Search NASA⌕ Search

SEARCH · Search NASA

Results for “atom mapping”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accurate prediction of global-density-dependent range-separation parameters based on machine learning

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0−1 and only five systems (0.07%) with an absolute error larger than 0.01 a0−1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

Chemistry↗
DOE OSTI2025

Uncovering Structure–Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.

Naghshnejad, Pegah [Department of Chemical Enginee↗
DOE OSTI2020

Beam controlled nano-robotic device

A system and method (referred to as a method) to fabricate nanorobots. The method generates a pixel map of an atomic object and identifies portions of the atomic object that form a nanorobot. The method stores those identifications in a memory. The method adjusts an electron beam to a noninvasive operating level and images the portions of the atomic object that form the nanorobot. The method executes a plurality of scanning profiles by the electron beam to form the nanorobot and detects nanorobot characteristics and their surroundings via the electron beam in response to executing the plurality of scanning profiles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

How to correct relative voxel scale factors for calculations of vector-difference Fourier maps in cryo-EM

The atomic coordinates derived from cryo-electron microscopy (cryo-EM) maps can be inaccurate when the voxel scaling factors are not properly calibrated. Here, we describe a method for correcting relative voxel scaling factors between pairs of cryo-EM maps for the same or similar structures that are expanded or contracted relative to each other. We find that the correction of scaling factors reduces the amplitude differences of Fourier-inverted structure factors from voxel-rescaled maps by up to 20–30%, as shown by two cryo-EM maps of the SARS-CoV-2 spike protein measured at pH 4.0 and pH 8.0. This allows for the calculation of the difference map after properly scaling, revealing differences between the two structures for individual amino acid residues. Unexpectedly, the analysis uncovers two previously overlooked differences of amino acid residues in structures and their local structural changes. Furthermore, we demonstrate the method as applied to two cryo-EM maps of monomeric apo-photosystem II from the cyanobacteria Synechocystis sp. PCC 6803 and Thermosynechococcus elongatus. The resulting difference maps reveal many changes in the peripheral transmembrane PsbX subunit between the two species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Facet-dependent growth and dissolution of hematite resulting from autocatalytic interactions with Fe(II) and oxalic acid

The ability to simultaneously monitor the flux of iron atoms within the solution and solid phases can provide considerable insight into mechanisms of iron oxide mineral transformations. The autocatalytic interaction between hematite and Fe(II)-oxalate has long been of interest for its environmental and industrial relevance. In this study we take advantage of iron isotopic labelling and mass-sensitive imaging at the single particle scale to determine how changes in solution composition correlate with the morphologic evolution of faceted, micrometer-sized hematite platelets. Net dissolution is confirmed through analyses of aqueous iron chemistry, as well as by quantitative atomic force microscopy. Isotopic mapping techniques show that Fe(II) readily adsorbs to (001) and (012) surfaces in the absence of oxalate, but when oxalate is present selective dissolution of the (001) surface prevails and 57Fe deposition via recrystallization is not observed. Comparison between particle microtopographies following reaction with Fe(II), oxalate, and Fe(II)-oxalate show substantially different behavior, consistent with distinct mechanisms of interaction with hematite surfaces. The extensive characterization conducted on the coupled solution/solid dynamics in this system provides new insight for distinguishing crystal growth, dissolution, and recrystallization processes.

Taylor, Sandra D. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

Revealing the three-dimensional arrangement of polar topology in nanoparticles

In the early 2000s, low dimensional ferroelectric systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and elastic distortion energies. However, the measurement and classification of topological textures for general ferroelectric nanostructures have remained elusive, as it requires mapping the local polarization at the atomic scale in three dimensions. Here we unveil topological polar structures in ferroelectric BaTiO3 nanoparticles via atomic electron tomography, which enables us to reconstruct the full three-dimensional arrangement of cation atoms at an individual atom level. Our three-dimensional polarization maps reveal clear topological orderings, along with evidence of size-dependent topological transitions from a single vortex structure to multiple vortices, consistent with theoretical predictions. The discovery of the predicted topological polar ordering in nanoscale ferroelectrics, independent of epitaxial strain, widens the research perspective and offers potential for practical applications utilizing contact-free switchable toroidal moments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Mutation of active site glutamate in serine hydroxymethyltransferase allows trapping a reactive intermediate: a combined neutron and X-ray crystallography study

Serine hydroxymethyltransferase (SHMT) is a pyridoxal-5′-phosphate (PLP) dependent enzyme that catalyzes a chemical transformation essential for the one-carbon (1C) metabolism. SHMT reversibly converts L-Ser into Gly and transfers a 1C unit to tetrahydrofolate (THF) to give 5,10-methylene-THF (5,10-MTHF). 5,10-MTHF, a 1C-unit donor, plays a crucial role in the downstream biomolecular syntheses required for the cell homeostasis and proliferation. SHMT is a prominent target for the drug discovery to battle bacterial and parasitic infections, and to treat various types of cancer. SHMT-catalyzed chemistry is governed by the general acid-base catalysis. Knowledge of the catalytic mechanism can aid drug design but can only be achieved when the atomic details of each reaction step are mapped, including accurate determination of hydrogen atom positions. Here we utilized the inactive E53Q mutant of Thermus thermophilus ( Tth ) SHMT to directly determine protonation states with room-temperature neutron crystallography and to capture a reactive intermediate containing the PLP-L-Ser external aldimine and THF in the enzyme active site. We observed protonation of the Schiff base nitrogen (N SB ) in the PLP internal aldimine but no change in the protonation states of other ionizable PLP groups and active site residues compared to wild-type Tth SHMT. X-ray structural analysis of the ternary intermediate complex E53Q-Ser-THF that eluded previous structural characterization shows the strategic positioning of the E53Q side chain in close proximity to the external aldimine and THF and reinforces the proposed role for E53 as the driver of proton transfer events along the reaction pathway.

Drago, Victoria N. [Oak Ridge National Laboratory ↗
DOE OSTI2023

Advanced silicon tracking detector developments for the future Electron-Ion Collider

The proposed Electron-Ion Collider (EIC) will operate high-luminosity high-energy electron+proton and electron+nucleus collisions at the collision energies from 20 GeV to 141 GeV to solve several fundamental questions in the high energy and nuclear physics fields. Its instantaneous luminosity can reach 10 33-34 cm -2 s -1 and the bunching crossing rate is around 10 ns. The EIC project has received CD1 approval from the US DOE in 2021 and moves toward the machine design and preparation for construction. To realize various particle measurements with high precision at the future EIC, a low material-budget and high-granularity silicon vertex and tracking detector with fine spatial and momentum resolutions and nearly 4π solid angle coverage is desired. The Monolithic Active Pixel Sensor (MAPS) and AC Coupled Low Gain Avalanche Diode (AC-LGAD) technologies stand out of several advanced technology options for the EIC silicon vertex and tracking detector subsystems. The MAPS technology has advanced features of low material budget, low power consumption, good radiation resistance and fine spatial resolution. The AC-LGAD technology can achieve fast timing resolution. Latest studies and progress of the EIC silicon vertex and tracking detector conceptual design, performance validations in simulation and ongoing MAPS and AC-LGAD R&D will be shown. Furthermore, schedule and plan of the EIC project detector development will be discussed as well.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Exploring Local Vibrational Structure in Protein-Bound Chlorophyll a: Isotope-Enrichment Experiments and Electrostatic Analysis

Local chlorophyll (Chl) vibrations play an essential role in biological photosynthesis by facilitating rapid energy transfer between pigments. In research studies, they also provide a useful spectroscopic probe of the local protein environment that surrounds each pigment. However, measuring the complete vibrational absorption spectrum of a protein-bound Chl molecule is much more difficult than, for example, Chl in neat solvent due to overlap with protein vibrations that typically drown out Chl vibrational features. Resonance Raman and fluorescence spectroscopies provide a way around this problem for Franck–Condon active vibrations, but these often rely on cryogenic measurement conditions and fail to capture vibrational signatures from, e.g., ester-group vibrations that lack coupling to a convenient electronic transition. In the present contribution, we use 13 C-enrichment of the protein backbone to shift protein background signals to lower frequency, providing a largely clean spectral window in which to study local Chl a C═O stretch modes. A room-temperature absorption spectrum for Chl a in the water-soluble chlorophyll protein (WSCP) of Lepidium virginicum is thus extracted as a difference between protein-plus-pigment and protein-only vibrational spectra. Excellent agreement in the molecular fingerprint region with the vibrational spectrum of Chl a in organic solvents confirms that the resulting spectrum represents the response of the protein-bound Chl a molecule. Furthermore, the ester group resonance is observed to shift in response to the S53P mutation that eliminates a 17 3 ester group hydrogen bond from the protein environment. Finally, we analyze these experimental results using MD-based electrostatic analysis, finding that electric-field mapping at the C atom of the ester group provides a satisfactory explanation for the observed frequency shifts between organic solvent and protein environment. MD analysis further suggests that a red-shifted ester peak observed experimentally for the S53P mutant results from solvation of the 17 3 Chl ester group due increase water penetration into the Chl-binding pocket relative to wild-type WSCP. Here, we anticipate that these results will prove useful both for benchmarking future simulation work and as a reference for interpreting Chl vibrational spectra as a probe of pigment–protein interactions.

14 SOLAR ENERGY↗
DOE OSTI2023

Smooth, homogeneous, high-purity Nb 3 Sn superconducting RF resonant cavity by seed-free electrochemical synthesis

Abstract Workbench-size particle accelerators, enabled by Nb 3 Sn-based superconducting radio-frequency (SRF) cavities, hold the potential of driving scientific discovery by offering a widely accessible and affordable source of high-energy electrons and x-rays. Thin-film Nb 3 Sn RF superconductors with high quality factors, high operation temperatures, and high-field potentials are critical for these devices. However, surface roughness, non-stoichiometry, and impurities in Nb 3 Sn deposited by conventional Sn-vapor diffusion prevent them from reaching their theoretical capabilities. Here we demonstrate a seed-free electrochemical synthesis that pushes the limit of chemical and physical properties in Nb 3 Sn. Utilization of electrochemical Sn pre-deposits reduces the roughness of converted Nb 3 Sn by five times compared to typical vapor-diffused Nb 3 Sn. Quantitative mappings using chemical and atomic probes confirm improved stoichiometry and minimized impurity concentrations in electrochemically synthesized Nb 3 Sn. We have successfully applied this Nb 3 Sn to the large-scale 1.3 GHz SRF cavity and demonstrated ultra-low BCS surface resistances at multiple operation temperatures, notably lower than vapor-diffused cavities. Our smooth, homogeneous, high-purity Nb 3 Sn provides the route toward high efficiency and high fields for SRF applications under helium-free cryogenic operations.

Physics↗
DOE OSTI2021

In-Operando Characterization of P-I-N Perovskite Solar Cell Under Reverse Bias

A solar module is often exposed to a variety of intensity and uniformity illumination conditions throughout a day. In the field, objects or dirt can block light from hitting the module creating a partial shading event. When a partial shading event occurs the module power point tracking system will typically push the shaded cells into reverse bias in order to avoid the current loss from the other series connected illuminated cells. When perovskite solar cells are operated in reverse bias, electrochemical reactions can occur, leading to both reversible and irreversible performance losses. Excess holes generated by reverse bias cause and push the oxidation reaction of halide ions from the lattice to neutral halogen interstitial. Small interstitial halogen atoms can diffuse across the device and potentially move from the perovskite layer and into the transport layers, shifting the fermi levels in the devices. In this investigation we utilize cross-sectional kelvin probe atomic force microscopy to map the band energetics of perovskite solar cells during reverse bias. We observe a 150 meV decrease in the work function of the C 60 . This leads to a decrease of 150 mV and 4 mA/cm 2 in V oc and in J sc respectively. Finally, we discuss methods to prevent this irreversible degradation after a perovskite device is held under reverse bias.

fullerene↗
DOE OSTI2025

Electron Energy-Loss Spectroscopy and Differential Phase Contrast Imaging with Active Decision in Multimodal Electron Microscopy: Isotopic detection at the atomic scale

Isotopic engineering provides a powerful route to control phonon behavior in crystalline solids, enabling fundamental studies of lattice dynamics and heat transport at the atomic scale. Here, we directly visualize isotope-dependent phonon propagation in epitaxial Cr 2 O 3 using aberration-corrected scanning transmission electron microscopy (STEM) combined with monochromated, high-energy-resolution electron energy-loss spectroscopy (EELS). Guided by ab initio phonon calculations, we demonstrate that optical phonon modes above 70 meV are predominantly oxygen-derived and exhibit measurable redshifts upon substitution of natural 16 O by enriched 18 O. Spatially resolved vibrational spectrum imaging reveals isotope-enriched tracer layers within Cr 2 O 3 thin films, correlating isotope concentration with phonon intensity variations and vibrational energy shifts. At the nanometer and atomic scales, vibrational EELS mapping uncovers coherent phonon propagation across isotopic interfaces, consistent with theoretical phonon density of states and dispersion relations. These results establish vibrational EELS as a quantitative probe for isotope-dependent phonon transport in materials, opening new possibilities for studying energy dissipation and lattice dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Toward Discovering the Structure and Dynamics of the Sun-Interstellar Medium System (Los Alamos LDRD Report)

We have taken major steps toward developing the tools necessary to explore with unprecedented resolution the plasma structure and dynamics of the Sun-interstellar medium (ISM) interaction region, known as the heliosheath. We have applied a rigorous imaging technique known as Generalized Ridge Regression (GRR) to construct statistically robust sky maps of hydrogen energetic neutral atoms (ENAs) emanating from this region and detected by the Los Alamos-led IBEX-Hi ENA imager [1] on NASA’s Interstellar Boundary Explorer (IBEX) mission [2]. Our methods go far beyond the map reconstruction process currently applied by the IBEX Science Operations Center (ISOC), opening up the possibility of new discovery science with the IBEX data set and positioning LANL for a lead science role for the upcoming Interstellar Mapping and Acceleration Probe (IMAP) mission [3] for which LANL is providing two key experiments. We have successfully demonstrated that the new maps are higher resolution and are capable of revealing structures that are not presently resolveable in standard ISOC maps

58 GEOSCIENCES↗
DOE OSTI2022

Mapping the effects of physical and chemical reduction parameters on local atomic distributions within bimetallic nanoparticles

Bimetallic nanoparticles prove advantageous over their monometallic counterparts due to the tunable, hybrid properties that result from combining different atomic species in a controlled way. The favorable optical and catalytic properties resulting from AgAu nanoparticle formation have been widely attributed to the existence of Ag–Au bonds, the maximization of which assumes the formation of a homogeneous alloy. Despite the importance of atomic scale structure in these systems, synthetic studies are typically not paired with structural characterization at the atomic scale. Herein, a comprehensive synthetic exploration of physical and chemical reduction parameters of resulting nanoparticle products is complemented with thorough X-ray characterization to probe how these parameters affect atomic scale alloy distributions within AgAu nanoparticles. Presented evidence shows Ag is substantially underincorporated into nanoparticle constructs compared with solution Ag : Au ratios regardless of precursor :reductant ratio or volume of reductant added. Both Ag and Au exhibit significant local clustering, with Ag distributed preferentially towards the nanoparticle surface. Most significantly, the results of this investigation suggest that reduction parameters alone can affect the local alloy distributions and homogeneity within bimetallic nanoparticles, even when the ratio of metallic precursors remains constant. Overall, this investigation presents the ability to control alloy distributions using kinetics and provides new considerations for optimizing synthetic methods to produce functional bimetallic nanoparticles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Nanoscale Mapping of Hydrogen Distribution in Nuclear Structural Materials Using Cryogenic Transfer Atom Probe Tomography

Analyzing hydrogen distribution in nuclear structural materials at sub-nanometer scale spatial resolution has been challenging by electron microscopy. Furthermore, methods such as thermal desorption spectroscopy only provides a bulk estimate of hydrogen concentration with no nanoscale spatial resolution. In the last decade, cryogenic transfer atom probe tomography (APT) has been emerging as an invaluable characterization technique for analyzing the nanoscale distribution of hydrogen isotopes in materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗