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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Replication Data for: Aggregated nanoparticles: Sample preparation and analysis by atom probe tomography

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

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

Data and Code for Atomic Scale Etching of Diamond: Insights from Molecular Dynamics Simulations

This work investigates the effects of argon ions, hydrogen atoms, and hydrogen ions on the diamond (100) surface using classical molecular dynamics simulations. The purpose of this investigation was to asses plasma processing techniques for applications in quantum device manufacturing. The simulations suggest that combining argon ion smoothing with selective, near threshold energy H removal of amorphous C could be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices. Results were found to differ significantly with interatomic potential, and an analysis of these differences was also carried out. Included in this repository are LAMMPS source files, input scripts, and plotting scripts required to reproduce the data. Also included are the output data required to make all the plots included in the associated publication.

Brenner↗
DOE OSTI2022

Replication Data for: Efficient and selective carbon-carbon coupling on coke-resistant PdAu single-atom alloys

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The value of archived data

The rapid progress of atomic and nuclear physics in the twentieth century altered the way scientific results are documented, preserved and disseminated. Boris Pritychenko discusses how atomic and nuclear data tables have become a central resource for the community.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Ultrafast method for scanning tunneling spectroscopy

A scanning tunneling microscope (STM) combines unique capabilities in imaging and spectroscopy with atomic precision, and it can obtain energy-resolved spectroscopic data with atomic resolution. In this paper, we utilize a recently proposed modification to the STM feedback control loop to acquire high quality d 2 I/dV 2 images. We have developed a constant differential conductance imaging method by closing the STM feedback loop with a high precision dI/dV measurement. In this mode, the tip’s vertical position is adjusted so as to keep the differential conductance constant during raster scanning of the surface. Based on this imaging mode, we propose a new technique to acquire fast and reliable scanning tunneling spectroscopy (STS) data simultaneously with the imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Accelerating Multiscale Materials Modeling with Machine Learning

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10 2 atoms) to the mesoscale (10 4 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2020

CI-MBPT line strengths and atomic probabilities for some transitions of neutral iodine

Iodine is a promising alternative to expensive xenon as a propellant for electric propulsion devices. Iodine optical spectroscopy is a valuable diagnostic tool for optimization of the iodine propellant. Recently, some measurements have been conducted with cells containing iodine plasma, and iodine atomic absorption at several wavelengths (911, 906, 206 and 1315 nm) was measured with a Ti:sapphire laser. Motivated by these experiments and future applications, here we have calculated line strengths and atomic probabilities for the transitions between different types of 5s 2 5p 5 , 5s 2 5p 4 6s, 5s 2 5p 4 6p and 5s 2 5p 4 5d states of atomic iodine, using configuration-interaction many-body perturbation theory. Since experimental transition data for atomic iodine are only available for a relatively small number of transitions, we also performed calculations for the bromine atom, to further validate our approach. We found good agreement between our theory and available experimental data for both elements in most cases. Therefore, we believe that our predicted line strengths and transition probabilities are quite reliable and will be useful for iodine spectroscopy.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗
NASA NTRS1997

Differentiating Between H and F or H and CN on C(III) or Si(III) Surfaces

A Sc tipped probe molecule yields a larger difference for the probe-H vs probe-F interaction energies than our previously studied, electron-rich pyridine (C5H5N) and (CH3)3PO probes. However, the electron-deficient Sc tipped probe does not eliminate the probe-nearest data neighbor interaction problem associated with the C(III) surface. The difference in the probe-H and probe-F interaction energies is smaller for Si(III) than C(III), making it more difficult to differentiate between these two atoms on Si(III). The larger lattice constant for Si(III) significantly reduces the data atom-data atom interaction energy as well as the probe-neighbor interaction energies. This means that the H/CN system which is not practical for C(III) due to the CN-CN repulsion, is possible for Si(III). The difference in the probe-H and probe-CN interaction energies is very large for the H/CN data storage system, making this the best system studied to date. This system could be used on C(III) if a hydrogen 'fence' is built around each data site, which corresponds to using only one quarter of the surface sites for data storage.

Bauschlicher, Charles W.↗
NASA NTRS1985

Refined potentials for rare gas atom adsorption on rare gas and alkali-halide surfaces

The utilization of models of interatomic potential for physical interaction to estimate the long range attractive potential for rare gases and ions is discussed. The long range attractive force is calculated in terms of the atomic dispersion properties. A data base of atomic dispersion parameters for rare gas atoms, alkali ion, and halogen ions is applied to the study of the repulsive core; the procedure for evaluating the repulsive core of ion interactions is described. The interaction of rare gas atoms on ideal rare gas solid and alkali-halide surfaces is analyzed; zero coverage absorption potentials are derived.

Wilson, J. W.↗
NASA NTRS2010

Ring Current Dynamics in Moderate and Strong Storms: Comparative Analysis of TWINS and IMAGE/HENA Data with the Comprehensive Ring Current Model

We present a comparative study of ring current dynamics during strong and moderate storms. The ring current during the strong storm is studied with IMAGE/HENA data near the solar cycle maximum in 2000. The ring current during the moderate storm is studied using energetic neutral atom (ENA) data from the Two Wide-Angle Imaging Neutral- Atom Spectrometers (TWINS) mission during the solar minimum in 2008. For both storms, the local time distributions of ENA emissions show signatures of postmidnight enhancement (PME) during the main phases. To model the ring current and ENA emissions, we use the Comprehensive Ring Current Model (CRCM). CRCM results show that the main-phase ring current pressure peaks in the premidnight-dusk sector, while the most intense CRCM-simulated ENA emissions show PME signatures. We analyze two factors to explain this difference: the dependence of charge-exchange cross section on energy and pitch angle distributions of ring current. We find that the IMF By effect (twisting of the convection pattern due to By) is not needed to form the PME. Additionally, the PME is more pronounced for the strong storm, although relative shielding and hence electric field skewing is well developed for both events.

Buzulukova, N.↗
DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗
DOE OSTI2022

Revealing latent pole and zone line information in atom probe detector maps using crystallographically correlated metrics

Poles and zone lines observed within atom probe field evaporation images are useful for a range of atom probe crystallography studies, including calibration of the reconstruction and crystallographic characterisation of microstructural features such as grain boundaries. However, this information is not always readily apparent. Techniques for plotting crystallographically correlated metrics contained within atom probe data to enhance pole and zone line contrast across the detector space are developed. This includes consideration of the electric field, molecular ions, lattice structure retained within the reconstruction, specific elemental species, the number of pulses between detection events, and the lateral distance between sequential detection events. These approaches are then applied to experimental atom probe tomography datasets on technically pure Al, nanocrystalline Al, highly doped Si, and additively manufactured Inconel 738, Haynes 282, and Ti–6Al–4V. The results facilitate the extension of atom probe crystallography studies to a broader range of crystalline datasets where crystallographic information is not readily apparent from existing methods, as well as a deeper understanding of field evaporation behaviour during an atom probe experiment.

47 OTHER INSTRUMENTATION↗
NASA NTRS1983

A global model of the neutral thermosphere in magnetic coordinates based on AE-C data

Molecular nitrogen, atomic oxygen, and helium densities obtained from the AE-C satellite are analyzed in magnetic latitude and magnetic local time coordinates and compared to OGO 6 data for various seasons and magnetic activity levels. A depletion region for atomic oxygen and helium with respect to molecular nitrogen at high magnetic latitudes in the postmidnight magnetic local time sector persists under both high and low solar activity conditions. A global model in magnetic coordinates, patterned after the mass spectrometer and incoherent scatter (MSIS) model, is developed for molecular nitrogen, atomic oxygen, and helium. It is shown to represent the data well, without any residual UT dependence, and with an accuracy comparable to that provided by the more complex MSIS model with longitude terms The advantage of using magnetic coordinates is that they are more directly related to the major energy inputs and momentum sources in the polar regions than are geographic coordinates and are more convenient to use in studies of high latitude energy deposition processes. This is important for comparison with theoretical models where the number of coordinates is limited.

Stehle, C. G.↗
DOE OSTI2023

Understanding grain boundary segregation under irradiation: A mesoscale modeling study

Grain boundaries (GBs) are an important component in the design of irradiation resistant materials as they play a crucial role as point defect sinks for non-equilibrium vacancies and self-interstitials. However, in the presence of sustained point defect fluxes, the coupled transport of atomic components with inherently different diffusivities often leads to radiation-induced segregation (RIS), with undesired effects on properties such as intergranular corrosion and stress-assisted cracking. In this talk, we investigate the fundamental mechanisms leading to GB segregation in BCC FeCr-based alloys using multiscale modeling and experiments. A phase-field model is employed to account for both thermodynamic and kinetic segregation mechanisms. The full Onsager transport matrix is derived using kinetic Monte Carlo, molecular dynamics, and kinetic cluster expansion (KineCluE) calculations. The thermodynamics of GB segregation is described via an atomic density-based modification to the CALPHAD free energy. The modeling predictions are then compared against segregation data from atom probe characterization for various temperatures, compositions, and neutron irradiation conditions. We will conclude by discussing the utility of the multiscale modeling approach to shed light on the fundamental mechanisms leading to solute-GB interaction.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evidence of Xe-incorporation in the bubble superlattice in irradiated U-Mo fuel

For years, researchers have reported competing ideas on the formation of fission gas bubble super lattices in metallic fuels, which was postulated to be comprised of elemental xenon (Xe) atoms. However, the chemical and physical arrangement of these elements within this gas bubble superlattice has not been verified. In this contribution, Xe was chemically profiled using atomic resolution scanning transmission electron microscopy (STEM) and atom probe tomography (APT) techniques in irradiated uranium-molybdenum (U-Mo) fuels. These complementary techniques provide conclusive evidence of Xe presence in bubble superlattice up to fission densities of 4.5×10 21 fissions/cm 3 and provide quantitative assessment of Xe content within the bubble superlattice. Based on the results of simultaneous imaging and spectroscopy using STEM, the chemical composition of the Xe bubble superlattice present in fission densities less than 4.5x10 21 fissions/cm 3 in U-Mo fuel was measured and found to be up to 8±1.3 atomic %. APT data complements STEM findings on Xe distribution in irradiated U-Mo fuel sample. APT showed a chemical composition of 0.7±0.1 atomic%. Finally, this study concludes by providing conclusive evidence that Xe is not only present within an atomically arranged bubble superlattice but provides fundamental insight the physical state of Xe in low enriched U-Mo monolithic fuel.

36 MATERIALS SCIENCE↗