Work extraction from active matter

This project explores how to efficiently harvest energy from "active particles" (such as E. coli) at the microscopic scale, where motion is subject to non-negligible noise resulting from random inter-particle collisions. Studying active particles in general provides a promising route to explore the broader field of non-equilibrium systems. The latter concerns anything that draws in energy from its surroundings to perform mechanical work, often in the form of self-propelled motion, and so ranges in scale from cellular tissues and bacterial colonies, to flocks of starlings and crowds of humans. The potential impacts of studying active particles are numerous and include the extraction of useful work, biomedical applications, the design of autonomous nanomachines, and crowd management.
The non-equilibrium nature of active particles also poses the most significant hurdle in understanding their properties, as the standard techniques provided by the toolbox of equilibrium statistical mechanics, such as detailed balance and free energy, no longer apply. Hence, to harness active particles, we must first come up with a way to understand and accurately predict their behaviour.
The objective of this project is to devise and solve suitable minimal models of active particle motion, in particular focussing on those where useful work can be extracted. This can be achieved by incorporating potentials that rectify the otherwise incoherent motion of individual active particles [Roberts and Zhen, 2023], or through continuous feedback protocols that apply optimal counterforces at all times through observation of (and despite) noise-dominated trajectories [Cocconi, Knight and Roberts, 2023]. These models are solved through a combination of analytical and numerical techniques, but a particularly novel aspect of the research methodology is the application of field-theoretic techniques, which provide a systematic scheme to approximate the "difficulties" arising from the microscopic dynamics, including interactions and self-propulsion [Roberts and Pruessner, 2022].
This research comes under the EPSRC research areas of Mathematical biology and Mathematical physics, and aligns with the EPSRC research themes of Mathematical sciences and Physical sciences.