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Reproducible long-term cycling data of AlO coated LiNiCoMnO cathodes for lithium-ion batteries.

作者信息

Negi Rajendra S, Elm Matthias T

机构信息

Center for Materials Research (LaMa) Justus-Liebig-University Giessen, Heinrich-Buff-Ring 16, D-35392, Giessen, Germany.

Institute of Physical Chemistry, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 17, D-35392, Giessen, Germany.

出版信息

Sci Data. 2022 Mar 30;9(1):127. doi: 10.1038/s41597-022-01217-5.

DOI:10.1038/s41597-022-01217-5
PMID:35354832
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8967842/
Abstract

LiNiCoMnO (NCM) based cathodes for Li-ion batteries (LIBs) are of great interest due to their higher energy density and lower costs compared to conventional LiCoO based cathodes. However, NCM based cathodes suffer from instabilities of the cathode-electrolyte interface resulting in faster capacity fading during long-term cycling. Different NCM compositions along with different coatings have been developed to protect the interface. However, a detailed understanding why and how coatings work is still missing. Up to now, no state-of-the-art NCM or coating material have been agreed upon yet, making it difficult to benchmark the performance of the coating material. Undefined standards complicate the use of experimentally produced data for model-based studies, which are a key element in assessing the beneficial effect of coatings. In this work, we therefore describe reproducible long-term cycling data of NCM based cathodes with and without an AlO based coating. The data set is provided to be used for parameter fitting and/or as training data to encourage the simulation of the performance of LIBs in model-based approaches.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df9/8967842/6337293ae189/41597_2022_1217_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df9/8967842/f9f5ecb639aa/41597_2022_1217_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df9/8967842/6337293ae189/41597_2022_1217_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df9/8967842/f9f5ecb639aa/41597_2022_1217_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df9/8967842/6337293ae189/41597_2022_1217_Fig4_HTML.jpg

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本文引用的文献

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Machine learning toward advanced energy storage devices and systems.面向先进储能设备和系统的机器学习。
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Enhancing the Electrochemical Performance of LiNiCoMnO Cathodes Using a Practical Solution-Based AlO Coating.使用基于溶液的实用 AlO 涂层提高 LiNiCoMnO 正极的电化学性能
ACS Appl Mater Interfaces. 2020 Jul 15;12(28):31392-31400. doi: 10.1021/acsami.0c06484. Epub 2020 Jul 2.
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Room temperature, liquid-phase AlO surface coating approach for Ni-rich layered oxide cathode material.
室温下富镍层状氧化物阴极材料的液相 AlO 表面涂层方法。
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Understanding the Role of Temperature and Cathode Composition on Interface and Bulk: Optimizing Aluminum Oxide Coatings for Li-Ion Cathodes.了解温度和阴极成分对界面和体相的作用:优化锂离子阴极用氧化铝涂层。
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