Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes

Zhisen Jiang,Peter Cloetens,Piero Pianetta,Linqin Mu,Kejie Zhao,Xiqian Yu,Jizhou Li,Yijin Liu,Feng Lin,Chenxi Wei,Yang Yang

doi:10.1038/s41467-020-16233-5

Abstract

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.

Highlights

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery
An ideal composite electrode would offer a mechanically stable framework that allows for optimal electron and lithium-ion-conducting pathways, which entails the delicate control of the electrode microstructure through systematic electrode-scale studies
We demonstrate the visualization of active particles, carbon/binder domain (CBD), and pore structures in a Ni-rich LiNi0.8Mn0.1Co0.1O2 (NMC) composite cathode at the charged state

Summary

Introduction

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. We point out here that X-ray diffraction tomography[29] and pair distribution function tomography, in which the spatially resolved Xray diffraction signal is recorded as the sample is raster scanned and rotated, have been utilized to look into the structural heterogeneity in battery materials under operating conditions[30,31,32] While these techniques are sensitive to the atomic arrangements of the material’s lattice structure, the effective spatial resolution is often determined by the X-ray focal spot used to raster scan the sample and is typically only at the micron-level due to practical experimental constraints, such as inferior data collection speed. Such a phenomenon is attributed to the sub-particle level structural and chemical defects, which could lead to thermodynamically stable charge heterogeneity and play a significant role in the active particle degradation

Methods

Results

Conclusion