Affiliation:
1. Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095
2. California NanoSystems Institute, University of California, Los Angeles, CA 90095
Abstract
A fundamental understanding of extracellular microenvironments of O
2
and reactive oxygen species (ROS) such as H
2
O
2
, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O
2
and H
2
O
2
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 O
2
and H
2
O
2
heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O
2
and H
2
O
2
profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O
2
and H
2
O
2
profiles with spatial resolution of ∼10
1
μm and temporal resolution of ∼10° s. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O
2
and H
2
O
2
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.
Funder
HHS | NIH | National Institute of General Medical Sciences
Publisher
Proceedings of the National Academy of Sciences
Cited by
3 articles.
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