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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Molecular Origin of Nanoscale Anion Ordering of LiTFSI Electrolytes Revealed through SAXS/WAXS and Molecular Dynamics Simulations

Recent developments in “water-in-salt” electrolytes have precipitated a renewed effort to study imide-based electrolytes. While previous small-/wide-angle X-ray scattering (SAXS/WAXS) studies have attributed the emergence of a low-Q peak in the SAXS profile of aqueous LiTFSI electrolytes to nanometer-scale anion clustering, a molecular-level understanding of the root of these clusters remains unclear. Here, in this study, we combined molecular dynamics simulations and SAXS/WAXS to study the solvation structures of LiTFSI in acetonitrile, methanol, and water. We concluded that hydrogen bonding in water and MeOH stabilizes anion clusters, while nonpolar methyl groups on methanol and acetonitrile interrupt the nanoscale ordering of TFSI anions. This causes LiTFSI in water and MeOH electrolytes to exhibit two low-Q SAXS profile peaks while LiTFSI in acetonitrile exhibits only a single peak below Q = 1 Å –1 . These findings shed light on the underlying molecular origins of nanoscale anion clusters, which may help in the design of the next generation of electrolyte chemistries.

Trojanowski, Lucas [Univ. of Michigan, Ann Arbor, ↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Monte Carlo simulations of Cu/Ni–Si–Mn co-precipitation in duplex stainless steels

First-Passage Kinetic Monte Carlo (FPKMC) simulations of species migration in duplex stainless steels were performed in order to establish relationships between alloy content, segregation behavior, and the formation of Ni-Si-Mn rich particles in cast duplex stainless steels during thermal aging. The Ni-Si-Mn-rich second phase forms after extended aging at reactor operating temperatures and degrades alloy properties. Simulations of Ni-Si-Mn cluster formation were validated through comparison with experimental results obtained through Atom Probe Tomography (APT) on similar alloy compositions, identifying several trends. First, Cu promotes the formation of Ni-Si-Mn clusters, but only when Ni or Mn prefer segregation to the surface of Cu particles; without the segregation of these species, the critical composition for the clusters to form was not achieved. Second, the width of precipitate-denuded zones near ?/d interfaces increases with decreasing Cu content. This finding was in strong agreement with APT results, further validating our model. Finally, our model predicts that Ni-Si-Mn cluster formation will be the most extensive when the Si:Mn ratio is approximately 1:1 and the least extensive when one of these key elements is less concentrated (Mn = 0.5 at.% or Si = 0.5 at.%). The implications of these results on how to improve alloy properties are discussed.

Cu co-precipitation, Atom probe tomography (APT), ↗
DOE OSTI2025

Development of an interatomic potential for the Ta–Li system

A new interatomic potential for the Ta–Li system is introduced to facilitate the study of phase stability, mechanical properties and non-equilibrium dynamics after Li implantation in Ta. Here, this potential is based on a generalization of the embedded atom method (GEAM) and includes contributions from embedding energy, explicit two- and three-body interactions, and nonlocal many-body interaction terms. The parameters of the potential are optimized using energies and atomic forces for a wide range of configurations obtained from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, including elastic constants, equations of state, phonon dispersion curves, point defect properties, and melting temperatures for different compositions. Although our potential is trained on a small dataset, its accuracy is comparable to that of available machine learning potentials for Li and Ta. Our simulations show that at temperatures below 500 K, Li atoms in Ta–Li alloys form clusters separated by Ta-rich domains, and we find no evidence of ordered phase formation. For Li concentrations below a few percent, Li atoms preferentially segregate to surfaces and grain boundaries. However, in alloys containing more than ~10% Li, the accumulation of Li in symmetric-tilt grain boundaries can lead to one of the following effects: formation of amorphous-like regions, changes in grain boundary structural units, or lateral movement of the grain boundary.

GEAM potential↗
DOE OSTI2025

The physical origin of heterogeneous solute clustering and nanoprecipitation at grain boundaries in ultrafine-grained immiscible alloys

Here, grain-boundary segregation effects on heterogeneous solute clustering and nanoprecipitation at low solute concentrations were investigated in sputter-deposited ultrafine-grained Ag-Cu alloy films. X-ray diffraction and scanning transmission electron microscopy revealed extended solubility of Cu in Ag matrix, accompanied by the formation of Cu-rich nanoprecipitates and solute clusters at grain boundaries and their junctions. Atomistic simulations further demonstrated that Cu solutes heterogeneously segregate to Ag GBs and form small clusters that grow into nuclei for Cu nanoprecipitates. These findings provide critical insights into the role of heterogeneous grain-boundary segregation in governing the phase separation pathways of immiscible nanocrystalline and ultrafine-grained alloys.

Heterogeneous segregation↗
DOE OSTI2023

High-Precision Calculation of Nanoparticle (Nanocrystal) Potentials of Mean Force and Internal Energies

Thermodynamic stability assessment of nanocrystal systems requires precise free energy calculations. This study highlights the importance of meticulous control over various factors, including the thermostat, time step, potential cutoff, initial configuration, sampling method, and overall simulation duration. Free energy computations in dry (solvent-free) systems are on the order of several hundred $k_BT$ but can be obtained with consistent accuracy. However, calculation of internal energies becomes challenging, as they are typically much larger in magnitude than free energies and exhibit significant noise and reduced reliability. To address this limitation, we propose a new internal energy estimate that drastically reduces the noise. Here, we also present formulas that enable the optimization of the parameters of the harmonic bias potential for optimal convergence. Finally, we discuss the implications of these findings for the computation of free energies in nanocrystal clusters and superlattices.

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

Machine-Learning-Based Analysis of the Guy-Greenbrier, Arkansas Earthquakes: A Tale of Two Sequences

Here, we revisited the June 2010 to October 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is composed of many small clusters that exhibit diffusion along the fault at shorter timescales. Our study demonstrates that machine-learning-based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.

58 GEOSCIENCES↗
DOE OSTI2024

Deciphering baryonic feedback with galaxy clusters

Abstract Upcoming cosmic shear analyses will precisely measure the cosmic matter distribution at low redshifts. At these redshifts, the matter distribution is affected by galaxy formation physics, primarily baryonic feedback from star formation and active galactic nuclei. Employing measurements from theMagneticumandIllustrisTNGsimulations and a dark matter + baryon (DMB) halo model, this paper demonstrates that Sunyaev-Zel'dovich (SZ) effect observations of galaxy clusters, whose masses have been calibrated using weak gravitational lensing, can constrain the baryonic impact on cosmic shear with statistical and systematic errors subdominant to the measurement errors of DES-Y3 and LSST-Y1, with systematic errors on S 8 and Ω m reaching 10% and 50% of the statistical errors, respectively. For LSST-Y6 and Roman surveys, these systematic errors increase to 150% and 100% of the statistical errors, indicating the necessity for further model developments for future surveys. We further dissect the contributions from different scales and halos with different masses to cosmic shear, highlighting the dominant role of SZ clusters at scales critical for cosmic shear analyses. These findings suggest a promising avenue for future joint analyses of Cosmic Microwave Background (CMB) and lensing surveys.

Astronomy & Astrophysics↗
DOE OSTI2025

Old neutron stars as a new probe of relic neutrinos and sterile neutrino dark matter

We study the kinetic cooling (heating) of old neutron stars due to coherent scattering with relic neutrinos (sterile neutrino dark matter) via Standard Model neutral-current interactions. We take into account several important physical effects, such as coherent enhancement, gravitational clustering, neutron degeneracy, Pauli blocking and weak potential. We find that the anomalous cooling of neutron stars due to relic neutrino scattering is difficult to observe. However, the anomalous heating of neutron stars due to coherent scattering with keV-scale sterile neutrino dark matter may be observed by current and future telescopes operating in the optical to near-infrared frequency band, such as the James Webb Space Telescope (JWST), which would probe hitherto unexplored parameter space in the sterile neutrino mass-mixing plane. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

MoS 2 -supported Au 31 for CO hydrogenation: A first-principle study

While the basal plane of single-layer molybdenum disulphide (MoS 2 ) is inert, attempts have been made to functionalize it chemically through the creation of defects and/or addition of dopants. With nanoparticles as dopants, the authors present density functional theory-based calculations of the hydrogenation of CO on a 31-atom, bilayer Au cluster supported on single-layer MoS 2 (Au31/MoS 2 ). Not surprisingly, the adsorption and reaction of all species involved in the hydrogenation occur at the edges of the cluster—the regions at which the interaction between MoS 2 and the Au cluster is tracked to be the strongest. The authors find two possible mechanisms that lead to the formation of methanol: (1) CO* → CHO* → CH 2 O* → CH 3 O* → CH 3 OH* and (2) CO* → CHO* → HCOH* → CH2OH* → CH3OH*, where * indicates the adsorbed species. Detailed analysis of the reaction pathways, however, does not result in favoring one mechanism over the other. Rather, both mechanisms are facile. In addition, the rate-limiting step in each mechanism is found to be the formation of the formyl radical group (CO* + H* → CHO*).

2D materials↗
DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗
DOE OSTI2021

Distant Relatives: The Chemical Homogeneity of Comoving Pairs Identified in Gaia

Comoving pairs, even at the separations of $\mathcal{O}$(10 6 ) are a predicted reservoir of conatal stars. Here we present detailed chemical abundances of 62 stars in 31 comoving pairs with separations of 10 2 –10 7 au and 3D velocity differences <2 km s -1 . This sample includes both bound comoving pairs/wide binaries and unbound comoving pairs. Observations were taken using the Magellan Inamori Kyocera Echelle (MIKE) spectrograph on board the Magellan/Clay Telescope at high resolution (R~ 45,000) with a typical signal-to-noise ratio of 150 pixel -1 . With these spectra, we measure surface abundances for 24 elements, including Li, C, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, Zr, Ba, La, Nd, and Eu. Taking iron as the representative element, our sample of wide binaries is chemically homogeneous at the level of 0.05 dex, which agrees with prior studies on wide binaries. Importantly, even systems at separations 2 × 10 5 –10 7 au are homogeneous to 0.09 dex, as opposed to the random pairs, which have a dispersion of 0.23 dex. Assuming a mixture model of the wide binaries and random pairs, we find that 73 ± 22% of the comoving pairs at separations 2 × 10 5 –10 7 au are conatal. Our results imply that a much larger parameter space of phase space may be used to find conatal stars, to study M-dwarfs, star cluster evolution, exoplanets, chemical tagging, and beyond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The mechanism behind the high radiation tolerance of Fe-Cr alloys

With the great demand for high radiation tolerant materials for advanced nuclear energy technologies, Fe-Cr alloys are at the forefront with long standing validated performance. Yet, the real mechanism behind their high radiation resistance is in question and understanding the effect of varying Cr percentage is a grand challenge limiting further improvements. Here we applied depth resolved atomic scale probe of defects to uncover the real mechanism on how Cr improves radiation resistance and explain the controversial impact of increasing Cr percentage. By combining depth-resolved positron annihilation lifetime spectroscopy and Doppler broadening spectroscopy we investigated the effect of Cr alloying on the formation and evolution of atomic size clusters induced by ion irradiation in Fe. We also used atom probe tomography to investigate the possible presence of Cr clusters or a’ phase with high Cr composition. The study reveals that the well-known resistance to radiation in Fe-Cr alloys arises from the stabilization of vacancy clusters around Cr atoms which act as sinks for radiation-induced defects. Thus, Cr atoms do not provide a direct sink for interstitials; rather defect complexes for that consist of Cr atoms and vacancies in turn act as sinks for irradiation-induced vacancies and interstitials. Most importantly, we find that lower amounts of Cr create smaller, uniformly distributed defect clusters that act as efficient sinks for radiation damage, but larger quantities of Cr form a defect structure that is less homogenous in size and spatial distribution, resulting in less efficient damage recombination. No evidence of phase a’ was found before or after irradiation, which indicates that it does not contribute to the observed radiation tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Elucidation of Bottom-up Growth of CaCO3 Involving Prenucleation Clusters from Structure Predictions and Decomposition of Globally Optimized (CaCO3)n Nanoclusters

Low-energy minima structures for (CaCO3)n, n = 28, are predicted using bottom-up genetic algorithms in conjunction with density functional theory electronic structure calculations, in comparison with the frozen and relaxed top-down clusters generated by cuts from the calcite, vaterite, and aragonite crystal structures. Similarities in structural motifs for the bottom-up and relaxed top-down are revealed using a fragment recognition technique. Fragment energy decomposition analysis shows that the bottom-up and relaxed top-down clusters belong to two classes of amorphous clusters with distinct intracluster energy distributions, despite their structural similarity. The bottom-up clusters with >20 formula units are surface stabilized with negative surface energy densities. In contrast, the top-down clusters are interior stabilized with positive surface energy densities. We prove that the sign of the surface energy density determines whether the nucleation reaction energy as a function of nuclear size has a maximum or a minimum. The surface-stabilized bottom-up clusters are proposed to be a type of prenucleation cluster at the minimum of the nucleation reaction energy. A mechanism for mineralization of CaCO3 involving prenucleation clusters and nonclassical growth pathway is proposed on the basis of our theoretical findings, which is consistent with previous titration experiments.

calcium carbonate, prenucleation cluster, biominer↗
DOE OSTI2022

Electrolyte clusters as hydrogen sponges: diffusion Monte Carlo simulations

Here, we carry out Diffusion Monte Carlo simulations of up to five hydrogen molecules aggregated with two Stockmayer clusters that solvate a single lithium ion. The first one contains six point dipole solvent particles with parameters tuned to emulate nitromethane. The second cluster is a relative large system investigated recently [G. DiEmma, S. Kalette, and E. Curotto, Chem. Phys. Lett. 2019, 725, 80–86]. In both cases we find that the aggregated hydrogen molecules perturb significantly the ground state of the host cluster and form a distorted tetrahedral cage around the Li + ion. The fifth hydrogen molecule is absorbed by the larger Stockmayer cluster while remaining in the proximity of the solvated charge.

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

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . III. Combined cosmological constraints

Here we present cosmological constraints from the analysis of two-point correlation functions between galaxy positions and galaxy lensing measured in Dark Energy Survey (DES) Year 3 data and measurements of cosmic microwave background (CMB) lensing from the South Pole Telescope (SPT) and Planck. When jointly analyzing the DES-only two-point functions and the DES cross-correlations with SPT+Planck CMB lensing, we find Ω m = 0.344 ± 0.030 and S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.773 ± 0.016, assuming ΛCDM. When additionally combining with measurements of the CMB lensing autospectrum, we find Ω m = 0.306$^{+0.018}_{-0.021}$ and S 8 = 0.792 ± 0.012. The high signal-to-noise of the CMB lensing cross-correlations enables several powerful consistency tests of these results, including comparisons with constraints derived from cross-correlations only, and comparisons designed to test the robustness of the galaxy lensing and clustering measurements from DES. Applying these tests to our measurements, we find no evidence of significant biases in the baseline cosmological constraints from the DES-only analyses or from the joint analyses with CMB lensing cross-correlations. However, the CMB lensing cross-correlations suggest possible problems with the correlation function measurements using alternative lens galaxy samples, in particular the redMaGiC galaxies and high-redshift MagLim galaxies, consistent with the findings of previous studies. We use the CMB lensing cross-correlations to identify directions for further investigating these problems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Chandra view of Abell 407: the central compact group of galaxies and the interaction between the radio AGN and the ICM

ABSTRACT Abell 407 (A407) is a unique galaxy cluster hosting a central compact group of nine galaxies (named as ‘Zwicky’s Nonet’; G1–G9 in this work) within a 30 kpc radius region. The cluster core also hosts a luminous radio active galactic nucleus (AGN), 4C 35.06 with helically twisted jets extending over 200 kpc. With a 44 ks Chandra observation of A407, we characterize the X-ray properties of its intracluster medium and central galaxies. The mean X-ray temperature of A407 is 2.7 keV and the M200 is $1.9 \times 10^{14}\, {\mathrm{M}_{\odot }}$. We suggest that A407 has a weak cool core at r < 60 kpc scales and at its very centre, <1–2 kpc radius, a small galaxy corona associated with the strong radio AGN. We also conclude that the AGN 4C 35.06 host galaxy is most likely G3. We suggest that the central group of galaxies is undergoing a ‘slow merge’ procedure. The range of the merging time-scale is 0.3 ∼ 2.3 Gyr and the stellar mass of the future brightest cluster galaxy (BCG) will be $7.4\times 10^{11} \, \mathrm{M}_{\odot }$. We find that the regions that overlap with the radio jets have higher temperature and metallicity. This is consistent with AGN feedback activity. The central entropy is higher than that for other clusters, which may be due to the AGN feedback and/or merging activity. With all these facts, we suggest that A407 is a unique and rare system in the local universe that could help us to understand the formation of a massive BCG.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Selection function of clusters in Dark Energy Survey year 3 data from cross-matching with South Pole Telescope detections

Context. Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. However, modeling the selection function of such samples is complicated by noncluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). Aims. We empirically constrained the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy Survey data with the redMaPPer algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). Methods. For matched clusters, we augmented the redMaPPer catalog with the SPT detection significance. For unmatched objects we used the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multiwavelength data, we explored various physically motivated models to describe the relationship between observed richness and halo mass. Results. Our analysis reveals a clear preference for models with an additional skewed scatter component associated with projection effects over a purely log-normal scatter model. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge that requires a combination of simulation and data-driven approaches.

79 ASTRONOMY AND ASTROPHYSICS↗