Smart active particles learn and transcend bacterial foraging strategies.

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.

Structure formation of PNIPAM microgels in foams and foam films.

Responsive aqueous foams are very interesting from a fundamental point of view and for various applications like foam flooding or foam flotation. In this study thermoresponsive microgels (MGs) made from poly(N-isopropyl-acrylamide) (PNIPAM) with varying cross-linker content, are used as foam stabilisers. The foams obtained are thermoresponsive and can be destabilised by increasing the temperature. The structuring of MGs inside the foam films is investigated with small-angle neutron scattering and in a thin film pressure balance. The foam films are inhomogeneous and form a network-like structure, in which thin and MG depleted zones with a thickness of ca. 30 nm are interspersed in a continuous network of thick MG containing areas with a thickness of several 100 nm. The thickness of this continuous network is related to the elastic modulus of the individual MGs, which was determined by atomic force microscopy indentation experiments. Both, the elastic moduli and foam film thicknesses, indicate a correlation to the network elasticity of the MGs predicted by the affine network model.