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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electron-Proton Scattering Event Generation using Structured Tokenization

Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.

Goldenberg, Steven [Thomas Jefferson National Acce↗
DOE OSTI2025

Electron-Proton Scattering Event Generation using Structured Tokenization

Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.

Goldenberg, Steven [Thomas Jefferson National Acce↗
DOE OSTI2025

Building morphologies of the USA structures database; a gauntlet feature set

In recent years there has been a proliferation of methods and data to extract building footprints from satellite imagery. However there has been very little effort to provide additional insight about these buildings beyond their spatial location and shape. Features derived from their geometries can be used to better characterize these buildings which are critical for further research and development. In this work a set of 65 unique features for every building for more than 131 million buildings covering the US has been developed. This rich feature dataset will enable researchers, policymakers and various agencies to derive additional building characteristics like height, occupancy type, and help to gain valuable and new insights of the built environment.

Environmental sciences↗
DOE OSTI2025

Utah FORGE: Well 16B(78)-32 Drill Core Fracture Analysis Images and Data

This dataset contains drilling core data from well 16B(78)-32, including PDF documents with flattened core images annotated by feature type and core interval, as well as spreadsheets detailing feature morphologies by depth, planar feature measurements, and planar feature orientations rotated to in situ conditions. Core was recovered from three intervals, one per stimulation stage, in the crystalline rocks affected by the stimulation of well 16A(78)-32. Seven core runs were conducted, yielding 135.8 feet of recovered core. Features in the core were categorized into planar fractures, semi-planar fractures, unbroken mineralized fractures, rough fractures, curviplanar fractures, concave-convex surfaces, and planar compositional features such as mylonite or dike-like structures. Planar features were measured while the core was positioned horizontally, with the core axis aligned to a downhole azimuth of 42 degrees. Planar core measurements from stimulations 2 and 3 that could be confidently correlated with FMI data were rotated to in situ orientations. This was done by rotating the planes along vertical and horizontal axes to match the azimuth and inclination data recorded in the directional survey of well 16B(78)-32, as well as applying an axial rotation to resemble the fracture orientations observed in the FMI log at corresponding depths. Coherent sets of planar fracture measurements were made by aligning the core within each 3-foot section of the dissected core barrel, and between adjacent 3-foot sections within a core run by matching rock fabrics, saw cuts and/or tool marks. Where coherent fracture measurements could not be made within a core run, data sets are denoted by a subscript (i.e. 2-Ta and 2-Tb both come from tangent core run number 2).

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

The pectin puzzle: Decoding the fine structure of rhamnogalacturonan-I (RG-I) in Arabidopsis thaliana uncovers new pectin features

Pectin is generally divided into four distinct structural categories, namely homogalacturonan, xylogalacturonan, rhamnogalacturonan I (RG-I) and rhamnogalacturonan II. While much of the structural diversity of homogalacturonan, xylogalacturonan and rhamnogalacturonan II has been elucidated, the structural features of RG-I are less well understood. In this work, we employed multiple complementary analytical techniques to present a detailed structural analysis of RG-I in the model species Arabidopsis thaliana . Starting with highly purified RG-I from different Arabidopsis tissues, we employed comparative linkage and nuclear magnetic resonance analysis along with mass spectrometry analysis of enzymatically digested RG-I oligosaccharides. Besides the presence of the canonical α-1,5-arabinan, β-1,4-galactan, β-1,6-galactan and arabinogalactan RG-I side chains of varying lengths, we show that a large portion of the β-1,6-galactan is terminated by either 4-O-methyl β-glucuronic acid (GlcA) residues or, to a smaller degree, β-GlcA that lacks the Me-ether group. Importantly, O-acetylation of RG-I GalA residues is a minor modification while 10 % of the backbone Rha residues are 3-O-acetylated, and most of the acetylated Rha is additionally branched with β-galactose substituents. Taken together, the combined results of these different analytical techniques present the most comprehensive structural overview of Arabidopsis thaliana RG-I to date.

25 ENERGY STORAGE↗
DOE OSTI2025

Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods

Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Pressure-induced cation and vacancy disorder-order transition and near-zero τ f in deficient hexagonal perovskite Ba 8 ZnTa 6 O 24 dielectrics

Pressure applications can enable the tuning of atomic/defect ordering and provide access to new functional materials. Here, in this study, we report that pressure-induced structural transformation featuring disorder-order transition of both cations and vacancies in the 8-layer deficient hexagonal perovskite tantalate dielectrics Ba 8 ZnTa 6 O 24 , which transformed the structure from twin to shift and remarkably lowered the temperature coefficient of resonant frequency τ f down to near zero (∼0.56 ppm/°C) from 38 ppm/°C for the twinned precursor. The atomic scale STEM-HAADF and EDS results confirm the ordering of Zn in the Ta host at the nanometer scale in the shifted material featuring well-ordered Ba 8 ZnTa 6 O 24 slabs intergrown with Ba 3 ZnTa 2 O 9 and Ba 5 Ta 4 O 15 monolayers and anti-phase grain boundaries as planar defects. The pressure-induced twin-shift structural transformation of Ba 8 ZnTa 6 O 24 features the rare constant concentration of the hexagonal stacked layers, which is allowed by the vacancy ordering at the central layers of face-shared octahedral (FSO) trimers avoiding the FSO B-B repulsion, and remarkably the faster cationic ordering kinetics compared with the 2:1 ordered complex perovskites. Although the inclusion of numerous planar defects and the oxidizable atomic defects led to significant p-type conduction and inhomogeneous electrical microstructures, resulting in an extraordinarily high extrinsic dielectric loss for the high-pressure shifted Ba 8 ZnTa 6 O 24 pellet, the intrinsically near-zero τ f could make the shifted Ba 8 ZnTa 6 O 24 perovskite an ideal microwave dielectric resonator candidate if the defects could be eliminated.

high pressure↗
DOE OSTI2024

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Science & Technology - Other Topics↗
DOE OSTI2024

Uncovering an Interfacial Band Resulting from Orbital Hybridization in Nickelate Heterostructures

The interaction of atomic orbitals at the interface of perovskite oxide heterostructures has been investigated for its profound impact on the band structures and electronic properties, giving rise to unique electronic states and a variety of tunable functionalities. In this study, we conducted an extensive investigation of the optical and electronic properties of epitaxial NdNiO 3 thin films grown on a series of single crystal substrates. Unlike films synthesized on other substrates, NdNiO 3 on SrTiO 3 (NNO/STO) gives rise to a unique band structure which features an additional unoccupied band situated above the Fermi level. Our comprehensive investigation, which incorporated a wide array of experimental techniques and density functional theory calculations, revealed that the emergence of the interfacial band structure is primarily driven by the orbital hybridization between Ti 3d orbitals of the STO substrate and O 2p orbitals of the NNO thin film. Furthermore, exciton peaks have been detected in the optical spectra of the NNO/STO film, attributable to the pronounced electron-electron (e-e) and electron-hole (e-h) interactions propagating from the STO substrate into the NNO film. Further, these findings underscore the substantial influence of interfacial orbital hybridization on the electronic structure of oxide thin-films, thereby offering key insights into tuning their interfacial properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Extended X-ray absorption spectroscopy using an ultrashort pulse laboratory-scale laser-plasma accelerator

Laser-driven compact particle accelerators can provide ultrashort pulses of broadband X-rays, well suited for undertaking X-ray absorption spectroscopy measurements on a femtosecond timescale. Here the Extended X-ray Absorption Fine Structure (EXAFS) features of the K-edge of a copper sample have been observed over a 250 eV window in a single shot using a laser wakefield accelerator, providing information on both the electronic and ionic structure simultaneously. This capability will allow the investigation of ultrafast processes, and in particular, probing high-energy-density matter and physics far-from-equilibrium where the sample refresh rate is slow and shot number is limited. For example, states that replicate the tremendous pressures and temperatures of planetary bodies or the conditions inside nuclear fusion reactions. Using high-power lasers to pump these samples also has the advantage of being inherently synchronised to the laser-driven X-ray probe. A perspective on the additional strengths of a laboratory-based ultrafast X-ray absorption source is presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Property optimized energy absorber for automotive bumpers utilizing multi-material and structural design strategies

This study proposes a novel design for automotive bumper using optimized lattice structures and multi-materials to balance low-speed collision and high-speed pedestrian impact performance. Different blends of 20 % carbon fiber-reinforced acrylonitrile butadiene styrene with thermoplastic polyurethane were used to tailor material properties. The energy absorber features lattice structures with customized mechanical responses, created by varying the incline angle θ from 0 to 180°. We conducted 576 finite element simulations on a half-scale model to optimize energy absorption and stiffness, leading to 66 optimized designs that met both low-speed and high-speed impact criteria. Two sub-scale optimized energy absorbers with different peak forces—both meeting low-speed impact requirements—were 3D printed and validated through drop-weight testing. The one with lower peak stress demonstrated a more compliant response, exhibiting approximately 90 % lower initial peak force and an increase in energy absorption of around 33 % (from 24 J to 32 J). Compared to the baseline triangular lattice, the optimized absorber increased energy absorption by 68 % from (19 J to 32 J) and reduced peak stress by 70 %. It also showed near-complete recovery with minimal fractures, making it suitable for repeated use. This design improves safety while offering a lightweight, durable, and cost-effective bumper system.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2025

Terrace‐Edge‐Induced Domain Nucleation in Room‐Temperature 2D Magnetic Van Der Waals Heterostructures

Abstract Terrace‐edges, which are step‐like height discontinuities formed during layer stacking or exfoliation, can locally modify magnetic characteristics. Magnetic domain behavior and its structural dependence in a 2D van der Waals (vdW) heterostructure composed of two distinct room‐temperature ferromagnets: Fe 3 GaTe 2 , an intrinsic vdW ferromagnet, and vanadium‐doped WSe 2 , a transition metal dichalcogenide exhibiting defect‐induced magnetism, is investigated. Using magnetic transmission X‐ray microscopy, the formation and annihilation of magnetic domains are directly observed and it is found that domains preferentially form at terrace‐edges of the heterostructure. Micromagnetic simulations reveal that in‐plane magnetization tilting near the terrace‐edge results in a localized maximum in total magnetic energy that promotes domain formation. The findings highlight the significant role of structural edge features and interfacial magnetic interactions in determining domain formation in hybrid 2D vdW magnetic systems, offering a new route to spatially controlled magnetization at room temperature.

Lee, Jieun↗
DOE OSTI2025

Solving high-dimensional partial integral differential equations: The finite expression method

Partial integro-differential equations (PIDEs) have broad applications in the sciences, from electro-magnetism to options pricing. Here, in this paper, we introduce a new finite expression method (FEX) to solve PIDEs. This approach builds upon the original FEX and its inherent advantages with new advances: 1) A novel method of parameter grouping is proposed to reduce the number of coefficients in high-dimensional function approximation; 2) A Taylor series approximation method is implemented to significantly improve the computational efficiency and accuracy of the evaluation of the integral terms of PIDEs. The new FEX based method, denoted FEX-PG to indicate the addition of the parameter grouping (PG) step to the algorithm, provides both high accuracy and interpretable numerical solutions, with the outcome being an explicit equation that facilitates intuitive understanding of the underlying solution structures. These features are often absent in traditional methods, such as finite element methods (FEM) and finite difference methods, as well as in deep learning-based approaches. To benchmark our method against recent advances, we apply the new FEX-PG to solve benchmark PIDEs in the literature. In high-dimensional settings, FEX-PG exhibits strong and robust performance, achieving relative errors on the order of single precision machine epsilon, significantly outperforming existing approaches based on neural networks.

Combinatorial optimization↗
DOE OSTI2025

Influence of temperature, oxygen partial pressure, and microstructure on the high-temperature oxidation behavior of the SiC Layer of TRISO particles

Tristructural isotropic (TRISO)-coated fuel particles are designed for use in high-temperature gas-cooled nuclear reactors, featuring a structural SiC layer that may be exposed to oxygen-rich environments over 1000 °C. Surrogate TRISO particles were tested in 0.2–20 kPa O 2 atmospheres to observe the differences in oxidation behavior. Oxide growth mechanisms remained consistent from 1200–1600 °C for each P O$_2$ , with activation energies of 228 ± 7 kJ/mol for 20 kPa O 2 and 188 ± 8 kJ/mol for 0.2 kPa O 2 . At 1600 °C, kinetic analysis revealed a change in oxide growth mechanisms between 0.2 and 6 kPa O2. In 0.2 kPa O 2 , oxidation produced raised oxide nodules on pockets with nanocrystalline SiC. Oxidation mechanisms were determined using Atom probe tomography. Active SiC oxidation occurred in C-rich grain boundaries with low P O$_2$ , leading to SiO 2 buildup in porous nodules. Here, this phenomenon was not observed at any temperature in 20 kPa O 2 environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Stabilizing lithium superoxide formation in lithium-air batteries by Janus chalcogenide catalysts

Solid lithium peroxide (Li 2 O 2 ) is the major discharge product in Li-air batteries. However, the electronically insulating nature of Li 2 O 2 tends to affect the battery’s performance such as the polarization gap and cyclability. On the other hand, lithium superoxide (LiO 2 ), generated through a one-electron transfer process, offers greater electronic conductivity, lower charge transfer resistance, and thus reduced charge potential. Nevertheless, LiO 2 long-term stabilization as a final product remains a significant challenge. Here, in this study, we present the molybdenum (Mo)-based Janus chalcogenide family featuring asymmetric structures as a new generation of cathode catalysts for Li-air batteries. These catalysts demonstrate remarkable efficacy in stabilizing LiO 2 discharge products, even under high current densities of 5000 mA/g (corresponding to 0.5 mA/cm 2 ). Our density functional calculations provide an understanding of why the asymmetric Mo-Janus chalcogenides result in LiO 2 formation whereas the symmetric Mo-dichalcogenides produce Li 2 O 2 as the discharge product. These results pave the way to explore a new generation of advanced catalysts for superoxide-based Li-air batteries.

Chalcogenide↗
DOE OSTI2025

Emergence of heavy-fermion behavior and distorted square nets in partially vacancy-ordered Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5)

Disorders in intermetallic systems belonging to the CeNiSi 2 -family are frequently overlooked. Even compounds presumed to be stoichiometric, such as YFeGe 2 , can be misidentified. Here, in this study, we report a series of Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5) compounds and show, using high-resolution synchrotron X-ray diffraction, that they feature asymmetrical structural distortions in the Fe and Ge sites that lead to a superstructure with partially ordered Fe vacancies and distorted Ge square-net in the triclinic crystal system, space group P[1 with combining macron] with a = 11.4441(3) Å, b = 32.7356(7) Å, c = 11.4456(3) Å, α = 79.6330(10)°, β = 88.3300(10)°, and γ = 79.6350 (10)°. The unit cell is 16 times the conventional orthorhombic cell with the space group Cmcm. We identified the lower and upper limits for Fe in Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5). Our physical property measurements yielded a Sommerfeld coefficient γ = 39.8 mJ mole −1 K −2 , a Kadowaki–Woods ratio of 1.2 × 10 −5 μΩ cm mole 2 K 2 mJ −2 , and a Wilson ratio of 1.83, suggesting heavy fermion behavior in the absence of f electrons, a rather rare case. Furthermore, we observed strong spin frustration and noted findings indicating possible superconductivity associated with the Fe content.

Zhao, Hengdi [Argonne National Laboratory (ANL), A↗