Machine learning-based inverse design for electrochemically controlled microscopic gradients of O2 and H2O2.
Proc Natl Acad Sci U S A
; 119(32): e2206321119, 2022 08 09.
Article
em En
| MEDLINE
| ID: mdl-35914135
ABSTRACT
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 â¼101 µm and temporal resolution of â¼10° 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.
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MEDLINE
Assunto principal:
Oxigênio
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Eletroquímica
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Microambiente Celular
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Aprendizado de Máquina
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Peróxido de Hidrogênio
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article