Extracting maximum power from active colloidal heat engines

TL;DR

This paper investigates how active colloidal heat engines can be optimized for maximum power output, revealing that cyclic activity variations can enhance performance beyond passive limits, supported by mean-field theory and simulations.

Contribution

It introduces an adiabatic mean-field approach to optimize active colloidal heat engines and demonstrates conditions for maximum power output, including cyclic activity variations.

Findings

Active engines at constant activity produce less power than passive ones.

Cyclic variations in activity can optimize power output.

Simulations confirm mean-field predictions beyond approximations.

Abstract

Colloidal heat engines extract power out of a fluctuating bath by manipulating a confined tracer. Considering a self-propelled tracer surrounded by a bath of passive colloids, we optimize the engine performances based on the maximum available power. Our approach relies on an adiabatic mean-field treatment of the bath particles which reduces the many-body description into an effective tracer dynamics. It leads us to reveal that, when operated at constant activity, an engine can only produce less maximum power than its passive counterpart. In contrast, the output power of an isothermal engine, operating with cyclic variations of the self-propulsion without any passive equivalent, exhibits an optimum in terms of confinement and activity. Direct numerical simulations of the microscopic dynamics support the validity of these results even beyond the mean-field regime, with potential relevance to the design of experimental engines.