Tuning the performance of a micrometer-sized Stirling engine through reservoir engineering

TL;DR

This paper investigates how a colloidal Stirling engine's performance is affected by non-Gaussian, memoryless noise in its thermal reservoir, revealing that noise statistics can be used to tune nano/micro machine efficiency.

Contribution

It provides the first analysis of a colloidal Stirling engine operating between non-Gaussian and Gaussian baths, showing how noise statistics influence engine irreversibility and power output.

Findings

Non-Gaussian noise causes earlier irreversibility onset.

Engine performance can be tuned by changing noise statistics.

Maximum power shifts with noise type and operating speed.

Abstract

Colloidal heat engines are paradigmatic models to understand the conversion of heat into work in a noisy environment - a domain where biological and synthetic nano/micro machines function. While the operation of these engines across thermal baths is well-understood, how they function across baths with noise statistics that is non-Gaussian and also lacks memory, the simplest departure from equilibrium, remains unclear. Here we quantified the performance of a colloidal Stirling engine operating between an engineered \textit{memoryless} non-Gaussian bath and a Gaussian one. In the quasistatic limit, the non-Gaussian engine functioned like an equilibrium one as predicted by theory. On increasing the operating speed, due to the nature of noise statistics, the onset of irreversibility for the non-Gaussian engine preceded its thermal counterpart and thus shifted the operating speed at which power is maximum. The performance of nano/micro machines can be tuned by altering only the nature of reservoir noise statistics.