Brownian motors in micro-scale domain: Enhancement of efficiency by noise

TL;DR

This paper demonstrates that biased noise can significantly enhance the efficiency and speed of Brownian motors at the micro-scale, outperforming deterministic forces in certain regimes, with potential experimental applications.

Contribution

It shows that biased noise of mean value equal to the force can outperform static forces in driving Brownian motors, increasing efficiency and reducing fluctuations.

Findings

Noise-driven motors move faster with smaller fluctuations.

Efficiency increases several times with biased noise.

Results applicable to Josephson junction experiments.

Abstract

We study a noisy drive mechanism for efficiency enhancement of Brownian motors operating on the micro-scale domain. It was proven [J. Spiechowicz et al., J. Stat. Mech. P02044, (2013)] that biased noise $\eta(t)$ can induce normal and anomalous transport processes similar to those generated by a static force $F$ acting on inertial Brownian particles in a reflection-symmetric periodic structure in presence of symmetric unbiased time-periodic driving. Here, we show that within selected parameter regimes, noise $\eta(t)$ of the mean value $\langle \eta(t) \rangle = F$ can be significantly more effective than the deterministic force $F$: the motor can move much faster, its velocity fluctuations are much smaller and the motor efficiency increases several times. These features hold true in both normal and absolute negative mobility regimes. We demonstrate this with detailed simulations by resource to generalized white Poissonian noise. Our theoretical results can be tested and corroborated experimentally by use of a setup that consists of a resistively and capacitively shunted Josephson junction. The suggested strategy to replace $F$ by $\eta(t)$ may provide a new operating principle in which micro- and nanomotors could be powered by biased noise.