X-ray imaging with AI-driven super-resolution deep learning for investigating battery electrode microstructural properties over cycling

Abstract

Rechargeable batteries are promising for transition to clean energy. This study investigates microstructural dynamics of LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811)-based cathodes over cycling using X-ray computed tomography (XCT). There is a long-standing imaging challenge of compromising between large field-of-view (FoV) to be representative of the electrodes and high resolution to observe fine details of individual particles. Here, we provide a framework that mitigates this trade-off by comparing two deep learning models-convolutional neural networks (CNNs) and generative adversarial networks (GANs)-for super-resolution enhancement of the XCT data to achieve both a large FoV (4 times larger) and sub-micron resolutions. We fabricated NMC811 cathodes containing different initial porosity (0.46-0.85) and tortuosity (1.24-2.74) by two different methods, directional ice templating (DIT) and dry processing to eliminate toxic organic solvent during fabrication. Microcracks inside individual NMC811 secondary particles and shifts in pixel intensity distributions were observed after 100 (dis)charge cycles. The DIT cathode exhibited a larger irreversible volume expansion due to the more favorable ion diffusion kinetics and higher active material utilization. Interestingly, the higher pore volume and carbon binder domain (CBD) surrounding the NMC811 particles effectively accommodated the volume expansion, and the DIT cathode exhibited a higher capacity retention over cycling than the dry coated cathode that exhibited initial lower porosity and higher tortuosity. A linear regression model was used to correlate the various microstructural properties such as porosity and tortuosity in the pristine state, and expansion after cycling to develop a framework of predicting optimal initial microstructure and electrochemical performance over cycling.