Li, Zeyuan | Search NASA

DOE OSTI2024

In Situ and Operando Observation of Zinc Moss Growth and Dissolution in Alkaline Electrolyte for Zinc–Air Batteries

As a promising battery technology, zinc–air batteries still face significant challenges, including the formation of a mossy structure on the zinc metal anode in alkaline electrolyte. Because a similar phenomenon also plagues lithium and sodium metal batteries, elucidating its mechanism has important implications for progress in energy storage. Herein, operando X-ray nanotomography was employed to visualize zinc moss growth and dissolution at the individual colony level. By tracking its microstructure evolution, zinc moss was found to display irreversible plating/stripping behavior. While zinc moss exhibits self-limiting growth and zinc deposition occurs mainly in its outer region, zinc dissolution is more uniformly distributed inside the moss colony upon stripping, leading to the formation of “dead” zinc and capacity loss. A direct correlation is established between the moss amount and zinc plating/stripping efficiency. Finally, results from this study offer new insights into mitigating the unstable zinc plating morphology and improving the cycle life of aqueous zinc–air batteries.

36 MATERIALS SCIENCE↗

DOE OSTI2024

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↗