Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles

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.

Keywords

Identifiers

DOI

10.1016/j.patter.2022.100610

PubMed

36419444

Journal

Patterns

Year

2022