Strong enhancement of current, efficiency and mass separation in Brownian motors driven by non Gaussian noises

TL;DR

This paper demonstrates that non-Gaussian colored noise significantly enhances current, efficiency, and mass separation in Brownian motors, especially when the noise distribution follows a power law for q>1.

Contribution

It introduces a model of Brownian motors driven by non-Gaussian noise and shows the substantial improvements in performance metrics compared to Gaussian noise scenarios.

Findings

Enhanced current and efficiency with non-Gaussian noise for q>1

Significant increase in mass separation capability with inertia and non-Gaussian noise

Power-law noise distribution leads to marked performance improvements

Abstract

We study a Brownian motor driven by a colored non Gaussian noise source with a $q$-dependent probability distribution, where $q$ is a parameter indicating the departure from Gaussianity. For $q=1$ the noise is Gaussian (Ornstein--Uhlenbeck), while, for $q>1$, the probability distribution falls like a power law. In the latter case, we find a marked enhancement of both the current and the efficiency of the Brownian motor in the overdamped regime. We also analyze the case with inertia and show that, again for $q > 1$, a remarkable increase of the ratchet's mass separation capability is obtained.