Enhancing Predictions of Acetate and Ethanol Production from Microbial Electrosynthesis Using Optimized Machine Learning Models-Reference-Cited by-同舟云学术

Enhancing Predictions of Acetate and Ethanol Production from Microbial Electrosynthesis Using Optimized Machine Learning Models

Published:2024-02-16 Issue:10 Volume:12 Page:4264-4275
ISSN:2168-0485
Container-title:ACS Sustainable Chemistry & Engineering
language:en
Short-container-title:ACS Sustainable Chem. Eng.

Author:

Li Chunyan¹²,Li Huyang¹²,Li Pengsong¹²^ORCID,Dang Yan¹²,Sun Dezhi¹²,Guo Dongchao³

Affiliation:

1. Beijing Key Lab for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China

2. Engineering Research Center for Water Pollution Source Control & Eco-remediation, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, China

3. School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Municipal Education Commission

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssuschemeng.3c08356

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4. Parameters influencing the development of highly conductive and efficient biofilm during microbial electrosynthesis: the importance of applied potential and inorganic carbon source

5. Microbial Electrosynthesis: Feeding Microbes Electricity To Convert Carbon Dioxide and Water to Multicarbon Extracellular Organic Compounds