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Area of Science:

  • Soft Matter Physics
  • Active Matter Systems
  • Statistical Mechanics

Background:

  • Motility-induced phase separation (MIPS) describes how self-propelled particles spontaneously form dense clusters.
  • Understanding the dynamics of these clusters, including their growth and aggregation, is crucial for active matter research.
  • Existing models often focus on Ostwald ripening, but active systems may exhibit different aggregation mechanisms.

Purpose of the Study:

  • To investigate the aggregation mechanisms and dynamics of clusters formed by active Brownian disks.
  • To characterize the growth of these clusters and identify key parameters influencing their behavior.
  • To compare the dynamics of active clusters with a passive system to highlight the role of self-propulsion.

Main Methods:

  • Development and application of a novel algorithm for tracking cluster trajectories.
  • Numerical simulations of active Brownian disks undergoing motility-induced phase separation.
  • Comparative analysis between active and passive Brownian disk systems.

Main Results:

  • Identification of an aggregation mechanism distinct from Ostwald ripening.
  • Determination of a dynamic exponent for cluster growth, z=3, within explored numerical timescales.
  • Characterization of cluster self-propulsion with enhanced diffusivity (D∼Pe²/√M).
  • Observation of large, fractal aggregation structures composed of diverse hexatic orders coexisting with smaller, uniform clusters.
  • Significant differences observed between active and passive system dynamics.

Conclusions:

  • Active Brownian disks aggregate through a mechanism beyond Ostwald ripening, leading to fractal structures.
  • Cluster self-propulsion and enhanced diffusivity are key drivers of aggregation and complex morphology in active systems.
  • The behavior of active clusters is fundamentally different from passive systems, underscoring the importance of activity in emergent phenomena.