Machine Learning in Bioelectrocatalysis
Jiamin Huang, Yang Gao, Yanhong Chang, Jiajie Peng +2
AI summary
90% confidenceMES research in energy, microbial. At present, the global energy crisis and environmental pollution coexist, and th...
Generated by MESSAI extraction pipeline · review against source PDF
Representative microbial electrochemical system — matched on the paper’s system type only, not its reactor or geometry.
Reported parameters
No values extracted from this paper yet.
Open in lab for full controls, parameter editing, and template overlays.
Open in lab →What they did
- System
- MES
What worked
No outcome metrics extracted yet.
Abstract
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.
Keywords
Identifiers
- Journal
- Advanced Science
- Year
- 2023