材料科学
电化学
镍
钴
阴极
锂(药物)
电池(电)
储能
化学工程
电极
复合材料
冶金
电气工程
医学
功率(物理)
化学
物理
物理化学
量子力学
内分泌学
工程类
作者
Qi Shi,Feng Wu,Haoyu Wang,Yun Lu,Jinyang Dong,Jiayu Zhao,Yibiao Guan,Bin Zhang,Rui Tang,Yun Liu,Jinzhong Liu,Yuefeng Su,Lai Chen
标识
DOI:10.1016/j.ensm.2024.103264
摘要
Nickel-rich layered oxide stands as one of the most promising cathodes in demand for higher energy density of lithium-ion batteries (LIBs) in next generation. While increasing nickel content brings more capacity, it also makes this kind of cathode more vulnerable to the ambient, both the air outside the cell and the electrolyte inside, causing aggravated storage, structural and thermal instability. The cathode surface, as the first line of defense against the ambient attack, is the essential place to address these issues. Enlightened by the mechanism of "sustained-release capsule", here we decorate the polydimethylsiloxane (PDMS), a skin with exceptional moisture-resistance and chemical stability, onto the surface of LiNi0.9Mn0.1O2. This PDMS skin demonstrated the smart response to the surrounding environment that the hydrophobic surface guarantees the ambient air storage stability by cutting off residual alkali accumulation and the protective role of coating could be relayed as another functional mechanism once entering the battery. The detrimental HF species generated from water-induced electrolyte decomposition are effectively captured and eliminated to defend cathode corrosion and enhance interfacial stability during long-term cycling. Attributed to the seamless protection from the outside to the inside of the battery, this well-capsuled Ni-rich cathode exhibits negligible capacity fading compared with fresh state even after long ambient exposure, and realizes superior cycling and thermal stability. This work demonstrates new guidelines to design smart-responsive coatings for cathode materials to reduce their susceptibility to the surrounding environment, towards improving both ambient storage stability and cycling stability.
科研通智能强力驱动
Strongly Powered by AbleSci AI