论文标题
拥挤的活动粒子悬架中的示踪动力学
Tracer Dynamics in Crowded Active-Particle Suspensions
论文作者
论文摘要
我们通过使用投影式操纵器方案在密集的被动布朗颗粒系统和密集的活性棕色粒子系统中的被动式示踪剂系统建模的拥挤环境中得出了活性布朗颗粒（ABP）的均方位移（MSD）的运动方程。示踪剂粒子与致密宿主环境的相互作用会产生强烈的记忆效应。我们在活跃的布朗颗粒（ABP-MCT）的玻璃转变的最近开发的模式耦合理论的框架中评估这些框架，我们讨论了活动引起的超扩散运动和密度诱导的亚降低运动的各种状态。该理论的预测与事件驱动的布朗动力学模拟方案的结果非常吻合，该方案是二维活动性布朗磁盘动力学的结果。
We derive equations of motion for the mean-squared displacement (MSD) of an active Brownian particle (ABP) in a crowded environment modeled by a dense system of passive Brownian particles, and of a passive tracer particle in a dense active-Brownian particle system, using a projection-operator scheme. The interaction of the tracer particle with the dense host environment gives rise to strong memory effects. Evaluating these approximately in the framework of a recently developed mode-coupling theory for the glass transition in active Brownian particles (ABP-MCT), we discuss the various regimes of activity-induced super-diffusive motion and density-induced sub-diffusive motion. The predictions of the theory are shown to be in good agreement with results from an event-driven Brownian dynamics simulation scheme for the dynamics of two-dimensional active Brownian hard disks.
