Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling-Reference-Cited by-同舟云学术

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Improved Rate Capability for Dry Thick Electrodes through Finite Elements Method and Machine Learning Coupling

Published:2024-03-12 Issue:4 Volume:9 Page:1480-1486
ISSN:2380-8195
Container-title:ACS Energy Letters
language:en
Short-container-title:ACS Energy Lett.

Author:

Chouchane Mehdi¹,Yao Weiliang²,Cronk Ashley²^ORCID,Zhang Minghao³,Meng Ying Shirley¹³^ORCID

Affiliation:

1. Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States

2. Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States

3. Department of NanoEngineering, University of California San Diego, La Jolla, California 92093, United States

Funder

FACCTS

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsenergylett.4c00203

Reference39 articles.

1. A comprehensive understanding of electrode thickness effects on the electrochemical performances of Li-ion battery cathodes

2. Multiscale Understanding and Architecture Design of High Energy/Power Lithium‐Ion Battery Electrodes

3. Drying of Granular Ceramic Films: I, Effect of Processing Variables on Cracking Behavior

4. Cracking in Drying Colloidal Films

5. Analysis of Rate-Limiting Factors in Thick Electrodes for Electric Vehicle Applications

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