Search NASA⌕ Search

Engineering topics

Ge, Mingyuan (ORCID:0000000156827443)

Publications and source records attributed to Ge, Mingyuan (ORCID:0000000156827443).

Parasitic structure defect blights sustainability of cobalt-free single crystalline cathodes

Abstract Recent efforts to reduce battery costs and enhance sustainability have focused on eliminating Cobalt (Co) from cathode materials. While Co-free designs have shown notable success in polycrystalline cathodes, their impact on single crystalline (SC) cathodes remains less understood due to the significantly extended lithium diffusion pathways and the higher-temperature synthesis involved. Here, we reveal that removing Co from SC cathodes is structurally and electrochemically unfavorable, exhibiting unusual voltage fade behavior. Using multiscale diffraction and imaging techniques, we identify lithium-rich nanodomains (LRNDs) as a heterogeneous phase within the layered structure of Co-free SC cathodes. These LRNDs act as critical tipping points, inducing significant chemo-mechanical lattice strain and irreversible structural degradation, which exacerbates the voltage and capacity loss in electrochemical performance. Our findings highlight the considerable challenges of developing Co-free SC cathodes compared to polycrystalline ones and emphasize the need for new strategies to balance the interplay between cost, sustainability, and performance.

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↗