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

Area of Science:

• Bioelectrochemical systems
• Data science applications in microbiology
• Electrogenic bacteria research

Background:

• Optimizing microbial current production requires understanding complex interactions.
• Limited reliable databases exist for bioelectrochemical systems.
• Data science offers potential for optimizing multivariable phenomena.

Purpose of the Study:

• To develop a high-throughput platform for optimizing microbial current production.
• To apply 2D Bayesian estimation for electrode potential and additive concentration.
• To create a reliable database for bioelectrochemical systems.

Main Methods:

• Utilized a 96-channel potentiostat for high-throughput electrochemical measurements (<10% SD).
• Acquired 576 time-current profiles across 120 conditions with *Shewanella* and *Geobacter*.
• Employed 2D Bayesian estimation to optimize electrode potential and redox-active additive concentration.

Main Results:

• Identified riboflavin as a high-performance additive for *Shewanella* across a wide potential range.
• Validated the mechanism using electrochemical analysis with mutant strains lacking key redox enzymes.
• Demonstrated the platform's capability to generate reliable data for bioelectrochemical optimization.

Conclusions:

• The integration of data science and high-throughput electrochemistry significantly advances bioelectrochemical technologies.
• The developed platform enables efficient optimization of microbial current production.
• This approach is expected to accelerate breakthroughs in the field.