find a qualitative contrast between the two mechanisms: while steric blocking leads to reduced diffusivity with increasing persistence, trail-induced blocking causes diffusivity to increase monotonically. We characterize this fundamental difference and the associated, unexpected transport properties, and discuss potential applications of our findings.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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