Accelerated Discovery of CO<sub>2</sub> Solid Sorbents Using Active Machine Learning: Review and Perspectives-Reference-Cited by-同舟云学术

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Accelerated Discovery of CO₂ Solid Sorbents Using Active Machine Learning: Review and Perspectives

Published:2024-08-31 Issue: Volume: Page:
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Xu Deyang¹,Yang Jing¹²,Xu Zhaoxiang¹,Gu Guo-yu-lin¹,Qiao Fen¹^ORCID,Wang Junfeng¹,Li Bin³^ORCID,Li Chaoen⁴,Liu Dongjing¹^ORCID

Affiliation:

1. School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China

2. Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong 100872, China

3. School of Engineering, Anhui Agricultural University, Hefei 230036, China

4. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315016, China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.4c02789

Reference99 articles.

1. A new-to-nature carboxylation module to improve natural and synthetic CO2 fixation

2. Environmental pollutants and the immune response

3. Design and diagnosis of high-performance CO2-to-CO electrolyzer cells

4. Global evaluation of carbon neutrality and peak carbon dioxide emissions: current challenges and future outlook

5. An overview of technologies for capturing, storing, and utilizing carbon dioxide: Technology readiness, large-scale demonstration, and cost

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