Maximizing power and velocity of an information engine

TL;DR

This paper presents an experimental design of an information engine using a colloidal particle and feedback control, achieving higher power and velocity than previous engines by optimizing physical parameters.

Contribution

It introduces a simple, optimized design of an information ratchet that maximizes power and velocity, surpassing previous experimental implementations.

Findings

Power and velocity are limited only by physical parameters.

Performance saturates with observation frequency, not measurement process.

Extracted power and velocity are at least an order of magnitude higher than prior engines.

Abstract

Information-driven engines that rectify thermal fluctuations are a modern realization of the Maxwell-demon thought experiment. We introduce a simple design based on a heavy colloidal particle, held by an optical trap and immersed in water. Using a carefully designed feedback loop, our experimental realization of an "information ratchet" takes advantage of favorable "up" fluctuations to lift a weight against gravity, storing potential energy without doing external work. By optimizing the ratchet design for performance via a simple theory, we find that the rate of work storage and velocity of directed motion is limited only by the physical parameters of the engine: the size of the particle, stiffness of the ratchet spring, friction produced by the motion, and temperature of the surrounding medium. Notably, because performance saturates with increasing frequency of observations, the measurement process is not a limiting factor. The extracted power and velocity are at least an order of magnitude higher than in previously reported engines.