Autonomous Ratcheting by Stochastic Resetting

TL;DR

This paper introduces a stochastic resetting mechanism for Brownian particles in periodic potentials, demonstrating how it can induce directed motion in asymmetric environments, with potential applications in modeling molecular motors.

Contribution

It generalizes stochastic resetting to include potential well resets, showing how this induces rectified motion in asymmetric potentials, a novel approach for autonomous particle transport.

Findings

Maximum drift speed at optimal resetting time

Rectification of unbiased diffusion in asymmetric potentials

Potential modeling of molecular motors and microorganisms

Abstract

We propose a generalization of the stochastic resetting mechanism for a Brownian particle diffusing in a one-dimensional periodic potential: randomly in time, the particle gets reset at the bottom of the potential well it was in. Numerical simulations show that in mirror asymmetric potentials, stochastic resetting rectifies the particle's dynamics, with maximum drift speed for an optimal average resetting time. Accordingly, an unbiased Brownian tracer diffusing on an asymmetric substrate can rectify its motion by adopting an adaptive stop-and-go strategy. Our proposed ratchet mechanism can model directed autonomous motion of molecular motors and micro-organisms