Machine learning-based inverse design for electrochemically controlled microscopic gradients of O <subscript>2</subscript> and H <subscript>2</subscript> O <subscript>2</subscript> .
In: Proceedings of the National Academy of Sciences of the United States of America, Jg. 119 (2022-08-09), Heft 32, S. e2206321119
Online
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Zugriff:
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.
Titel: |
Machine learning-based inverse design for electrochemically controlled microscopic gradients of O <subscript>2</subscript> and H <subscript>2</subscript> O <subscript>2</subscript> .
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Autor/in / Beteiligte Person: | Chen, Y ; Wang, J ; Hoar, BB ; Lu, S ; Liu, C |
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Zeitschrift: | Proceedings of the National Academy of Sciences of the United States of America, Jg. 119 (2022-08-09), Heft 32, S. e2206321119 |
Veröffentlichung: | Washington, DC : National Academy of Sciences, 2022 |
Medientyp: | academicJournal |
ISSN: | 1091-6490 (electronic) |
DOI: | 10.1073/pnas.2206321119 |
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