title: Brownian motors in the microscale domain: enhancement of efficiency by noise.
abstract: We study a noisy drive mechanism for efficiency enhancement of Brownian motors operating on the microscale domain. It was proven [J. Spiechowicz et al., J. Stat. Mech. (2013) P02044] that biased noise η(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 the presence of symmetric unbiased time-periodic driving. Here, we show that within selected parameter regimes, noise η(t) of the mean value 〈η(t)〉=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 η(t) may provide a new operating principle in which micro- and nanomotors could be powered by biased noise.
authors.affiliation: Institute of Physics, University of Silesia, 40-007 Katowice, Poland.
authors.forename: J
authors.initials: J
authors.lastname: Spiechowicz
authors.affiliation: Institute of Physics, University of Augsburg, 86135 Augsburg, Germany and Nanosystems Initiative Munich, Schellingstrasse, 4, D-80799 München, Germany.
authors.forename: P
authors.initials: P
authors.lastname: Hänggi
authors.affiliation: Institute of Physics, University of Silesia, 40-007 Katowice, Poland and Silesian Center for Education and Interdisciplinary Research, University of Silesia, 41-500 Chorzów, Poland.
authors.forename: J
authors.initials: J
authors.lastname: Łuczka
mesh_terms: D008962:Models, Theoretical; D009038:Motion; D013997:Time Factors