Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles.
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ABSTRACT: Data science emerges as a promising approach for studying and optimizing complex multivariable phenomena, such as the interaction between microorganisms and electrodes. However, there have been limited reports on a bioelectrochemical system that can produce a reliable database until date. Herein, we developed a high-throughput platform with low deviation to apply two-dimensional (2D) Bayesian estimation for electrode potential and redox-active additive concentration to optimize microbial current production (I c ). A 96-channel potentiostat represents <10% SD for maximum I c . 576 time-I c profiles were obtained in 120 different electrolyte and potentiostatic conditions with two model electrogenic bacteria, Shewanella and Geobacter. Acquisition functions showed the highest performance per concentration for riboflavin over a wide potential range in Shewanella. The underlying mechanism was validated by electrochemical analysis with mutant strains lacking outer-membrane redox enzymes. We anticipate that the combination of data science and high-throughput electrochemistry will greatly accelerate a breakthrough for bioelectrochemical technologies.
SUBMITTER: Miran W
PROVIDER: S-EPMC9676538 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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