New aspects on Current enhancement in Brownian motors driven by non Gaussian noises

TL;DR

This paper explores how non-Gaussian noise influences the current and efficiency of Brownian motors, providing analytical insights into the effects of noise distribution deviations and tail behaviors.

Contribution

It offers new analytical results using an adiabatic approximation and examines the impact of long probability distribution tails on Brownian motor performance.

Findings

Non-Gaussian noise enhances current and efficiency in Brownian motors.

Analytical results based on adiabatic approximation elucidate noise effects.

Long tails in probability distributions significantly influence motor behavior.

Abstract

Recent studies on Brownian motors driven by colored non Gaussian noises have shown that the departure of the noise distribution from Gaussian behavior induces an enhancement of its current and efficiency. Here we discuss some new aspects of this phenomenon focusing in some analytical results based in an adiabatic approximation, and in the analysis of the long probability distribution tails' role.