Colossal Power Extraction from Active Cyclic Brownian Information Engines.

Area of Science:

• Thermodynamics
• Statistical Mechanics
• Active Matter Physics

Background:

• Brownian information engines typically operate in thermal baths, extracting work from thermal fluctuations using information.
• Natural environments like cellular systems or microorganism suspensions represent nonequilibrium active baths, differing significantly from passive thermal baths.

Purpose of the Study:

• To introduce and analyze a Maxwell's demon type cyclic Brownian information engine operating in a Gaussian correlated active bath.
• To investigate the potential for enhanced work extraction in active baths compared to traditional thermal baths.

Main Methods:

• Development of an archetypal model for a cyclic Brownian information engine.
• Application of integral fluctuation theorems adapted for active baths.
• Analysis of mutual information and effective temperature in the active bath.

Main Results:

• The active Brownian information engine demonstrates the capability to extract more work than its thermal counterpart.
• A general integral fluctuation theorem for active engines is derived, incorporating mutual information from the active bath.
• A unique effective temperature is identified, modifying the generalized second law and establishing a new upper bound for extracted work.
• Colossal power extraction is observed, peaking at a finite cycle period, unlike passive engines in thermal baths.

Conclusions:

• Active baths offer a superior medium for work extraction by Brownian information engines compared to thermal baths.
• The study provides fundamental insights into the operation of synthetic and biological submicrometer motors in active environments under feedback control.
• The findings pave the way for designing more efficient nanoscale engines by leveraging nonequilibrium conditions.