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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measure the effect of molten halide salt exposure on creep rupture lifetime

Recent resurgence in the research and commercial interests in molten salt reactors (MSRs) as a viable advanced reactor concept to achieve the short- and long-term climate goals has resulted in ongoing efforts to demonstrate their commercial potential. These are relying on a combination of the extensive legacy knowledge from the molten salt reactor experiment (MSRE) and relatively recent data on materials compatibility of structural materials of interest such as 316H in molten salts environments. However, there is a critical lack of data on the mechanical behavior of alloys of interest for MSRS such as 316H, 617 and 709 in molten fluoride (FLiNaK or FLiBe) or chloride (NaCl-MgCl 2 ) salts. Limited legacy data from the molten salt reactor experiment (MSRE) program showed a significant reduction in creep rupture strength of a Ni-base alloy (Ni-15Cr-7Fe wt.%) in the molten fluoride NaF-ZrF4-UF4 (50-46-4 mol.%) salt. With ongoing efforts to commercialize different molten salt reactor concepts, the industry can considerably benefit from quantitative information on the impact of molten halide salts on the engineering properties such as creep and fatigue strength of materials of interest. Creep tests for 316H were conducted with fluoride (FLiNaK) and chloride (NaCl-MgCl 2 ) salts tat 650°C/150 MPa while alloys 709 and 617 were tested with FLiNaK at 700C/158 MPa and 750C.146 MPa respectively. Baseline tests were conducted in air to assess the impact of the molten salts on the creep behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Measure the effect of molten halide salt exposure on creep rupture lifetime

Recent resurgence in the research and commercial interests in molten salt reactors (MSRs) as a viable advanced reactor concept to achieve the short- and long-term climate goals has resulted in ongoing efforts to demonstrate their commercial potential. These are relying on a combination of the extensive legacy knowledge from the molten salt reactor experiment (MSRE) and relatively recent data on materials compatibility of structural materials of interest such as 316H in molten salts environments. However, there is a critical lack of data on the mechanical behavior of alloys of interest for MSRS such as 316H, 617 and 709 in molten fluoride (FLiNaK or FLiBe) or chloride (NaCl-MgCl 2 ) salts. Limited legacy data from the molten salt reactor experiment (MSRE) program showed a significant reduction in creep rupture strength of a Ni-base alloy (Ni-15Cr-7Fe wt.%) in the molten fluoride NaF-ZrF4-UF4 (50-46-4 mol.%) salt. With ongoing efforts to commercialize different molten salt reactor concepts, the industry can considerably benefit from quantitative information on the impact of molten halide salts on the engineering properties such as creep and fatigue strength of materials of interest. Creep tests for 316H were conducted with fluoride (FLiNaK) and chloride (NaCl-MgCl 2 ) salts tat 650°C/150 MPa while alloys 709 and 617 were tested with FLiNaK at 700C/158 MPa and 750C.146 MPa respectively. Baseline tests were conducted in air to assess the impact of the molten salts on the creep behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

CLM5 Simulations of Soil Moisture and Grain Carbon for CONUS at 0.125 degrees

This dataset provides 0.125° gridded simulations of soil moisture and crop grain carbon for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980–2015) and mid-century future projections (2020–2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM-only): These scenarios apply future atmospheric forcings while holding land use and land cover change (LULCC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the file names as atm45cooler, atm45hotter, atm85cooler, and atm85hotter. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULCC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as atm45cooler_ssp3, atm45hotter_ssp3, atm45cooler_ssp5, atm45hotter_ssp5, atm85cooler_ssp3, atm85hotter_ssp3, atm85cooler_ssp5, and atm85hotter_ssp5. Please refer to the README file for detailed information on file structure, variables, units, and data formats.

Yao, Lili [Pacific Northwest National Laboratory] ↗
DOE OSTI2026

CLM5 Simulations of Soil Moisture and Gross Primary Productivity for CONUS at 0.125 degrees

This dataset provides 0.125-degree gridded simulations of soil moisture and gross primary productivity (GPP) for the Contiguous United States (CONUS), generated using the Community Land Model version 5 (CLM5) with the biogeochemistry module enabled. The data covers a historical baseline (1980-2015) and mid-century future projections (2020-2055). Future projections are organized into two sets of scenarios to distinguish the impacts of different drivers: (1) Atmospheric Only (ATM): These scenarios apply future atmospheric forcings while holding land use and land cover (LULC) at historical baseline levels. The atmospheric forcings represent moderately versus severely hotter/drier atmospheric conditions (dynamically downscaled perturbed thermodynamics simulations based on CMIP6 SSP245 and SSP585 warming signals), each with cooler versus hotter Earth System Model temperature sensitivity instantiations. These scenarios are identified in the folder names as rcp45_cooler_near, rcp45_hotter_near, rcp85_cooler_near, and rcp85_hotter_near. (2) Coupled Atmospheric and Land-Use (LAND+ATM): These scenarios apply future atmospheric forcing together with future LULC by pairing atmospheric pathways with lower versus higher population/economic growth scenarios representing Shared Socioeconomic Pathways 3 and 5 (SSP3 and SSP5). These scenarios are identified in the file names as ssp3_rcp45_cooler_near, ssp3_rcp45_hotter_near, ssp5_rcp85_cooler_near, and ssp5_rcp85_hotter_near. Please refer to the "README_first.md" file for detailed information on file structure, variables, units, and data formats.

drought↗
DOE OSTI2024

Effect of solvothermal synthesis parameters on the crystallite size and atomic structure of cobalt iron oxide nanoparticles

We here investigate how the synthesis method affects the crystallite size and atomic structure of cobalt iron oxide nanoparticles. By using a simple solvothermal method, we first synthesized cobalt ferrite nanoparticles of ca. 2 and 7 nm, characterized by Transmission Electron Microscopy (TEM), Small Angle X-ray scattering (SAXS), X-ray and neutron total scattering. The smallest particle size corresponds to only a few spinel unit cells. Nevertheless, Pair Distribution Function (PDF) analysis of X-ray and neutron total scattering data shows that the atomic structure, even in the smallest nanoparticles, is well described by the spinel structure, although with significant disorder and a contraction of the unit cell parameter. These effects can be explained by the surface oxidation of the small nanoparticles, which is confirmed by X-ray near edge absorption spectroscopy (XANES). Neutron total scattering data and PDF analysis reveal a higher degree of inversion in the spinel structure of the smallest nanoparticles. Neutron total scattering data also allow magnetic PDF (mPDF) analysis, which shows that the ferrimagnetic domains correspond to ca. 80% of the crystallite size in the larger particles. A similar but less well-defined magnetic ordering was observed for the smallest nanoparticles. Finally, we used a co-precipitation synthesis method at room temperature to synthesize ferrite nanoparticles similar in size to the smallest crystallites synthesized by the solvothermal method. Structural analysis with PDF demonstrates that the ferrite nanoparticles synthesized via this method exhibit a significantly more defective structure compared to those synthesized via a solvothermal method.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2025

Structure of beta-decaying states in the deformed, neutron-rich nucleus 104 Nb

Excited structures in 104 Mo were populated by β decays of the ground and isomeric states in the neutron-rich nucleus 104 Nb. The beams were produced by the CARIBU facility at Argonne National Laboratory, re-accelerated by the ATLAS accelerator and implanted on a moving-tape system in the middle of the GAMMASPHERE array. Separate decay schemes for the two β-decaying states in 104 Nb were constructed for the first time. The structure of the isomers are discussed in the framework of the deformed Nilsson model and systematics of known quasiparticle structures in neighboring nuclei.

Nilsson model↗
DOE OSTI2025

Text Mining for Process–Structure–Properties Relationships in Metals

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery—although significant challenges remain. While LLMs offer promising few- and zero-shot learning capabilities, particularly valuable in the materials domain where expert annotations are scarce, general-purpose LLMs often fail to address key materials-specific queries without further adaptation. To bridge this gap, fine-tuning LLMs on human-labeled data is essential for effective structured knowledge extraction (Liu in The Importance of Human-Labeled Data in the Era of LLMs, 2023). Here, in this study, we introduce a novel annotation schema designed to extract generic process–structure–properties relationships from scientific literature. We demonstrate the utility of this approach using a dataset of 128 abstracts, with annotations drawn from two distinct domains: high-temperature materials (Domain I) and uncertainty quantification in simulating materials microstructure (Domain II). Initially, we developed a conditional random field (CRF) model based on MatBERT—a domain-specific BERT variant—and evaluated its performance on Domain I. Subsequently, we compared this model with a fine-tuned LLM (GPT-4o from OpenAI) under identical conditions. Our results indicate that fine-tuning LLMs can significantly improve entity extraction performance over the BERT-CRF baseline on Domain I. However, when additional examples from Domain II were incorporated, the performance of the BERT-CRF model became comparable to that of the GPT-4o model. These findings underscore the potential of our schema for structured knowledge extraction and highlight the complementary strengths of both modeling approaches.

Materials science↗
DOE OSTI2025

Probing the local structure of Bi 2 O 3 chemical derivatives: the neglected cation sublattice

High-resolution synchrotron X-ray diffraction data confirm the stabilization of the average structure at ambient temperatures as a defect fluorite structure in ternary and quaternary oxides of Bi 2 O 3 , where some Bi cations are replaced with Dy 3+ and/or Er 3+ , Nb 5+ and W 6+ . Rietveld refinement of the diffraction data indicates that when the larger cation Er 3+ (0.89 Å) is replaced with smaller cations W 6+ (0.42 Å) and/or Nb 5+ (0.48 Å), the unit-cell parameter a, based on the relative ionic radii of the substituents, counterintuitively increases. Total scattering data show that the identity of the cation substituent is important in determining the positions of some Bi cations in the defect fluorite structure. Er 3+ and Dy 3+ cations induce monoclinic-like distortions that extend only locally in the Bi 3+ sublattice. X-ray absorption spectroscopy provides evidence that some Dy cations, in the δ phases, prefer to keep a similar local environment to that in the parent Dy 2 O 3 . W cations are found to be predominantly tetrahedrally coordinated in the δ phases explored in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2026

Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.

Anomaly analysis↗
DOE OSTI2025

Low Frequency Vibrational Modes of Two-Dimensional Lead-Free Metal Halide Double Perovskites

2D layered double perovskites of (S-MPA) 4 AgBiI 8 (MPA-AgBiI 8 ) and (S-MPA) 4 CuBiI 8 (MPA-CuBiI 8 ) (S-MPA, S-β-methylphenethylammonium) were synthesized with a hydrothermal method. The crystal structure of MPA-AgBiI 8 was determined using single-crystal X-ray diffraction (scXRD). Powder XRD (pXRD) data suggest that the crystal structure of MPA-CuBiI 8 is more complex than that of MPA-AgBiI 8 . UV-Vis electronic absorption spectra of these perovskites reveal a bandgap of 2.03 eV for both. Short exciton lifetime from time-resolved photoluminescence (TRPL) results and low PL intensity of the Cu-based perovskite suggest a high density of trap states within the bandgap. Low frequency Raman spectra of both materials show distinct peaks and a slightly higher frequency for the Cu-based perovskite than the Ag-based perovskite. Density functional theory (DFT) calculations were conducted to simulate the low frequency Raman spectra and help explain the different phonon modes, which are collective vibrations of metal halide bond bending and stretching within the Ag- and Cu-centered octahedra coupled with MPA libration and twisting within the inorganic layer. The DFT theory also quantified octahedral distortions in the two perovskites. Furthermore, this combined experimental and computational study provides new insights into the low frequency vibrations of 2D perovskites.

Halogens↗
DOE OSTI2026

Flash Communication: Properties and Applications of a Pentavalent Bromoantimony Lewis Acid

While normally reluctant toward oxidation, SbBr 3 can be oxidized with 3,4,5,6-tetrachloro-1,2-benzoquinone (o-chloranil) in the presence of a Lewis base like the bromide anion or triphenylphosphine oxide (PPh 3 O) to form the tetrachlorocatecholate (cat Cl ) derivatives [SbBr 4 (cat Cl )] − ([2-Br] − ) and SbBr 3 (cat Cl )•OPPh 3 (2•OPPh 3 ), respectively. Structural, spectroscopic, and computational data indicate that the SbBr 3 (cat Cl ) fragment is similarly, if slightly less, Lewis acidic than the previously reported SbCl 3 (cat Cl ) fragment. The SbBr 3 /o-chloranil system, as well as its previously reported counterpart SbCl 3 /o-chloranil, were tested as Lewis acid catalysts for C−O bond metathesis and polycarbonate depolymerization. These results identified SbX 3 /o-chloranil pairs (X = Cl, Br) as simple main-group platforms for C−O bond cleavage chemistry.

Anions↗
DOE OSTI2026

Vibrational and electronic properties of Np 2 O 5 from experimental spectroscopy and first principles calculations

High-valence actinide oxides are critical to understanding the behavior of 5f-electrons, yet their structural and electronic properties remain poorly understood due to challenges in synthesis and handling. We report the first Raman spectroscopic study of single-crystalline Np 2 O 5 and the first scanning tunneling spectroscopy (STS) measurement on any neptunium-containing material. Hydrothermally synthesized crystals were structurally verified by X-ray diffraction. Raman spectra revealed sharply resolved vibrational features, including previously unreported low-frequency modes. STS measurements revealed a band gap of 1.5 eV. Density functional theory (DFT) enables vibrational mode assignments, reveals neptunium-dominated low-frequency phonons, oxygen-dominated high-frequency modes, and predicts an indirect band gap of 1.68 eV. This predicted value is in excellent agreement with the experimentally measured STS gap. This combined Raman, DFT, and STS approach provides a robust framework for correlating lattice dynamics and electronic structure in actinide materials, providing benchmark data for Np 2 O 5 , and opening new avenues for probing structure–property relationships in complex f-electron materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Collinear limit of the four-point energy correlator in N = 4 supersymmetric Yang-Mills theory

We present a compact formula, expressed in terms of classical polylogarithms up to weight three, for the leading order four-point energy correlator in maximally supersymmetric Yang-Mills theory, in the limit where the four detectors are collinear. This formula is derived by combining a simplified, manifestly dual conformal invariant form of the 1 → 4 splitting function obtained from the square of the tree-level five-particle form factor of stress-tensor multiplet operators, with a novel integration-by-parts algorithm operating directly on Feynman parameter integrals. Our results provide valuable data for exploring the structure of physical observables in perturbation theory, and for calculations of jet substructure observables in quantum chromodynamics. Published by the American Physical Society 2024

Chicherin, Dmitry (ORCID:000000028985084X)↗
DOE OSTI2024

X-ray scattering based scanning tomography for imaging and structural characterization of cellulose in plants

X-ray and neutron scattering have long been used for structural characterization of cellulose in plants. Due to averaging over the illuminated sample volume, these measurements traditionally overlooked the compositional and morphological heterogeneity within the sample. Here, a scanning tomographic imaging method is described, using contrast derived from the X-ray scattering intensity, for virtually sectioning the sample to reveal its internal structure at a resolution of a few micrometres. This method provides a means for retrieving the local scattering signal that corresponds to any voxel within the virtual section, enabling characterization of the local structure using traditional data-analysis methods. This is accomplished through tomographic reconstruction of the spatial distribution of a handful of mathematical components identified by non-negative matrix factorization from the large dataset of X-ray scattering intensity. Joint analysis of multiple datasets, to find similarity between voxels by clustering of the decomposed data, could help elucidate systematic differences between samples, such as those expected from genetic modifications, chemical treatments or fungal decay. The spatial distribution of the microfibril angle can also be analyzed, based on the tomographically reconstructed scattering intensity as a function of the azimuthal angle.

36 MATERIALS SCIENCE↗