期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：32
DOI：10.1073/pnas.2206321119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
In microbiology, extracellular oxygen (O
2) 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 O
2 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 O
2 and reactive oxygen species (ROS) such as H
2O
2, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O
2 and H
2O
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
2O
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
2O
2 profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O
2 and H
2O
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
2O
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.
