| About this Abstract |
| Meeting |
2019 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization of Materials through High Resolution Imaging
|
| Presentation Title |
Identification and Visualization of Chemical Outliers through Scientific Data Mining in Nanoscale Spectro-microscopic Study of NMC Electrode |
| Author(s) |
Enyuan Hu, Yijin Liu, Xiao-Qing Yang |
| On-Site Speaker (Planned) |
Enyuan Hu |
| Abstract Scope |
The battery electrode is structurally and chemically heterogeneous over a wide range of length scales. The heterogeneity, which could be intentionally engineered or could be formed due to anticipated/unanticipated evolution of the material upon battery operation, plays a key role in affecting the performance at device level. In this work, the chemical sensitive transmission x-ray microscopy was used to investigate NMC secondary particles that have gone through long term cycling over different voltage windows. While the overall degree of chemical heterogeneity is found to be similar in all the samples, through a machine learning approach developed herein, we identified and visualized the chemical outliers in the particles that have gone through relatively more harsh cycling condition. The formation, evolution, and migration of these chemical outliers are believed to be responsible for the capacity and voltage fade of the NMC electrode when cycled to high electrochemical potential. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |