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Consistent performance of Li metal electrode (LME) relies on well-distributed current across the electrode surface. Lithium plating and dendrite growth are challenging issues for LME performance in high-energy rechargeable Li battery (RLB) development. The morphology of Li plating is affected by local current density variations on LME. A three-dimensional RLB cell model is used to study current density variations induced by cathode particle size. Smaller cathode particles show lower variances in the current density distribution throughout the separator. The results suggest particle size could affect uniformity of Li plating, propensity for dendrite formation and ultimately cycle life of RLB.

A predictive model of melt rate in waste glass vitrification operations is needed to inform melter operations during normal and off‐normal operations. This paper describes the development of a model of the cold cap (the reacting melter feed floating on molten glass in a glass melter) that couples heat transfer with the feed‐to‐glass conversion kinetics. The model was applied to four melter feeds designed for high‐level and low‐activity nuclear waste feeds using the material properties, either measured or estimated, to obtain temperature and conversion distribution within the cold cap. The cold cap model, when coupled with a computational fluid dynamics model of a Joule‐heated glass melter, allows the prediction of the glass production rate and power consumption. The results show reasonable agreement with the melting rates measured during pilot‐scale melter tests.

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Wh kg −1 , and a life of 1000 cycles. [3] To meet these targets, large improvements in specific energy are critically needed. One promising route to increase the specific energy of batteries is to switch to Li metal anodes. Batteries with Li metal serving as the anode can deliver exceptionally high energy densities, [4] because of their advantages in having the lowest redox potential (−3.04 V vs standard hydrogen electrode) and a high theoretical specific capacity (3860 mAh g −1). Studies suggest that the energy density of batteries can be doubled or tripled relative to present technologies using Li metal anodes. [2] However, the practical applications of Li metal anodes are severely hindered by their rapid failure. This failure is primarily driven by parasitic reactions between Li metal and electrolyte. The common signatures of failure are the formation of dead/isolated Li, which is electrically disconnected from the bulk Li metal during uneven stripping on the anode surface, [5,6] the dendritic or mossy Li, which is induced by inhomogeneous distributions of space charge [7] on the anode surface which arise from uneven anode surfaces or cracks in the solid electrolyte interphase (SEI). [8] Rechargeable Li metal batteries (LMBs) cannot be deployed in automotive applications unless lifetime limitations are overcome. Numerous strategies have been explored to successfully extend the lifetime of LMBs. These include developing novel electrolytes that produce more stable SEI layers, creating artificial SEI layers on the Li metal surface, and applying external pressures. [9] While these and other methods provide pathways for improving the lifetime of batteries, none of them have succeeded in meeting industrial performance targets. In order to accelerate the development of viable LMB technologies, it is critically important to develop diagnostic techniques that can rapidly and easily identify the primary cause of battery failure and that can make useful predictions about the lifetime of a cell based on data collected early in its lifetime (instead of waiting long times for cells to reach their actual failure point before getting feedback). There are three commonly seen battery failure mechanisms for LMBs that reduce the capacity of a cell over its lifetime. [10] Lithium (Li) metal serving as an anode has the potential to double or triple stored energies in rechargeable Li batteries. However, they typically have short cycling lifetimes due to parasitic reactions between the Li metal and electrolyte. It is critically required to develop early fault-detection methods for different failure mechanisms and quick lifetime-prediction methods to ensure rapid development. Prior efforts to determine the dominant failure mechanisms have typically required destructive cell disassembly. In this study, non-destructive diagnostic method based on rest voltages and coulombic efficiency are used to easily distinguish the different failure mechanismsfrom loss of Li inventory, electrolyte depletion, and increased cell impedancewhich are deep...

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