Geometric Brownian Information Engine: Essentials for the best performance

TL;DR

This paper analyzes a Geometric Brownian Information Engine (GBIE) with feedback control, identifying optimal parameters for maximum work extraction and efficiency, and examining how entropic effects influence performance.

Contribution

It provides the first detailed analysis of GBIE performance, establishing optimal feedback parameters and elucidating the impact of entropy on work and efficacy.

Findings

Maximum work at feedback site x_f=2x_m with x_m~0.6σ

Work output is lower in entropic systems due to information loss

Maximum average displacement occurs at x_m~0.81σ

Abstract

We investigate a Geometric Brownian Information Engine (GBIE) in the presence of an error-free feedback controller that transforms the information gathered on the state of Brownian particles entrapped in monolobal geometric confinement into extractable work. Outcomes of the information engine depend on the reference measurement distance $x_m$, feedback site $x_f$ and the transverse force $G$. We determine the benchmarks for utilizing the available information in an output work and the optimum operating requisites for best work extraction. Transverse bias force ($G$) tunes the entropic contribution in the effective potential and hence the standard deviation ($\sigma$) of the equilibrium marginal probability distribution. We recognize that the amount of extracted work reaches a global maximum when $x_f = 2x_m$ with $x_m \sim 0.6\sigma$, irrespective of the extent of the entropic limitation. Because of the higher loss of information during the relaxation process, the best achievable work of a GBIE is lower in an entropic system. The feedback regulation also bears the unidirectional passage of particles. The average displacement increases with growing entropic control and is maximum when $x_m \sim 0.81\sigma$. Finally, we explore the efficacy of the information engine, a quantity that regulates the efficiency in utilizing the information acquired. With $x_f=2x_m$, the maximum efficacy reduces with increasing entropic control and shows a cross over from $2$ to $11/9$. We discover that the condition for the best efficacy depends only on the confinement length scale along the feedback direction. The broader marginal probability distribution accredits the increased average displacement in a cycle and the lower efficacy in an entropy-dominated system.