Brownian Vortexes
Bo Sun, Jiayi Lin, Ellis Darby, Alexander Y. Grosberg, David G., Grier

TL;DR
This paper introduces the concept of Brownian vortexes, where particles in non-conservative force fields exhibit steady-state circulation, demonstrating flux reversal both theoretically and experimentally in optical trapping systems.
Contribution
It reveals a new class of stochastic heat engines called Brownian vortexes, showing how circulation arises from force and diffusion interplay, and demonstrates flux reversal in practical optical tweezer setups.
Findings
Brownian vortexes exhibit steady-state circulation in non-conservative force fields.
Circulation can undergo flux reversal under certain conditions.
Experimental and theoretical evidence supports the flux reversal phenomenon.
Abstract
A particle diffusing around a point of stable mechanical equilibrium in a static but non-conservative force field enters into a steady state characterized by circulation in the probability flux. Circulation in such a Brownian vortex is not simply a deterministic response to the solenoidal component of the force, but rather reflects an interplay between force-driven probability currents and diffusion. As an example of this previously unrecognized class of stochastic heat engines, we consider a colloidal sphere stably trapped in a conventional optical tweezer. Rather than coming into thermodynamic equilibrium with the surrounding heat bath, the particle's Brownian fluctuations are biased into a toroidal roll. We demonstrate both theoretically and experimentally that the circulation in this practical realization of the Brownian vortex can undergo flux reversal.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Thermodynamics and Statistical Mechanics · Micro and Nano Robotics · Field-Flow Fractionation Techniques
