Chemokinesis-driven accumulation of active colloids in low-mobility regions of fuel gradients

Machine learning-based inverse design for electrochemically controlled microscopic gradients of O2 and H2O2

In microbiology, extracellular oxygen (O₂) and reactive oxygen species (ROS) are spatiotemporally heterogenous, ubiquitously, at macroscopic level. Such spatiotemporal heterogeneities are critical to microorganisms, yet a well-defined method of studying such heterogenous microenvironments is lacking. This work develops a machine learning–based inverse design strategy that builds an electrochemical platform for achieving spatiotemporal control of O₂ and ROS microenvironments relevant to microbiology. The inverse design strategy not only demonstrates the power of machine learning to design concentration profiles in electrochemistry but also accelerates the development of custom microenvironments for specific microbial systems and allows researchers to better study how microenvironments affect microorganisms in myriads of environmental, biomedical, and sustainability-related applications.

A fundamental understanding of extracellular microenvironments of O₂ and reactive oxygen species (ROS) such as H₂O₂, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O₂ and H₂O₂ at microscopic scale with high spatiotemporal precision. However, there is a paucity of high-throughput strategies of microenvironment design, and it remains challenging to achieve O₂ and H₂O₂ heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O₂ and H₂O₂ profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O₂ and H₂O₂ profiles with spatial resolution of ∼10¹ μm and temporal resolution of ∼10° s. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O₂ and H₂O₂ microenvironments while being two orders of magnitude faster than experimental explorations. Interfacing ML-based inverse design with electrochemically controlled concentration heterogeneity creates a viable fast-response platform toward better understanding the extracellular space with desirable spatiotemporal control.

Active colloidal molecules in activity gradients

We consider a rigid assembly of two active Brownian particles, forming an active colloidal dimer, in a gradient of activity. We show analytically that depending on the relative orientation of the two particles the active dimer accumulates in regions of either high or low activity, corresponding to, respectively, chemotaxis and antichemotaxis. Certain active dimers show both chemotactic and antichemotactic behavior, depending on the strength of the activity. Our coarse-grained Fokker-Planck approach yields an effective potential, which we use to construct a nonequilibrium phase diagram that classifies the dimers according to their tactic behavior. Moreover, we show that for certain dimers a higher persistence of the motion is achieved similar to the effect of a steering wheel in macroscopic devices. This work could be useful for designing autonomous active colloidal structures which adjust their motion depending on the local activity gradients.

Excess Entropy Scaling in Active-Matter Systems

Active-matter systems feature discrete particles that can convert stored or ambient free energy into motion. To realize the engineering potential of active matter, there is a strong need for predictive and theoretically grounded techniques for describing transport in these systems. In this work, we perform molecular-dynamics (MD) simulations of a model active-matter system, in which we vary the total fraction of active particles (0.01 ≤ ϕ ≤ 0.5) as well as the degree of activity of the active particles. These simulations reveal a fascinating array of transport phenomena, including activity-enhanced diffusion coefficients. By adapting an existing result for binary (inactive) fluids, we demonstrate the existence of an excess entropy scaling relation in an active system. This relationship is well supported by our MD results and establishes a new connection between transport (dynamics) and structure (statics) in active matter, a promising step for predictive and generalizable models of other transport phenomena in such systems.