Browsing by Author "Wei, Chenxi"
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- Charge distribution guided by grain crystallographic orientations in polycrystalline battery materialsXu, Zhengrui; Jiang, Zhisen; Kuai, Chunguang; Xu, Rong; Qin, Changdong; Zhang, Yan; Rahman, Muhammad Mominur; Wei, Chenxi; Nordlund, Dennis; Sun, Cheng-Jun; Xiao, Xianghui; Du, Xi-Wen; Zhao, Kejie; Yan, Pengfei; Liu, Yijin; Lin, Feng (2020-01-08)Architecting grain crystallographic orientation can modulate charge distribution and chemomechanical properties for enhancing the performance of polycrystalline battery materials. However, probing the interplay between charge distribution, grain crystallographic orientation, and performance remains a daunting challenge. Herein, we elucidate the spatially resolved charge distribution in lithium layered oxides with different grain crystallographic arrangements and establish a model to quantify their charge distributions. While the holistic "surface-to-bulk" charge distribution prevails in polycrystalline particles, the crystallographic orientation-guided redox reaction governs the charge distribution in the local charged nanodomains. Compared to the randomly oriented grains, the radially aligned grains exhibit a lower cell polarization and higher capacity retention upon battery cycling. The radially aligned grains create less tortuous lithium ion pathways, thus improving the charge homogeneity as statistically quantified from over 20 million nanodomains in polycrystalline particles. This study provides an improved understanding of the charge distribution and chemomechanical properties of polycrystalline battery materials.
- Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodesJiang, Zhisen; Li, Jizhou; Yang, Yang; Mu, Linqin; Wei, Chenxi; Yu, Xiqian; Pianetta, Piero; Zhao, Kejie; Cloetens, Peter; Lin, Feng; Liu, Yijin (Springer Nature, 2020)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.