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Machine Learning in Bioelectrocatalysis
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DOI：10.1002/advs.202306583.png)
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Abstract
En 中文
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. Bioelectrocatalysis for clean energy production and organic waste/environmental pollutant treatment has received a great deal of attention from scientists and engineers around the world. The introduction of machine learning (ML) in this field has just started. At present, ML is mainly applied to electrochemical biosensors and microbial fuel cells, and there are already visible achievements.image
Keywords:
bioelectrocatalysis
biosensors
interdisciplinary research
machine learning
microbial fuel cells
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