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For lithium–sulfur battery commercialization, research at a pouch cell level is essential, as some problems that can be ignored or deemed minimal at a smaller level can have a greater effect on the performance of the larger pouch cell. Herein, the failure mechanisms of Li–S pouch cells are deeply investigated via in operando pressure analysis. It is found that highly porous structures of cathodes/separators and slow electrolyte diffusion through cathodes/separators can both lead to poor initial wetting. Additionally, the Li‐metal anode dominates the thickness variation of the whole pouch cell, which is verified by in situ measured pressure variation. Consequently, a real‐time approach that combines normalized pressure with differential pressure analysis is proposed and validated to diagnose the morphology evolution of the Li‐metal anode. Moreover, applied pressure and porosity/tortuosity ratio of the cathode are both identified as independent factors that influence anode performance. In addition to stabilizing anodes, high pressure is proven to improve the cathode connectivity and avoid cathode cracking over cycling, which improves the possibility of developing cathodes with high sulfur mass loading. This work provides insights into Li–S pouch cell design (e.g., cathode and separator) and highlights pathways to improve cell capacity and cycling performance with applied and monitored pressure.

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Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. A mechanical understanding of fission gas bubble evolution behavior is a prerequisite for fuel development and qualification. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented detail and an extensive amount of morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multi-task deep learning network that integrates both U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to other similar material micro-structure segmentation tasks.