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Energy & Environmental Science
Article . 2025 . Peer-reviewed
License: CC BY NC
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Machine learning-assisted benign transformation of three zinc states in zinc ion batteries

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2025 English Publisher:Royal Society of Chemistry (RSC)Journal:Energy & Environmental Science, volume 18, pages 4,872-4,882 (issn: 1754-5692, eissn: 1754-5706, Copyright policy )
Authors: Jianbo Dong; Guolang Zhou; Wenhao Ding; Jiayi Ji; Qing Wang; Tianshi Wang; Lili Zhang; +3 Authors

doi: 10.1039/d5ee00650c

Machine learning-assisted benign transformation of three zinc states in zinc ion batteries

Abstract

A machine-learning-designed cerium-iron MOF layer enhances Zn anode stability, achieving over 4300 hours at 1 mA cm−2 and 99.8% coulombic efficiency over 1400 cycles at 2 mA cm−2, providing a cost-effective protective strategy.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
hybrid
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