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Summary
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This study introduces an active Brownian information engine (ABIE) that extracts work from heat using mutual information. Optimal performance is achieved when active and thermal timescales are balanced, enabling significant work extraction.

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Area of Science:

  • Thermodynamics
  • Statistical Mechanics
  • Active Matter Physics

Background:

  • Information engines utilize feedback and mutual information to extract work from heat baths.
  • Active Brownian particles exhibit unique dynamics influenced by both thermal noise and self-propulsion.

Purpose of the Study:

  • To design and analyze a feedback-driven active Brownian information engine (ABIE).
  • To investigate the performance criteria and work extraction capabilities of the ABIE.
  • To determine how timescale ratios influence the engine's efficiency.

Main Methods:

  • Modeling an overdamped active Ornstein-Uhlenbeck particle in a 1D harmonic potential.
  • Analyzing particle dynamics under thermal and active reservoirs with distinct timescales (τr, τa).
  • Evaluating work extraction based on steady-state distribution and mutual information.

Main Results:

  • ABIE performance depends on the ratio of active to thermal correlation times (τa/τr).
  • Colossal work extraction (∼0.202γ(D+Da)) is possible in the limit of τa/τr → 0.
  • Work extraction converges to the passive limit (∼0.202γD) as τa/τr → high.
  • At τa/τr = 1, half the upper bound of excess work is achieved regardless of reservoir strength.

Conclusions:

  • The ABIE's efficiency is tunable via the interplay between active and thermal fluctuations.
  • Balancing timescales (τa/τr = 1) offers a robust operating point for work extraction.
  • Active Brownian particles demonstrate enhanced displacement in feedback cycles compared to thermal analogs.