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Generalized continuous Maxwell demons.

Juan P Garrahan1,2, Felix Ritort3,4

  • 1School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, England, United Kingdom.

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|April 19, 2023
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Summary
This summary is machine-generated.

Generalized continuous Maxwell demons (GCMDs) offer enhanced thermodynamic efficiency for information-to-energy conversion. These models outperform traditional methods and are suitable for biological systems in information-rich environments.

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Area of Science:

  • Thermodynamics
  • Statistical Mechanics
  • Information Theory

Background:

  • Maxwell's demon paradox explores the relationship between information and thermodynamics.
  • Continuous Maxwell demons and Szilard engines are theoretical models for information-driven engines.
  • Understanding energy extraction from information is crucial for nanoscale systems.

Purpose of the Study:

  • Introduce generalized continuous Maxwell demons (GCMDs) combining Szilard and continuous Maxwell demon protocols.
  • Analyze work extraction, information content, and efficiency fluctuations in GCMD models.
  • Investigate the impact of finite-time operations and temporal correlations on information-to-energy conversion.

Main Methods:

  • Derivation of cycle distributions for work, information, and time.
  • Computation of power and information-to-work efficiency fluctuations.
  • Mapping finite-time protocols to a three-state GCMD model.

Main Results:

  • Maximum power efficiency achieved by opportunistic continuous GCMD protocols in rare-event regimes.
  • Finite-time correlations enhance information-to-work conversion efficiency.
  • GCMD models demonstrate superior thermodynamic efficiency compared to the single-measurement Szilard engine.

Conclusions:

  • GCMD models provide a more efficient framework for information-to-energy conversion.
  • Temporal correlations play a key role in optimizing energy conversion from information.
  • GCMD models are advantageous for describing biological processes in information-rich environments.