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All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Electron transport and space shielding handbook

The transport of electrons through material is discussed with emphasis on space radiation problems. Brief reviews of the electron radiation environment and electron transport properties are presented. Parametric representations of the electron transmitted fraction, energy spectrum, angular distribution, backscatter coefficient, and energy deposition are given as functions of electron energy and target material. Parametric equations for thick target bremsstrahlung are presented. Calculations of the transport of isotropically incident electrons relevant to space applications are shown. Comparisons of parametric representation with data, photon attenuation coefficients, electron energy deposition data, flux-to-dose conversion relations, and formalism applicable to space shielding problems are presented.

Shreve, D. C.↗
NASA NTRS1999

Quantitative Computer Tomography for Determining Composition of Microgravity and Ground Based Solid Solutions

Advances in x-ray Computer Tomography (CT) have been led by the medical profession, and by evaluation of industrial products, particularly castings. Porosity can readily be determined as a function of the density of a material, and CT is thus an industrially important NDE tool. Providing high purity, 100% dense standards of pure elements and compounds can be fabricated, the composition of solid solution alloys can be determined by measuring the CT number, which is a function of the absorption of the sample. Average densities across slices 1 mm thick can generally be determined to better than 1 percent. With present technology this spatial sensitivity is less than ideal, but important benefits can nevertheless be obtained by using CT, particularly single crystals, prior to making any destructive assault upon the sample. The sample can in fact be examined prior to removal from the mold within which it has been grown and, in the cases of microgravity flight samples, before removal from the cartridge assembly. This greatly assists the researcher in the characterization of the products, particularly as a guide to cutting and sampling. Examples of work with germanium-silicon alloys and mercury cadmium telluride taken with a radioactive cobalt source will be demonstrated.

Gillies, D. C.↗
NASA NTRS2012

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Climate change in tundra regions may alter vegetation species composition and ecosystem carbon balance. Remote sensing provides critical tools for monitoring these changes as optical signals provide a way to scale from plot measurements to regional patterns. Gas exchange measurements of pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow AK, show three significantly different values of light use efficiency (LUE) with values of 0.013+/-0.001, 0.0018+/-0.0002, and 0.0012 0.0001 mol C/mol absorbed quanta for vascular plants, mosses and lichens, respectively. Further, discriminant analysis of patch reflectance identifies five spectral bands that can separate each vegetation functional type as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. Patch-level statistical discriminant functions applied to in situ hyperspectral reflectance successfully unmixed cover fractions of the vegetation functional types. These functions, developed from the tram data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine regional variability in distribution of the vegetation functional types and from those distributions, the variability of LUE. Across the landscape, there was a fivefold variation in tundra LUE that was correlated to a spectral vegetation index developed to detect vegetation chlorophyll content.

Huemmrich, Karl F.↗
NASA NTRS1993

Surface compositional variations of Mo-47Re alloy as a function of temperature

Molybdenum-rhenium alloys are candidate materials for the National Aero-Space Plane (NASP) as well as for other applications in generic hypersonics. These materials are expected to be subjected to high-temperature (above 1200 C) casual hydrogen (below 50 torr), which could potentially degrade the material strength. Since the uptake of hydrogen may be controlled by the contaminant surface barriers, a study of Mo-47Re was conducted to examine the variations in surface composition as a function of temperature from 25 C to 1000 C. Pure molybdenum and rhenium were also examined and the results compared with those for the alloy. The analytical techniques employed were Auger electron spectroscopy, electron energy loss spectroscopy, ion scattering spectroscopy, and x ray photoelectron spectroscopy. The native surface was rich in metallic oxides that disappeared at elevated temperatures. As the temperature increased, the carbon and oxygen disappeared by 800 C and the surface was subsequently populated by the segregation of silicon, presumably from the grain boundaries. The alloy readily chemisorbed oxygen, which disappeared with heating. The disappearance temperature progressively increased for successive dosings. When the alloy was exposed to 800 torr of hydrogen at 900 C for 1 hour, no hydrogen interaction was observed.

Hoekje, S. J.↗
NASA NTRS1984

Stress decay in an orthotropic half-plane under self-equilibrating sinusoidal loading

An elastic orthotropic half-plane subjected to sinusoidal normal loading along an entire straight edge is analyzed. Stresses are calculated for material property combinations which are representative of some unidirectional fiber reinforced composites and of (+ or - 45) (subs) laminates made from the same unidirectional materials. Plots of the stresses as functions of the distance from the loaded boundary show that they can differ greatly from their counterparts in the isotropic half-plane under the same loading. How the results impact the question of the applicability of St. Venant's principle to orthotropic materials is briefly discussed.

Fichter, W. B.↗
NASA NTRS2011

Correlated Microanalysis of Cometary Organic Grains Returned by Stardust

Preliminary examination (PE) of samples returned from Comet 81P/Wild 2 by the NASA Stardust mission revealed a wide variety of carbonaceous samples [e.g. 1]. Carbonaceous matter is present as inclusions, rinds, and films in polyminerallic terminal particles [2-4], as carbon-rich particles along track walls [2, 5, 6], and as organic matter in aerogel around tracks [7, 8]. The organic chemistry of these samples ranges from purely aliphatic hydrocarbons to highly-aromatic material, often modified by various organic functional groups [2, 4, 5, 9-11]. Difficulty arises when interpreting the genesis of these carbonaceous samples, since contaminants could be introduced from the spacecraft [12], aerogel [1, 8], or during sample preparation. In addition, hypervelocity capture into aerogel may have heated cometary material in excess of 1000 C, which could have significantly altered the structure and chemistry of carbonaceous matter. Fortunately, much of this contamination or alteration can be identified through correlated microanalysis with transmission electron microscopy (TEM), scanning-transmission X-ray microscopy (STXM), and nanoscale secondary ion mass spectroscopy (SIMS).

DeGregorio, B. T.↗
NASA NTRS2013

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow, AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013 plus or minus 0.0002, 0.0018 plus or minus 0.0002, and 0.0012 plus or minus 0.0001 mol C mol (exp -1) absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Along the transect, area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. The patch-level statistical discriminant functions applied to in situ hyperspectral reflectance data collected along the transect successfully unmixed cover fractions of the vegetation functional types. The unmixing functions, developed from the transect data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine variability in distribution of the vegetation functional types for an area near Barrow, AK. Spatial variability of LUE was derived from the observed functional type distributions. Across this landscape, a fivefold variation in tundra LUE was observed. LUE calculated from the functional type cover fractions was also correlated to a spectral vegetation index developed to detect vegetation chlorophyll content. The concurrence of these alternate methods suggest that hyperspectral remote sensing can distinguish functionally distinct vegetation types and can be used to develop regional estimates of photosynthetic LUE in tundra landscapes.

VEGETATION↗
DOE OSTI2024

Digital Light Process 3D Printing of Magnetically Aligned Liquid Crystalline Elastomer Free–forms

Liquid crystalline elastomers (LCEs) are anisotropic soft materials capable of large dimensional changes when subjected to a stimulus. The magnitude and directionality of the stimuli-induced thermomechanical response is associated with the alignment of the LCE. Recent reports detail the preparation of LCEs by additive manufacturing (AM) techniques, predominately using direct ink write printing. Another AM technique, digital light process (DLP) 3D printing, has generated significant interest as it affords LCE free-forms with high fidelity and resolution. However, one challenge of printing LCEs using vat polymerization methods such as DLP is enforcing alignment. Here, we document the preparation of aligned, main-chain LCEs via DLP 3D printing using a 100 mT magnetic field. Systematic examination isolates the contribution of magnetic field strength, alignment time, and build layer thickness on the degree of orientation in 3D printed LCEs. Informed by this fundamental understanding, DLP is used to print complex LCE free-forms with through-thickness variation in both spatial orientations. Further, the hierarchical variation in spatial orientation within LCE free-forms is used to produce objects that exhibit mechanical instabilities upon heating. DLP printing of aligned LCEs opens new opportunities to fabricate stimuli-responsive materials in form factors optimized for functional use in soft robotics and energy absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Oxidation-resistant reflective surfaces for solar dynamic power generation in near Earth orbit

Reflective surfaces for space station power generation systems are required to withstand the atomic oxygen-dominated environment of near Earth orbit. Thin films of platinum and rhodium, which are corrosion resistant reflective metals, have been deposited by ion beam sputter deposition onto various substrate materials. Solar reflectances were then measured as a function of time of exposure to a RF-generated air plasma. Similarly, various protective coating materials, including MgF2, SiO2, Al2O3, and Si3N4, were deposited onto silver-coated substrates and then exposed to the plasma. Analysis of the films both before and after exposure by both ESCA and Auger spectroscopy was also performed. The results indicate that Pt and Rh do not suffer any loss in reflectance over the duration of the tests. Also, each of the coating materials survived the plasma environment. The ESCA and Auger analyses are discussed as well.

Gulino, D. A.↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
NASA NTRS1972

Development of high temperature fasteners using directionally solidified eutectic alloys

The suitability of the eutectics for high temperature fasteners was investigated. Material properties were determined as a function of temperature, and included shear parallel and perpendicular to the growth direction and torsion parallel to it. Techniques for fabricating typical fastener shapes included grinding, creep forming, and direct casting. Both lamellar Ni3Al-Ni3Nb and fibrous (Co,Cr,Al)-(Cr,Co)7C3 alloys showed promise as candidate materials for high temperature fastener applications. A brief evaluation of the performance of the best fabricated fastener design was made.

George, F. D.↗
DOE OSTI2024

Atomic structure of different surface terminations of polycrystalline ZnPd

The intermetallic compound ZnPd has been found to have desirable characteristics as a catalyst for the steam reforming of methanol. The understanding of the surface structure of ZnPd is important to optimize its catalytic behavior. However, due to the lack of bulk single-crystal samples and the complexity of characterizing surface properties in the available polycrystalline samples using common experimental techniques, all previous surface science studies of this compound have been performed on surface alloy samples formed through thin-film deposition. In this study, we present findings on the chemical and atomic structure of the surfaces of bulk polycrystalline ZnPd studied by a variety of complementary experimental techniques, including scanning tunneling microscopy (STM), x-ray photoelectron spectroscopy (XPS), low energy electron microscopy (LEEM), photoemission electron microscopy (PEEM), and microspot low-energy electron diffraction ( μ -LEED). These experimental techniques, combined with density functional theory (DFT)-based thermodynamic calculations of surface free energy and detachment kinetics at the step edges, confirm that surfaces terminated by atomic layers composed of both Zn and Pd atoms are more stable than those terminated by only Zn or Pd layers. DFT calculations also demonstrate that the primary contribution to the tunneling current arises from Pd atoms, in agreement with the STM results. The formation of intermetallics at surfaces may contribute to the superior catalyst properties of ZnPd over Zn or Pd elemental counterparts. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
NASA NTRS1977

Low-work-function surfaces produced by cesium carbonate decomposition

Cesium carbonate (Cs2CO3) was heated to the decomposition temperature of approximately 600 C. The nonvolatile decomposition products were condensed on a nickel substrate while the carbon dioxide was removed by pumping. The deposited material is characterized by an effective work function of between 1.05 and 1.15 eV at 450 K and by photoemission in the visible and near-infrared region of the spectrum. It is suggested that the deposited material consists of Cs2O, possibly Cs2O2, and adsorbed cesium. Silver, evaporated from a heated silver bead, produced the typical photoemissive and thermionic properties of a silver-oxygen-cesium (S-1) photocathode. The material may be of interest for thermionic energy converters and for the formation of silver-oxygen-cesium photocathodes.

Briere, T. R.↗
NASA NTRS1979

Surface temperatures and glassy state investigations in tribology, part 2

Measurements of lubricant shear rheological behavior in the amorphous solid region and near the liquid solid transition are reported. Elastic, plastic and viscous behavior was observed. The maximum yield shear stress (limiting shear stress) is a function of temperature and pressure and is believed to be the property which determines the maximum traction in elastohydrodynamic contacts such as traction drives. A shear rheological model based on primary laboratory data is proposed for concentrated contact lubrication. The model is Maxwell model modified with a limiting shear stress. Three material properties are required: low shear stress viscosity, limiting elastic shear modulus, and the limiting shear stress the material can withstand. All three are functions of temperature and pressure.

Bair, S. S.↗
NASA NTRS2006

Development of Design Analysis Methods for C/SiC Composite Structures

The stress-strain behavior at room temperature and at 1100 C (2000 F) was measured for two carbon-fiber-reinforced silicon carbide (C/SiC) composite materials: a two-dimensional plain-weave quasi-isotropic laminate and a three-dimensional angle-interlock woven composite. Micromechanics-based material models were developed for predicting the response properties of these two materials. The micromechanics based material models were calibrated by correlating the predicted material property values with the measured values. Four-point beam bending sub-element specimens were fabricated with these two fiber architectures and four-point bending tests were performed at room temperature and at 1100 C. Displacements and strains were measured at various locations along the beam and recorded as a function of load magnitude. The calibrated material models were used in concert with a nonlinear finite element solution to simulate the structural response of these two materials in the four-point beam bending tests. The structural response predicted by the nonlinear analysis method compares favorably with the measured response for both materials and for both test temperatures. Results show that the material models scale up fairly well from coupon to subcomponent level.

Sullivan, Roy M.↗