In Operando Acoustic Detection of Lithium Metal Plating in Commercial LiCoO2/Graphite Pouch Cells
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The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. Finally, the results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.
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We examine the stress field in Southern California with a focus on the region near the South Central Transverse Ranges (SCTR), using a refined stress inversion methodology. We independently invert declustered and aftershocks focal mechanisms from the years 1981 to 2017. Comparisons between the stress field orientations and the stress ratios from these inversions provide information on local sources of the stress field. Regionally, the SHmax tends to be toward NNE and the stress ratios vary from transtensional stress regime near the Eastern California Shear Zone (ECSZ), to strike-slip faulting near the SCTR, and toward transpression near the Western Transverse Ranges. Detailed analysis of stress parameters near the SCTR indicates deviations from the regional strike-slip faulting including the following: (1) The San Bernardino Mountain area shows S Hmax direction toward NNW and transpressional stress components likely associated with the motions of the San Andreas Fault and ECSZ; (2) the Cajon Pass and San Gorgonio Pass show transpressional stress regime near the bottom of the seismogenic zone likely associated with the elevated topography; and (3) in Crafton Hills, rotation of the principal stress plunges and SHmax direction and transtensional stress regime below ~10 km, along with lower estimated apparent friction coefficient, suggest a weak fault possibly associated with deep creep. The performed multiscale analysis resolves effects of regional and local loadings. The average stress parameters in the analyzed 37 years do not show significant temporal variations of stress field near the SCTR region.
Abstract The coupling of solid-state electrolytes with a Li-metal anode and state-of-the-art (SOA) cathode materials is a promising path to develop inherently safe batteries with high energy density (>1000 Wh L −1 ). However, integrating metallic Li with solid-electrolytes using scalable processes is not only challenging, but also adds extraneous volume since SOA cathodes are fully lithiated. Here we show the potential for “Li-free” battery manufacturing using the Li 7 La 3 Zr 2 O 12 (LLZO) electrolyte. We demonstrate that Li-metal anodes >20 μm can be electroplated onto a current collector in situ without LLZO degradation and we propose a model to relate electrochemical and nucleation behavior. A full cell consisting of in situ formed Li, LLZO, and NCA is demonstrated, which exhibits stable cycling over 50 cycles with high Coulombic efficiencies. These findings demonstrate the viability of “Li-free” configurations using LLZO which may guide the design and manufacturing of high energy density solid-state batteries.
Operando X-ray micro-computed tomography (µCT) provides an opportunity to observe the evolution of Li structures inside pouch cells. Segmentation is an essential step to quantitatively analyzing µCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets. Herein, we report a computational approach, batteryNET, to train an Iterative Residual U-Net-based network to detect Li structures. The resulting semantic segmentation shows singular Li-related component changes, addressing diverse morphologies in the dataset. In addition, visualizations of the dead Li are provided, including calculations about the volume and effective thickness of electrodes, deposited Li, and redeposited Li. We also report discoveries about the spatial relationships between these components. The approach focuses on a method for analyzing battery performance, which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.
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When a liquid drop impacts on a heated substrate, it can remain deposited, or violently boil in contact, or lift off with or without ever touching the surface. The latter is known as the Leidenfrost effect. The duration and area of the liquid–substrate contact are highly relevant for the heat transfer, as well as other effects such as corrosion. However, most experimental studies rely on side view imaging to determine contact times, and those are often mixed with the time until the drop lifts off from the substrate. In this work, we develop and validate a reliable method of contact time determination using high-speed X-ray imaging and total internal reflection imaging. We exemplarily compare contact and lift-off times on flat silicon and sapphire substrates. We show that drops can rebound even without formation of a complete vapor layer, with a wide range of lift-off times. On sapphire, we find a local minimum of lift-off times that is much shorter than expected from capillary rebound in the comparatively low-temperature regime of transition boiling/thermal atomization. We elucidate the underlying mechanism related to spontaneous rupture of the lamella and receding of the contact area.
Use of a microextraction device allows for ready sampling of solid substrates, such as cotton swipes, allowing transfer of sub-microgram quantities for multielement ICP-MS analysis.
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Thin walls of 304L stainless steel were fabricated by wire-arc additive manufacturing (WAAM) followed by post build thermomechanical processing to modify the as built microstructure and properties. The walls were made using a high-deposition-rate robotically controlled gas-metal arc welding system and were characterized using optical microscopy and tensile tested for mechanical properties. Post processing consisted of mechanical rolling the walls to 25% and 40% strains, followed by annealing at 1050°C to demonstrate a wide range of properties. Results show that WAAM can be used to build custom sized semi-finished parts or blanks that can later be thermomechanically processed to increase strength and ductility through recrystallization of the as built microstructure, and/or processed into desired shapes with improved mechanical properties.
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