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تسجيل مستخدم جديدInvestigating Degradation Modes in Zn-AgO Aqueous Batteries with $textit{In-Situ}$ X-ray Micro Computed Tomography
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To meet growing energy demands, degradation mechanisms of energy storage devices must be better understood. As a non-destructive tool, X-ray Computed Tomography (CT) has been increasingly used by the battery community to perform $textit{in-situ}$ experiments that can investigate dynamic phenomena. However, few have used X-ray CT to study representative battery systems over long cycle lifetimes (>100 cycles). Here, we report the $textit{in-situ}$ CT study of Zn-Ag batteries and demonstrate the effects of current collector parasitic gassing over long-term storage and cycling. We design performance representative $textit{in-situ}$ CT cells that can achieve >250 cycles at a high areal capacity of $mathrm{12.5;mAh/cm^2}$. Combined with electrochemical experiments, the effects of current collector parasitic gassing are revealed with micro-scale CT (MicroCT). The volume expansion and evolution of ZnO and Zn depletion is quantified with cycling and elevated temperature testing. The experimental insights are then utilized to develop larger form-factor $mathrm{4;cm^2}$ cells with electrochemically compatible current collectors. With this, we demonstrate over 325 cycles at a high capacity of $mathrm{12.5;mAh/cm^2}$ for a $mathrm{4;cm^2}$ form-factor. This work demonstrates that $textit{in-situ}$ X-ray CT used in long cycle-lifetime studies can be applied to examine a multitude of other battery chemistries to improve their performances.
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X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled
by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.
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