Colossal power extraction from active cyclic Brownian information engines

TL;DR

This paper introduces a model for a Brownian information engine operating in an active bath, demonstrating it can extract significantly more work than traditional thermal engines, with implications for designing microscopic motors.

Contribution

The study develops a new model for a cyclic Brownian information engine in an active bath, revealing enhanced work extraction and a modified second law with an effective temperature.

Findings

Active engine exceeds thermal bounds in work extraction.

Derived a fluctuation theorem including active bath mutual information.

Engine power peaks at a finite cycle period.

Abstract

Brownian information engines can extract work from thermal fluctuations by utilizing information. So far, the studies on Brownian information engines consider the system in a thermal bath; however, many processes in nature occur in a nonequilibrium setting, such as the suspensions of self-propelled microorganisms or cellular environments called an active bath. Here, we introduce an archetypal model for Maxwell-demon type cyclic Brownian information engine operating in a Gaussian correlated active bath. The active engine can extract more work than its thermal counterpart, exceeding the bound set by the second law of information thermodynamics. We obtain a general integral fluctuation theorem for the active engine that includes additional mutual information gained from the active bath with a unique effective temperature. This effective description modifies the second law and provides a new upper bound for the extracted work. Unlike the passive information engine operating in a thermal bath, the active information engine extracts colossal power that peaks at the finite cycle period. Our study provides fundamental insights into the design and functioning of synthetic and biological submicron motors in active baths under measurement and feedback control.