Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles.
basic_science · Level V
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- Record sourced from PubMed, PMID 36419444.
- Also identified by DOI 10.1016/j.patter.2022.100610 and PMC identifier 9676538.
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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>I</i> <sub><i>c</i></sub> ). A 96-channel potentiostat represents <10% SD for maximum <i>I</i> <sub><i>c</i></sub> . 576 time-<i>I</i> <sub><i>c</i></sub> profiles were obtained in 120 different electrolyte and potentiostatic conditions with two model electrogenic bacteria, <i>Shewanella</i> and <i>Geobacter</i>. Acquisition functions showed the highest performance per concentration for riboflavin over a wide potential range in <i>Shewanella</i>. 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.