التفاصيل البيبلوغرافية
العنوان: |
Machine Learning in Bioelectrocatalysis. |
المؤلفون: |
Huang, Jiamin, Gao, Yang, Chang, Yanhong, Peng, Jiajie, Yu, Yadong, Wang, Bin |
المصدر: |
Advanced Science; 1/15/2024, Vol. 11 Issue 2, p1-22, 22p |
مصطلحات موضوعية: |
MACHINE learning, MICROBIAL fuel cells, ENERGY shortages, BIODEGRADABLE materials, POLLUTION |
مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Advanced Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
قاعدة البيانات: |
Complementary Index |