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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Atomic structure of an FeCrMoCBY metallic glass revealed by high energy x-ray diffraction

Amorphous bulk metallic glasses with the composition Fe 48 Cr 15 Mo 14 C 15 B 6 Y 2 have been of interest due to their special mechanical and electronic properties, including corrosion resistance, high yield-strength, large elasticity, catalytic performance, and soft ferromagnetism. Here, we apply a reverse Monte Carlo technique to unravel the atomic structure of these glasses. The pair-distribution functions for various atomic pairs are computed based on the high-energy x-ray diffraction data we have taken from an amorphous sample. Monte Carlo cycles are used to move the atomic positions until the model reproduces the experimental pair-distribution function. The resulting fitted model is consistent with our ab initio simulations of the metallic glass. Our study contributes to the understanding of functional properties of Fe-based bulk metallic glasses driven by disorder effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Probing the Atomic-Scale Structure of Amorphous Aluminum Oxide Grown by Atomic Layer Deposition

Atomic layer deposition (ALD) is a well-established technique for depositing nanoscale coatings with pristine control of film thickness and composition. The trimethylaluminum (TMA) and water (H 2 O) ALD chemistry is inarguably the most widely used and yet to date, we have little information about the atomic-scale structure of the amorphous aluminum oxide (AlO x ) formed by this chemistry. This lack of understanding hinders our ability to establish process-structure-property relationships and ultimately limits technological advancements employing AlOx made via ALD. In this work, we employ synchrotron high-energy X-ray diffraction (HE-XRD) coupled with pair distribution function (PDF) analysis to characterize the atomic structure of amorphous AlO x ALD coatings. We combine ex situ and in operando HE-XRD measurements on ALD AlO x and fit these experimental data using stochastic structural modeling to reveal variations in the Al-O bond length, Al and O coordination environment, and extent of Al vacancies as a function of growth conditions. In particular, the local atomic structure of ALD AlO x is found to change with the substrate and number of ALD cycles. The observed trends are consistent with the formation of bulk Al 2 O 3 surrounded by an O-rich surface layer. We deconvolute these data to reveal atomic-scale structural information for both the bulk and surface phases. Overall, this work demonstrates the usefulness of HE-XRD and PDF analysis in improving our understanding of the structure of amorphous ALD thin films and provides a pathway to evaluate how process changes impact the structure and properties of ALD films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The AME 2020 atomic mass evaluation (II). Tables, graphs and references*

Abstract This is the second part of the new evaluation of atomic masses, AME2020. Using least-squares adjustments to all evaluated and accepted experimental data, described in Part I, we derived tables with numerical values and graphs which supersede those given in AME2016. The first table presents the recommended atomic mass values and their uncertainties. It is followed by a table of the influences of data on primary nuclides, a table of various reaction and decay energies, and finally, a series of graphs of separation and decay energies. The last section of this paper provides all input data references that were used in the AME2020 and the NUBASE2020 evaluations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2023

High-resolution laser system for the S 3 -Low Energy Branch

Here, in this paper we present the first high-resolution laser spectroscopy results obtained at the GISELE laser laboratory of the GANIL-SPIRAL2 facility, in preparation for the first experiments with the S 3 -Low Energy Branch. Studies of neutron-deficient radioactive isotopes of erbium and tin represent the first physics cases to be studied at S 3 . The measured isotope-shift and hyperfine structure data are presented for stable isotopes of these elements. The erbium isotopes were studied using the 4f 12 6s 2 3 H 6 → 4f 12 ( 3 H)6s6p J = 5 atomic transition (415 nm) and the tin isotopes were studied by the 5s 2 5p 2 ( 3 P 0 ) → 5s 2 5p6s( 3 P 1 ) atomic transition (286.4 nm), and are used as a benchmark of the laser setup. Additionally, the tin isotopes were studied by the 5s 2 5p6s( 3 P 1 ) → 5s 2 5p6p( 3 P 2 ) atomic transition (811.6 nm), for which new isotope-shift data was obtained and the corresponding field-shift F 812 and mass-shift M 812 factors are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Lattice dynamics of 119 Sn impurity in a bcc-Cr crystal

The chromium crystal doped with 119 Sn isotope was studied using the nuclear resonance inelastic x-ray scattering and first principles calculations. The Sn partial phonon density of states (PDOS) was obtained for three temperatures that correspond to different magnetic states of Cr. At all temperatures, the energy spectrum consists of a broad band around 18 meV and a narrow peak at 43 meV. The additional peak around 39 meV is observed only in the magnetically ordered phases, indicating the influence of magnetic order in chromium on lattice dynamics. The partial PDOS calculated with the antiferromagnetic order on Cr atoms show a very good agreement with the experimental data. It is revealed that the high-energy peak is lying above the phonon spectra of the pure bcc-Cr crystal. These are the local modes with the increased energies due to a strongly reduced distance between Sn and the nearest-neighbor Cr atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

“Best” Iterative Coupled-Cluster Triples Model? More Evidence for 3CC

To follow up on the unexpectedly good performance of several coupled-cluster models with approximate inclusion of 3-body clusters we performed a more complete assessment of the 3CC method for accurate computational thermochemistry in the standard HEAT framework. New spin-integrated implementation of the 3CC method applicable to closed- and open-shell systems utilizes a new automated toolchain for derivation, optimization, and evaluation of operator algebra in many-body electronic structure. We found that with a double-ζ basis set the 3CC correlation energies and their atomization energy contributions are almost always more accurate (with respect to the CCSDTQ reference) than the CCSDT model as well as the standard CCSD(T) model. The mean absolute errors in cc-pVDZ {3CC, CCSDT, and CCSD(T)} electronic (per valence electron) and atomization energies relative to the CCSDTQ reference for the HEAT data set, were {24, 70, 122} μE h /e and {0.46, 2.00, 2.58} kJ/mol, respectively. The mean absolute errors in the complete-basis-set limit {3CC, CCSDT, and CCSD(T)} atomization energies relative to the HEAT model reference, were {0.52, 2.00, and 1.07} kJ/mol, The significant and systematic reduction of the error by the 3CC method and its lower cost than CCSDT suggests it as a viable candidate for post- CCSD(T) thermochemistry applications, as well as the preferred alternative to CCSDT in general.

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

Nano-infrared imaging of epitaxial graphene on SiC revealing doping and thickness inhomogeneities

Here, we report on a nano-infrared (IR) imaging and spectroscopy study of epitaxial graphene on silicon carbide (SiC) by using scattering-type scanning near-field optical microscopy (s-SNOM). With nano-IR imaging, we reveal in real space microscopic domains with distinct IR contrasts. By analyzing the nano-IR, atomic force microscopy, and scanning tunneling microscopy imaging data, we conclude that the imaged domains correspond to single-layer graphene, bilayer graphene (BLG), and higher-doped BLG. With nano-IR spectroscopy, we find that graphene can screen the SiC phonon resonance, and the screening is stronger at more conductive sample regions. Our work offers insights into the rich surface properties of epitaxial graphene and demonstrates s-SNOM as an efficient and effective tool in characterizing graphene and possibly other two-dimensional materials.

2D materials↗
DOE OSTI2023

The UCLA Cosmochemistry Database

Abstract The UCLA Cosmochemistry Database was initiated as part of a data-rescue and -storage project aimed at archiving a variety of cosmochemical data acquired at University of California, Los Angeles (UCLA). The data collection includes elemental compositions of extraterrestrial materials analyzed by UCLA cosmochemists over the last five decades. The analytical techniques include atomic absorption spectrometry (AAS) and neutron activation analysis (NAA) at UCLA. The data collection is stored on the Astromaterials Data System (Astromat). We provide both interactive tables and downloadable datasheets for users to access all data. The UCLA Cosmochemistry Database archives cosmochemical data that are essential tools for increasing our understanding of the nature and origin of extraterrestrial materials. Future studies can reference the data collection in the examination, analysis, and classification of newly acquired extraterrestrial samples.

Science & Technology - Other Topics↗
DOE OSTI2021

Spin-lattice dynamics of surface vs core magnetization in Fe nanoparticles

Magnetization of clusters is often simulated using atomistic spin dynamics for a fixed lattice. Coupled spinlattice dynamics simulations of the magnetization of nanoparticles have up to now neglected the change in the size of the atomic magnetic moments near surfaces. We show that the introduction of variable magnetic moments leads to a better description of experimental data for the magnetization of small Fe nanoparticles. To this end we divide atoms into a surface-near shell and a core with bulk properties. Here, it is demonstrated that both the magnitude of the shell magnetic moment and the exchange interactions need to be modified to obtain a fair representation of the experimental data. This allows for a reasonable description of the average magnetic moment versus cluster size, and also the cluster magnetization versus temperature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Nanoconfinement of Molecular Magnesium Borohydride Captured in a Bipyridine-Functionalized Metal-Organic Framework

The lower limit of metal hydride nanoconfinement is demonstrated through the coordination of a molecular hydride species to binding sites inside the pores of a metal-organic framework (MOF). Magnesium borohydride, which has a high hydrogen capacity, is incorporated into the pores of UiO-67bpy (Zr6O4(OH)4(bpydc)6 with bpydc2- = 2,2'-bipyridine-5,5'- dicarboxylate) by solvent impregnation. The MOF retained its long-range order, and transmission electron microscopy and elemental mapping confirmed the retention of the crystal morphology and revealed a homogeneous distribution of the hydride within the MOF host. Notably, the B-, N-, and Mg-edge XAS data confirm the coordination of Mg(II) to the N atoms of the chelating bipyridine groups. In situ 11B MAS NMR studies helped elucidate the reaction mechanism and revealed that complete hydrogen release from Mg(BH4)2 occurs as low as 200 °C. Sieverts and thermogravimetric measurements indicate an increase in the rate of hydrogen release, with the onset of hydrogen desorption as low as 120 °C, which is approximately 150 °C lower than that of the bulk material. Furthermore, density functional theory calculations support the improved dehydrogenation properties and confirm the drastically lower activation energy for B-H bond dissociation.

Nanoconfinement, Metal Hydrides, Metal-Organic Fra↗
DOE OSTI2024

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improved joint X-ray and neutron refinement procedure in Phenix

Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Observation of ionization trends in a laboratory photoionized plasma experiment at Z

We report experimental and modeling results for the charge state distribution of laboratory photoionized neon plasmas in the first systematic study over nearly an order of magnitude range of ionization parameter ξ ∝ F/N e . The range of ξ is achieved by flexibility in the experimental platform to adjust either the x-ray drive flux F at the sample or the electron number density N e or both. Experimental measurements of photoionized plasma conditions over such a range of parameters enable a stringent test of atomic kinetics models used within codes that are applied to photoionized plasmas in the laboratory and astrophysics. From experimental transmission data, ion areal densities are extracted by spectroscopic analysis that is independent of atomic kinetics modeling. The measurements reveal the net result of the competition between photon-driven ionization and electron-driven recombination atomic processes as a function of ξ as it affects the charge state distribution. Results from radiation-hydrodynamics modeling calculations with detailed inline atomic kinetics modeling are compared with the experimental results. There is good agreement in the mean charge and overall qualitative similarities in the trends observed with ξ but significant quantitative differences in the fractional populations of individual ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future

Abstract As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) “Resolution Revolution” made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.

Burley, Stephen K. (ORCID:0000000224879713)↗
DOE OSTI2020

Energetics and Structure of Ag–Water Clusters Formed in Mordenite

Zeolite-supported Ag 0 clusters have broad applications from catalysis to medicine, necessitating a mechanistic understanding of the formation of Ag 0 clusters in situ. Density functional theory (DFT) simulations have been performed on silver, water, and silver–water clusters in silica mordenite (Si-MOR), to identify the role of the confinement on the structure and energetics of Ag 0 cluster formation. The most favorable binding energy in the 12-membered ring (MR) pore of the Si-MOR is a 10–15-atom Ag 0 cluster. Computational pair distribution function (PDF) data indicates that the Ag 0 and Ag 0 –H 2 O clusters formed in vacuum versus in Si-MOR exhibit structural differences. Additionally, when the Ag 0 cluster is confined, the density decreases and the surface area increases, hypothesized to be due to the limiting geometry of the 12-MR main channel. An energetic drive toward formation of larger Ag 0 clusters was also identified, with hydrated silver atoms generating higher energy structures. In conclusion, this work identifies mechanistic and structural insight into the role of nanoconfinement on formation of Ag 0 clusters in mordenite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗