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Tracer dynamics in crowded active-particle suspensions.

Julian Reichert1, Thomas Voigtmann1,2

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This summary is machine-generated.

We studied active Brownian particles (ABPs) in crowded systems. High densities lead to sub-diffusive motion, contrasting with super-diffusion from self-propulsion.

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Area of Science:

  • Physics
  • Soft Matter Physics
  • Statistical Mechanics

Background:

  • Active Brownian particles (ABPs) exhibit complex dynamics influenced by self-propulsion and interactions.
  • Crowded environments introduce significant memory effects impacting particle trajectories.
  • Understanding tracer particle dynamics in active and passive host systems is crucial for comprehending collective behavior.

Purpose of the Study:

  • To investigate the dynamics of active and passive tracer particles in crowded active and passive environments.
  • To derive exact equations for mean-squared displacement (MSD) using projection operator techniques.
  • To analyze emergent dynamical regimes using a mode-coupling theory for ABPs.

Main Methods:

  • Projection operator technique for deriving exact mean-squared displacement (MSD) equations.
  • Mode-coupling theory for active Brownian particles (ABP-MCT) for approximate evaluation.
  • Event-driven Brownian dynamics (ED-BD) simulations for validation.

Main Results:

  • Exact MSD equations were derived, extending solutions to dense systems.
  • Strong memory effects arise from tracer-host particle interactions.
  • A competition between super-diffusive (self-propulsion) and sub-diffusive (density-induced) regimes was identified.
  • ABP-MCT predictions showed good agreement with ED-BD simulations.

Conclusions:

  • The study provides a theoretical framework for understanding active Brownian particle dynamics in crowded media.
  • Density-induced interactions can lead to sub-diffusive behavior, counteracting self-propelled motion.
  • The developed theory accurately predicts emergent dynamical regimes observed in simulations.