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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Machine-learning molecular dynamics simulations pave the way to completely treat the anharmonicity of phonons. Low-energy anharmonic modes in transition-metal dichalcogenides drive the thermal and transport properties.
This review examines the roles of large language models (LLMs) and autonomous agents in chemistry, exploring advancements in molecule design, property prediction, and synthesis automation.
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Using ai-GCMC simulations,operandoWAXS, and kinetics analysis, we found that the high-rate performance of Sb as an alloy anode in Na-ion batteries is due to the presence of an amorphous intermediate phase formed during sodiation and desodiation.
Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.
Deploying advanced building control strategies in small- and medium-sized commercial buildings (SMBs) is critical but constrained by two major challenges: (1) the lack of sensor and control infrastructure (SCI) and (2) the significant engineering effort required for implementation and configuration. While these barriers limit large-scale adoption in SMBs, they remain underexplored in the literature, which has largely emphasized feasibility over scalability.
invited perspective article: The rapid demand for critical minerals (CMs) and hydrogen (H2) necessitates innovative solutions beyond conventional mining. This study explores enhanced mineral recovery (EMR) and geological hydrogen production (GeoH2) by leveraging the Earth’s subsurface as a reactive platform. Using ultramafic rocks and engineered fluids, the approach simultaneously mobilizes critical resources and produces H2 through natural processes like serpentinization. This paradigm offers a transformative pathway to secure essential materials and diversify energy resources, setting a foundation for the Terrestrial Mine of the Future
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Plastic waste accumulation poses significant environmental challenges due to a lack of economical solutions for the molecular deconstruction of diverse synthetic polymers. Biological‐based degradation offers promise but is hindered by the crystallinity, hydrophobicity, and additive complexity of plastics, which restrict biocatalyst access and activity. To address these problems, we propose a multi‐scale framework that combines detailed materials characterization, optimization of plastic‐biomolecular interfacial interactions, and enhancement of biocatalytic kinetics to develop effective plastic‐deconstructing enzymes. This approach leverages principles from reaction kinetics, transport and interfacial phenomena, and enzyme engineering to systematically address barriers across diverse plastic types. Our framework aims to accelerate the discovery and optimization of biocatalysts capable of scalable, selective, and efficient deconstruction of plastic waste. These advances hold potential to enable sustainable biological recycling and upcycling pathways, contributing to global efforts in mitigating plastic pollution and promoting circular material economies.
Abstract Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a multimodal dataset, and a suite of novel computer vision algorithms designed to automate the detection and localization of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4 155 instances of galaxies in 2 800 images with both radio and infrared channels. Each instance provides information about the extended radio galaxy class, its corresponding bounding box encompassing all components, the pixel-level segmentation mask, and the keypoint position of its corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. We benchmark several object detection algorithms on the dataset and propose a novel multimodal approach to simultaneously detect radio galaxies and the positions of infrared hosts.
Tungsten (W) and tungsten alloys are being considered as leading candidates for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for the design of magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. Despite these relevant features, there is a lack of understanding of how the structural and mechanical properties of W-based alloys are affected by the temperature in fusion power plants. In this work, we present a study on the thermo-mechanical properties of five W-based plasma-facing materials. First-principles density functional theory (DFT) calculations are combined with the quasi-harmonic approximation (QHA) theory to investigate the electronic, structural, mechanical, and thermal properties of these W-based alloys as a function of temperature. The coefficient of thermal expansion, temperature-dependent elastic constants, and several elastic parameters, including bulk and Young’s modulus, are calculated. Our work advances the understanding of the structural and thermo-mechanical behavior of W-based materials, thus providing insights into the design and selection of candidate plasma-facing materials in fusion energy devices.
We present a theory for nonlinear, resonant excitation of dynamical axions by counterpropagating electromagnetic waves in materials that break both 𝒫 and 𝒯 symmetries. We show that dynamical axions can mediate an exponential growth in the amplitude of the lower frequency (Stokes) beam. We also discuss spontaneous generation of a counterpropagating Stokes mode, enabled by resonant amplification of quantum and thermal fluctuations in the presence of a single pump laser. Remarkably, the amplification can be orders of magnitude larger than that obtained via stimulated Brillouin and Raman scattering processes, and can be modulated with the application of external magnetic fields, making stimulated axion scattering promising for optoelectronics applications.
Limited proteolysis promotes aggregation/gelation of plant proteins, but its relationship with the molecular characteristics of hydrolysates remains poorly understood. In here, response surface methodology was employed to evaluate the effects of protein concentration, Alcalase level, and hydrolysis time on the storage modulus (G′) of pea protein hydrolysates (PPH). 7.5% protein, 1.65% Alcalase, and a 6-min reaction time resulted in the highest relative increase in G′ upon heating PPH, whereas both higher (3.55%) and lower (0.3%) Alcalase levels led to less increment. Increasing the Alcalase level increased the degree of hydrolysis while decreasing the molecular weight, surface hydrophobicity, and total sulfhydryl and disulfide bond contents of PPH. Confocal laser scanning microscopy and X-ray scattering analyses found PPH produced with 1.65% Alcalase formed interconnected aggregates with dual structural domains, but not at other Alcalase conditions. These results demonstrate the role of controlled proteolysis in governing the aggregation/gelation behavior of PPH.
A detailed study of depth-resolved magnetization and microstructure of Fe16N2 thin films on MgO (001) and MgAl2O4 (001) substrates and Fe and Cr seed layers is presented. Two aspects of the magnetic properties of Fe16N2 thin films are discussed. First, magnetization enhancement at the interface is observed. Strain and nitrogen deficiency are discussed as possible interfacial mechanisms contributing to this enhancement. Second, the perpendicularly magnetized component (PMC) is identified in Fe16N2 thin films. Correlation with microstructural observations suggests that the PMC is associated with V-shaped grains that are not fully confined within the continuous Fe16N2 layer.
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Surrogate nuclear explosion debris (SNED) has emerged as a critical platform for advancing post-detonation nuclear forensic analysis in the absence of readily accessible historic materials. SNED enables controlled investigation and validation of analytical methodologies used to interrogate the chemical, isotopic, radiological, and microstructural signatures preserved in nuclear explosion debris. This review presents an integrated assessment of destructive and non-destructive analytical techniques commonly employed within decision-driven nuclear forensic workflows. Each technique is discussed individually while highlighting how it contributes to different stages of post-detonation analysis. Core methods – including gamma and alpha spectrometry, ICP-MS, TIMS, SIMS, SEM-EDS, XRF, LIBS, vibrational spectroscopy, and X-ray absorption spectroscopy – are critically evaluated with respect to forensic maturity, information content, and matrix limitations. Emphasis is placed on the role of SNED in benchmarking multi-modal workflows and identifying gaps in reproducing heterogeneity, fractionation, and radiation-driven evolution relevant to forensic attribution.
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