Szilard engines and information-based work extraction for active systems

Abstract

The out of equilibrium nature of active systems can be exploited for the design of information-based engines. We design two types of an active Szilard engine that use a Maxwell daemon to extract work from an active bath composed of non-interacting Active Brownian Particles (ABPs). The two engines exploit either the quasi-static active pressure of ABPs or the long correlation time of their velocities. For both engines the Landauer principle can be violated so that larger work can be extracted compared to conventional Szilard engines operating in quasi thermal equilibrium. For both of our engines,we identify the optimal regimes at which the work extracted and the efficiency are maximized. Finally, we we discuss them in the context of synthetic and biological active systems.

References in corpus (9)

• Motility-Induced Phase Separation
• Physics of Microswimmers - Single Particle Motion and Collective Behavior
• Efficiency at maximum power: An analytically solvable model for stochastic heat engines
• Dissipation: The phase-space perspective
• When are active Brownian particles and run-and-tumble particles equivalent? Consequences for motility-induced phase separation
• Large work extraction and the Landauer limit in a continuous Maxwell demon
• Thermal ratchet effect in confining geometries
• Efficiency of isothermal active matter engines: Strong driving beats weak driving
• Working under confinement

Cited by in corpus (8)

• Information engine in a nonequilibrium bath
• Optimal power extraction from active particles with hidden states
• Unlocking the potential of information flow: Maximizing free-energy transduction in a model of an autonomous rotary molecular motor
• Information Arbitrage in Bipartite Heat Engines
• Active particles in moving traps: minimum work protocols and information efficiency of work extraction
• Stationary particle currents in sedimenting active matter wetting a wall
• Performance limits of information engines
• Achievable Information-Energy Exchange in a Brownian Information Engine through Potential Profiling