Active matter information machines

The relation between information and thermodynamics remains a fundamental thought-provoking issue since the days of Maxwell.

The most effective platforms used to explore this relation are information machines: processes that convert measured information about a system to extractable work.

A crucial question that goes beyond the currently explored regime in the field is how information machines perform out of equilibrium, namely when not in equilibrium with a thermal bath. This is the general scenario of relevance to biological systems and stands at the focus of this proposal.

Our recent result, showing how temporal correlations affect the efficiency of information engines (PRL 2018), demonstrates our ability to manipulate, track, and analyze particle motion in order to address such outstanding questions experimentally.

We propose to realize and study three new information engines with increasing complexity: (1) a microscopic engine that converts information to osmotic pressure, (2) a macroscopic engine that converts information to pressure, and (3) a mobile engine converting local information to directed motion.

The proposed engines are unique in that they harvest work from active systems, not necessarily coupled to a heat bath, and include many particles.

By measuring work and information directly, we will be able to test the validity of the generalized second law of thermodynamics and the Jarzynski fluctuation theorem in active matter systems with feedback and control loops.

Providing essential, precise and detailed experimental observations in a field in which they are lacking will pave the way toward the extension of stochastic thermodynamics to active systems. Unraveling the mechanisms that govern conversion of information to useful work will benefit far-reaching applications.

These include macroscopic and microscopic biomimetic robots and machines made of an ensemble of simple agents, analogous to natural phenomena such as cargo transport in ant colonies