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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Investigation of U content in J-swipe/PIC samples

This final report documents the results of an evaluation of the U content in J-swipes for the International Atomic Energy Agency (IAEA). J-swipes are manufactured with inked numbers (1–30) and U backgrounds that are considered consistent across different lots and manufacturing times. These characteristics make them ideal for use by IAEA inspectors for pre-inspection check samples to ensure inspectors are not cross-contaminating sites. However, recent work has found elevated U concentrations in the inked numbers compared with the J-swipe material. To determine whether the inked numbers correlate with the U backgrounds, triplicate U content measurements were made on Swipes 1, 2, 11, 22, and 28. Triplicates of each swipe were processed through the standard chemistry used by the Oak Ridge National Laboratory Network of Analytical Laboratories team and analyzed for U content by isotope dilution via multicollector inductively coupled plasma mass spectrometry. Finally, samples were made by mass consisting of only ink areas and only cotton swipe material to determine the trace element differences between the cotton and the ink.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Structure of diopside, enstatite, and magnesium aluminosilicate glasses: A joint approach using neutron and x-ray diffraction and solid-state NMR

Neutron diffraction with magnesium isotope substitution, high energy x-ray diffraction, and 29 Si, 27 Al, and 25 Mg solid-state nuclear magnetic resonance (NMR) spectroscopy were used to measure the structure of glassy diopside (CaMgSi 2 O 6 ), enstatite (MgSiO 3 ), and four (MgO) x (Al 2 O 3 ) y (SiO 2 ) 1–x–y glasses, with x = 0.375 or 0.25 along the 50 mol. % silica tie-line (1 – x – y = 0.5) or with x = 0.3 or 0.2 along the 60 mol. % silica tie-line (1 – x – y = 0.6). The bound coherent neutron scattering length of the isotope 25 Mg was remeasured, and the value of 3.720(12) fm was obtained from a Rietveld refinement of the powder diffraction patterns measured for crystalline 25 MgO. The diffraction results for the glasses show a broad asymmetric distribution of Mg–O nearest-neighbors with a coordination number of 4.40(4) and 4.46(4) for the diopside and enstatite glasses, respectively. As magnesia is replaced by alumina along a tie-line with 50 or 60 mol. % silica, the Mg–O coordination number increases with the weighted bond distance as less Mg 2+ ions adopt a network-modifying role and more of these ions adopt a predominantly charge-compensating role. 25 Mg magic angle spinning (MAS) NMR results could not resolve the different coordination environments of Mg 2+ under the employed field strength (14.1 T) and spinning rate (20 kHz). The results emphasize the power of neutron diffraction with isotope substitution to provide unambiguous site-specific information on the coordination environment of magnesium in disordered materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ab-Initio molecular dynamics simulations of binary NaCl-ThCl 4 and ternary NaCl-ThCl 4 -UCl 3 molten salts

Molten salt reactor (MSR) with Thorium (Th) fuel cycle has attracted growing attention due to its merits such as safety, low radioactive waste production, and non-proliferation. The design and safe operation of MSR rely on a thorough understanding of the thermophysical properties of molten salts over a wide range of composition and temperature. However, experimental data of Th-based molten salts are limited due to the inherent challenges of dealing with corrosive molten salts and the radioactive nature of actinides. Here, in this work, thermophysical properties including density, heat capacity, thermal expansion coefficient, and mixing energy of binary NaCl-ThCl 4 and ternary NaCl-ThCl 4 -UCl 3 molten salts are explored using ab-initio molecular dynamic simulations (AIMD) with the dDsC dispersion correction. The calculated mixing energy of binary NaCl-ThCl 4 exhibits a minimum close to the eutectic composition. The heat capacity of the mixtures is linearly dependent on the mole fraction of each component. 7-fold and 6-fold coordinated Th complexes are dominant in the mixtures. 8-fold coordinated Th complex increases with ThCl 4 fraction due to the formation of network structures. The average coordination number of Th exhibits a minimum near the eutectic composition. The minimum mixing energy for the ternary mixtures is observed in systems with a composition close to [NaCl] 0.5 [ThCl 4 ] 0.25 [UCl 3 ] 0.25 . The density positively deviates from the ideal solution in mixtures near this composition. These results are important to fill the data and knowledge gap of ThCl 4 molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dispersion of modified fumed silica in elastomeric nanocomposites

In polymer nanocomposites, surface modification of silica aggregates can shield Coulombic interactions that inhibit agglomeration and formation of a network of agglomerates. Surface modification is usually achieved with silane coupling agents although carbon-coating during pyrolytic silica production is also possible. Pyrogenic silica with varying surface carbon contents were dispersed in styrene-butadiene (SBR) rubber to explore the impact on hierarchical dispersion, the emergence of meso-scale structures, and the rheological response. Pristine pyrogenic silica aggregates at concentrations above a critical value (related to the Debye screening length) display correlated meso-scale structures and poor filler network formation in rubber nanocomposites due to the presence of silanol groups on the surface. Here, in the present study, flame synthesized silica with sufficient surface carbon monolayers can mitigate the charge repulsion thereby impacting network structural emergence. The impact of the surface carbon on the van der Waals enthalpic attraction, a * , is determined. The van der Waals model for polymer nanocomposites is drawn through an analogy between thermal energy, k B T, and the accumulated strain, γ. The rheological response of the emergent meso-scale structures depends on the surface density of both carbon and silanol groups.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

Here we discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co 20 Fe 61 B 19 specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe 23 B 6 as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Grain boundary velocity and curvature are not correlated in Ni polycrystals

Grain boundary velocity has been believed to be correlated to curvature, and this is an important relationship for modeling how polycrystalline materials coarsen during annealing. We determined the velocities and curvatures of approximately 52,000 grain boundaries in a nickel polycrystal using three-dimensional orientation maps measured by high-energy diffraction microscopy before and after annealing at 800°C. Unexpectedly, the grain boundary velocities and curvatures were uncorrelated. Instead, we found strong correlations between the boundary velocity and the five macroscopic parameters that specify grain boundary crystallography. The sensitivity of the velocity to grain boundary crystallography might be the result of defect-mediated grain boundary migration or the anisotropy of the grain boundary energy. The absence of a correlation between velocity and curvature likely results from the constraints imposed by the grain boundary network and implies the need for a new model for grain boundary migration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Study of beyond nearest-neighbor environment and intermediate-range order in a sodium aluminosilicate geopolymer using Reverse Monte Carlo modeling

A large scale three-dimensional structural model of a geopolymer with an approximate composition of NaAlSi 2 O 6 .5.5H 2 O obtained by Reverse Monte Carlo (RMC) modeling based on experimental high-energy x-ray diffraction is presented for the first time in order to obtain information regarding beyond nearest-neighbor environment and intermediate-range order (IRO). RMC model exhibits a three-dimensional network consisting of randomly cross-linked AlO 4 and SiO 4 tetrahedral units with uniformly distributed Na atoms and H 2 O molecules. The bond angle distributions, i.e. T-O-T, O-T-O and Na-O-Na angles, are somewhat distorted with lower values compared to analogous crystal systems. The origin of the first peak in the structure factor indicating IRO is investigated using the partial structure factors; Na-Na, Si-Na, Al-Si, O-H, and H-H atom pairs are found to be the main contributors. Ring size distribution analysis demonstrates that the structure mainly involves 6-, 7- and 8-membered rings. Finally, the coherence length of these IRO characteristics is ~10.3 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2025

3D printable silicone compositions exhibiting high toughness and low durometer

3D printable silicones can be designed with varying crosslink density, network structures, and types of reinforcing additives. Within this design space, one may tailor the uncured material’s rheology and mechanical response to access a wide range of potential applications. However, printable, low modulus silicones, particularly those applicable to direct ink write, are underreported in published literature. To address the need for higher performance, low modulus silicones with demonstrated printability, a new set of ca. 20–50 Shore A hardness silicone elastomers exhibiting ca. 7 MPa ultimate tensile strength and ca. 400–1200% elongation at break is presented. Mechanical properties were analyzed, providing insight to the effects of formulation constituents on mechanical properties. Cyclic mechanical testing of the silicone formulations was also performed, and the energy loss and permanent set throughout cycling were determined. In conclusion, printed structures demonstrate the feasibility of these new silicones as durable frameworks for novel soft device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of processing humidity on ionomer film structure and performance in hydroxide exchange membrane electrolyzers

Hydroxide exchange membrane electrolyzers (HEMELs) enable hydrogen production using low-cost, earth-abundant materials. Improving electrode fabrication is integral to enhancing device performance, and ionomer-responsible for transporting hydroxide and mechanically supporting the catalyst-is a major component. Here, we use experiments and computation to study the effects of relative humidity (RH) during the drying process of poly(aryl piperidinium) ionomer films on HEMEL electrodes. Broadly, the drying environments determine the physical structure and electrochemical traits of the ionomer network. High RH drying yields a highly porous network with excessive water uptake, structural defects, washout, and 64% reduction in hydroxide conductivity. Extremely low RH drying produces an overly compact pore network that hinders hydroxide mobility. In contrast, moderately low RH drying (9% RH) creates an ionomer film with well-balanced traits: excellent mechanical stability and connectivity needed for catalyst retention and hydroxide transport, which improves HEMEL performance by 40% at 1.8 V compared to suboptimal RHs. This research advances HEMEL manufacturing by providing a simple, scalable, and low-cost approach to optimize electrode ionomer films.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nonthermal Bonding Origin of a Novel Photoexcited Lattice Instability in SnSe

Lattice dynamics measurements are often crucial tools for understanding how materials transform between different structures. Here, we report time-resolved x-ray scattering-based measurements of the nonequilibrium lattice dynamics in SnSe, a monochalcogenide reported to host a novel photoinduced lattice instability. By fitting interatomic force models to the fluence dependent excited-state dispersion, we determine the nonthermal origin of the lattice instability to be dominated by changes of interatomic interactions along a bilayer-connecting bond, rather than of an intralayer bonding network that is of primary importance to the lattice instability in thermal equilibrium.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Simulation to a Newborn Supernova Remnant from a Low-mass Iron Core Star

Supernova remnant observations show a high degree of asymmetry, mixing, and inhomogeneity. These asymmetries are seeded during the early seconds of the explosion and are further enhanced and modified as the shock and ejecta move through the stellar progenitor and into the circumstellar medium. We present simulations of a 9.6 M⊙ zero-metallicity progenitor initialized after shock revival and evolved for several years when the ejecta is in the circumstellar medium. A suite of 1D and 2D simulations examines the effects of neutron-star wind and radioactive decay heating. In 1D, decay heating forms a low-density bubble that suppresses the reverse shock. While in 2D, the heating is localized to metal-rich pockets, inflating them and compressing the surrounding material into dense shells. In 3D, the neutron-star wind and decay heating modify the plume morphology, producing more large-scale structures. The extended plume morphology leads to an asymmetrical shock breakout. After breakout, the leading plumes cannot keep up with the shock front, resulting in deceleration and fragmentation by the reverse shock while retaining the large-scale asymmetry. The projected ejecta morphology and velocities are strongly viewing angle dependent. The relatively uniform metal-rich distribution does not resemble the strongly inhomogeneous ejecta structure of Cas A. The 160-isotope decay network shows that 24.4% of the radioactive heating comes from decay chains other than the canonical 56Ni chain. The low explosion energy, low 56Ni yield, and Ni/Fe ratio greater than unity suggest an observational signature similar to an electron capture supernova.

Neopane, Sudarshan [University of Tennessee (UT)]↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

From electricity to fuels: Descriptors for C 1 selectivity in electrochemical CO 2 reduction

Electrochemical reduction of carbon dioxide (CO 2 RR) over transition metals follows a complex reaction network. Here, we combine observations from experimental literature with a theoretical analysis of energetics to rationalize that not all intermediates in the reduction of CO 2 are formed through direct protonation steps. We derive a selectivity map for two-electron products (carbon monoxide (CO) and formate) on pure metal surfaces using only the CO and OH binding energies as descriptors. For the pure metals that are selective towards CO formation, the variation of the CO binding energy is sufficient to further subdivide the map into domains that predominantly form H 2 , CO, and more reduced products. Our analysis rationalizes experimentally observed product distributions in CO 2 RR across pure metal systems. Overall, we highlight the need for additional material screening descriptors for CO 2 R and the importance of considering competition from the elementary steps of the hydrogen evolution reaction.

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

The developments in modifying functionality of lignin and its application in biocomposites

With the advancement of sustainable material innovations, renewable natural biopolymers are gradually replacing traditional metal and petroleum-based synthetic materials due to their environmental friendliness, biodegradability, and economic advantages. Lignin, the second most abundant natural aromatic polymer in the plant kingdom, has emerged as a key candidate raw material for the development of green polymer systems because of its unique phenylpropane unit network structure, high carbon content, and rich functional group characteristics. However, challenges such as the inherent structural complexity, chemical inertness, and uneven molecular weight distribution of lignin limit its direct application. By employing modification strategies such as chemical functionalization and physical regulation, researchers can precisely control its reactivity, thermal stability, and interfacial compatibility, enabling the preparation of high-performance lignin-based functional composites. Here, this paper systematically reviews the principles and methodological advancements in lignin's multi-dimensional modification technology. It analyzes the mechanisms by which various chemical and physical modification techniques enhance the mechanical properties, functional responsiveness, and environmental adaptability of materials, and discusses innovative applications in fields such as intelligent packaging, biomedical materials, and energy storage devices. Furthermore, this review addresses the key challenges encountered in the high-value transformation of lignin, with the aim of offering a theoretical framework and technical pathway for the transformative development of lignin from agricultural and forestry by-products to functional material platforms.

Functional composites↗
DOE OSTI2022

Hydrogen Blending into Natural Gas Pipeline Infrastructure: Review of the State of Technology

Hydrogen is an energy carrier that could play an important role in reducing emissions associated with difficult-to-decarbonize sectors including peaking and load-following electricity and industrial heating. Blending hydrogen into the natural gas pipelines has been proposed as an approach for achieving near-term emissions reductions and early-market access for hydrogen technologies such as electrolyzers. Numerous challenges and uncertainties complicate this approach to natural gas decarbonization, however, and this review summarizes current research on the material, economic, and operational factors that must be considered for hydrogen blending. First, this review explores previous research regarding the effects of blending hydrogen on gas mixture fluid and thermodynamic properties, pipeline materials and equipment performance within transmission and distribution networks, and supporting facilities such as underground storage and end-use hydrogen separation. We also investigate and summarize studies that developed mathematical models of natural gas pipeline networks with hydrogen blending, and the operational and techno-economic findings of these network studies. Finally, we discuss notable hydrogen blending demonstrations and their key outcomes. Many blending demonstrations internationally have proven that low hydrogen percentage blending is feasible under very specific scenarios with limited end-usage applications on both high-pressure transmission lines and low-pressure distribution lines. This report summarizes findings from literature into key areas of consensus and disagreement. Research gaps and disagreements between the literature are highlighted to provide directions for future hydrogen blending research.

03 NATURAL GAS↗