Leveraging artificial intelligence in bioelectrochemical systems

Potential and challenge in accelerating high-value conversion of CO2 in microbial electrosynthesis system via data-driven approach

Microbial electrosynthesis for CO2 utilization (MESCU) producing valuable chemicals with high energy density has garnered attention due to its long-term stability and high coulombic efficiency. The data-driven approaches offer a promising avenue by leveraging existing data to uncover the underlying patterns. This comprehensive review firstly uncovered the potentials of utilizing data-driven approaches to enhance high-value conversion of CO2 via MESCU. Firstly, critical challenges of MESCU advancing have been identified, including reactor configuration, cathode design, and microbial analysis. Subsequently, the potential of data-driven approaches to tackle the corresponding challenges, encompassing the identification of pivotal parameters governing reactor setup and cathode design, alongside the decipheration of omics data derived from microbial communities, have been discussed. Correspondingly, the future direction of data-driven approaches in assisting the application of MESCU has been addressed. This review offers guidance and theoretical support for future data-driven applications to accelerate MESCU research and potential industrialization.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.