LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance.However,the rapid capacity fading of NC...LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance.However,the rapid capacity fading of NCM severely hinders its development and applications.Here,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images.The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process.The space between the transition metal columns shrinks obviously,inducing dramatic capacity decay.This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation.It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.展开更多
基金This project was supported by the fund from National Natural Science Foundation of China(NSFC No.52077096 and 52107224)China Postdoctoral Science Foundation under Grant No.2019M662612+1 种基金the Interdisciplinary Program of Wuhan National High Magnetic Field Center from Huazhong University of Science and Technology(Grant No.WHMFC202138)State Grid Zhejiang Electric Power Co.,Ltd.technology project 5211UZ2000K1.
文摘LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523)has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance.However,the rapid capacity fading of NCM severely hinders its development and applications.Here,the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy(STEM).Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images.The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process.The space between the transition metal columns shrinks obviously,inducing dramatic capacity decay.This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation.It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.
