The widespread implementation of next-generation Li metal anodes is limited, in part, due to the formation of dendritic and/or mossy electrodeposits during cycling. These morphologies can lead to battery failure due to the formation of short circuits and significant volumetric expansion at the anode. One strategy to control the electrodeposition of Li metal is to use lithiophilic materials at the anode. Here, we evaluate the impact of Ag and Au on the early stages of Li metal electrodeposition and cycling. The alloying substrates decrease the voltage for Li reduction and improve Li wetting/adhesion. We probe volumetric expansion directly through dilatometry measurements and find that the degree of volumetric expansion is less when lithium is cycled on an alloying substrate compared to a non-alloying substrate (Cu). Dilatometry experiments reveal that Au has the least amount of volumetric expansion and coin cell cycling experiments indicate that Ag yields more stable cycling compared to Au or Cu. The evaluation of in situ cross-sectional images of cycled coin cells shows that Ag has the lowest volumetric expansion in a coin cell format.
Cements are a critical component in well construction, as they act to prevent well fluid and gas escape, prevent corrosion of the casing, and strengthen the wellbore to prevent deformation. Under the high temperature/pressure conditions common in geothermal systems, the injection of cold water for energy production is expected to induce cyclic damage to the borehole cement through the rapid temperature fluctuations. These “thermal shocks” are expected to cause casing shrinkage, annulus formation, and cement tensile stresses. To understand the effect of cold water injection on the wellbore environment, a set of rock-cement-steel samples were created to simulate the structure of a geothermal well. Lightweight thermally-insulating cement blends were tested under thermal shock conditions in this study. In each test, the samples were pressurized to an effective pressure of ∼3.5 MPa and placed at high temperatures. Thermal shocks were performed by injecting cold water (∼10-15 °C) through the samples at a constant rate while keeping the samples at high temperatures until the sample temperature stopped decreasing and deformation ceased. Eight thermal shock tests were conducted with each sample – two at 100 °C and six at 200 °C. Post-tests analysis was then conducted by cutting open each sample to examine the damage in each component of the simulated wellbore. Experimental results suggest that all samples experienced similar degrees of axial and lateral contraction during cold water injection, but for the most part this contraction is recoverable when injection halts. Post-test analysis revealed that fly ash cenosphere pre-treatment had the best effect on improving thermal shock resistance in the cement blends. Thermomechanical modeling of likely stress paths experienced by the cements during heating/cooling cycles shows that elasto-plastic cement constitutive behavior results in most plastic strain occurring during the initial heating steps, with mostly elastic strain occurring during the thermal shock cycles. This agrees with experimental evidence, suggesting that cement damage from shocking occurs via other mechanisms such as chemical alteration, corrosion, and fatigue.
This manuscript we present a new method for solving PDEs. The method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent convection-diffusion-reaction (CDR) equation. We reformulate the assembly process of the spectral element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the spectral element operators.
To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. In this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.
This manual provides step-by-step instructions for installing, configuring, and using BLADE, an automated framework for analyzing and classifying bolide light curves from NASA CNEOS datasets. BLADE enables efficient, reproducible analysis of atmospheric entry phenomena, supporting planetary defense and atmospheric science research through standardized signal processing and event classification.
This report addresses the need to predict infrasound signal arrival times and back azimuths at monitoring stations, enabling more focused and efficient searches within recorded waveform data.
The authors were tasked with writing a concise memo (now a short report) that adequately addresses the current ``state-of-the-art'' in the field of reactive flow modeling and burn models for mid-year 2025, along with identifying some of the modeling gaps. It is assumed that the reader has experience with burn models and running hydrocode simulations, and thus every effort is made to ensure brevity, clarity, and utility, so that this document will serve as a quick and useful reference. Note that
The structural frame used for conducting large structural tests was replaced by the ACME (Applied Combined Mechanical Environments) Lab in Building 860 of Sandia National Laboratories in Albuquerque, NM. This new design required simulation and analysis to verify maximum loads and corresponding factors of safety. Two major assemblies which are part of the ACME Lab, the Modular Wall and A-Frame, were designed to operate with a multitude of components in various configurations and with varying lo
Sandia National Laboratories (SNL) collaborated with the US Nuclear Regulatory Commission's (NRC) Office of Nuclear Security and Incident Response (NSIR) and Office of Nuclear Regulatory Research (RES) to investigate the reasonable variability in the estimation of dose exceedance distances. SNL performed calculations using the MACCS code to elucidate the sensitivity of dose exceedance distances to variations in user input parameters.
Understanding the safety profile of aged Li-ion batteries is essential for developing effective battery management and hazard mitigation strategies. However, most safety assessments have focused on fresh batteries, with just a few calorimetry studies on aged batteries with metal oxide positive electrodes. This study provides a broad assessment of commercial 18650-type Li-ion batteries with NCA, NMC, and LFP positive electrodes, both uncycled and aged under conditions that promoted solid electrolyte interphase (SEI) growth as the dominant degradation mechanism. The cells underwent mechanical (nail penetration, crush), electrical (overcharge, overdischarge), and thermal (accelerating rate calorimetry) abuse tests. Safety was rated on general characteristics such as mass loss, maximum temperature, and EUCAR (European Council for Automotive R&D) hazard level, as well as characteristics specific to individual abuse tests. Generally, aged cells with SEI growth exhibited similar or improved safety compared to uncycled cells, contrasting with our previous findings on NCA cells with Li plating as the dominant aging mechanism (Part I of this series). Yet, some tests and characteristics indicated reduced aged cell safety, such as earlier triggering of mechanical failure. These results emphasize the need to examine aged battery safety across diverse empirical techniques, degradation modes, and chemistries.
Identification of amorphous phases in nanoparticles by atomic-resolution transmission electron microscopy (TEM) requires analyses such as tilt-angle-dependent TEM imaging and single-nanoparticle electron diffraction, rather than relying on a single TEM image.
