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Liu, Sizhan

Publications and source records attributed to Liu, Sizhan.

Capacity Fade of Graphite/NMC811: Influence of Particle Morphology, Electrolyte, and Charge Voltage

LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) is an important Li-ion battery cathode material; however, there is a tradeoff between delivered capacity and capacity retention. As the charge potential increases the capacity rises but at the expense of capacity retention. The decrease in capacity retention has been ascribed to several factors including particle cracking, surface reconstruction, transition metal dissolution, and electrolyte reactivity. The present study compares 4.1 and 4.3 V charging limits in commercially relevant graphite/NMC811 pouch cells for single crystal (SC) and polycrystalline (PC) NMC811 with ethylene carbonate (EC)-containing or EC-free electrolytes. The electrochemistry is rationalized through analysis of electrochemical impedance spectroscopy, positive electrode X-ray photoelectron spectroscopy, soft X-ray absorption spectroscopy, X-ray diffraction, and negative electrode mapping by X-ray fluorescence. Graphite/SC-NMC811 cells show high-capacity retention at 4.1 V but exhibit degradation at 4.3 V charging potentials. The EC-free electrolyte cells led to higher capacity fade, especially when charged to 4.3 V. Cathode dissolution and deposition on the negative electrode from PC-NMC811 cells was higher than for samples from SC-NMC811 cells. This study reveals the impact of material type, charge voltage, and electrolyte composition on the reactions at the positive electrode, their influence on the negative electrode, and evolution with cycle number.

36 MATERIALS SCIENCE

Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning

Abstract Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNi x Mn y Co 1-x-y O 2 systems with high confidence.

36 MATERIALS SCIENCE