CeNS Colloquium

Prof. Yael Roichman, Tel Aviv University

We realize experimentally a Maxwell's demon that converts information gained by measurements to work. Our setup is composed of a colloidal particle in a channel filled with a flowing fluid. A barrier made by light prevents the particle from being carried away by the flow. The colloidal particle then performs biased Brownian motion in the vicinity of the barrier. The particle's position is measured periodically. When the particle is found to be far enough from the barrier feedback is applied by moving the barrier upstream while maintaining a given minimal distance from the particle. At steady state the net effect of this measurement and feedback loop is to direct the particle upstream while applying very little direct work on it. This clean example of a Maxwell's demon is also naturally operated in a parameter regime where correlations between outcomes of consecutive measurements are important. We develop an approximate scheme for the calculation of the rate of information gain at steady state that accounts for the most prominent correlations. We employ both experimental results and computer simulations to characterize the thermodynamic properties of this information engine. Interestingly, we find a tradeoff between output power and efficiency. The efficiency is maximal at quasi-static operating conditions, whereas both the power output and rate of information gain are maximal for very frequent measurements.