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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

ATOMIC DIFFUSION AND MIXING IN OLD STARS. III. ANALYSIS OF NGC 6397 STARS UNDER NEW CONSTRAINTS

We have previously reported on chemical abundance trends with evolutionary state in the globular cluster NGC 6397 discovered in analyses of spectra taken with FLAMES at the Very Large Telescope. Here, we reinvestigate the FLAMES-UVES sample of 18 stars, ranging from just above the turnoff point to the red giant branch below the bump. Inspired by new calibrations of the infrared flux method, we adopt a set of hotter temperature scales. Chemical abundances are determined for six elements (Li, Mg, Ca, Ti, Cr, and Fe). Signatures of cluster-internal pollution are identified and corrected for in the analysis of Mg. On the modified temperature scales, evolutionary trends in the abundances of Mg and Fe are found to be significant at the 2{sigma} and 3{sigma} levels, respectively. The detailed evolution of abundances for all six elements agrees with theoretical isochrones, calculated with effects of atomic diffusion and a weak to moderately strong efficiency of turbulent mixing. The age of these models is compatible with the external determination from the white dwarf cooling sequence. We find that the abundance analysis cannot be reconciled with the strong turbulent-mixing efficiency inferred elsewhere for halo field stars. A weak mixing efficiency reproduces observations best, indicating a diffusion-corrected primordial lithium abundance of log {epsilon}(Li) = 2.57 {+-} 0.10. At 1.2{sigma}, this value agrees well with Wilkinson Microwave Anisotropy Probe calibrated big bang nucleosynthesis predictions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Diselenophosphate-Induced Conversion of an Achiral [Cu 20 H 11 {S 2 P(O i Pr) 2 } 9 ] into a Chiral [Cu 20 H 11 {Se 2 P(O i Pr) 2 } 9 ] Polyhydrido Nanocluster

A polyhydrido copper nanocluster, [Cu 20 H 11 {Se 2 P(OiPr) 2 } 9 ] (2 H ), which exhibits an intrinsically chiral inorganic core of C-3 symmetry, was synthesized from achiral [Cu 20 H 11 {S 2 P(OiPr) 2 } 9 ] (1(H)) of C-3h symmetry by a ligand-exchange method. Likewise, the structure has a distorted cuboctahedral Cu-13 core, two triangular faces of which are capped along the C-3 axis, one by a Cu-6 cupola and the other by a single Cu atom. The Cu-20 framework is further stabilized by 9 diselenophosphate and 11 hydride ligands. The number of hydride, phosphorus, and selenium resonances and their splitting patterns in multinuclear NMR spectra of 2(H) indicate that the chiral Cu20H11 core retains its C-3 symmetry in solution. Moreover, the 11 hydride ligands were located by neutron diffraction experiments and shown to be capping (3)-H and interstitial (5)-H ligands (in square-pyramidal and trigonal-bipyramidal cavities), as supported by DFT calculations on [Cu 20 H 11 (Se 2 PH 2 ) 9 ] (2 H ') as a simplified model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Global teleconnections influencing large-scale drought in the United States using SVDI

Understanding recent large-scale drought patterns and the mechanisms producing extreme drought events is vital for future drought forecasts and understanding future drought risks. Increasingly, vapor pressure deficit (VPD) has been used as an important measure of evaporative demand and proxy for drought detection. In this study, VPD is used to calculate the new Standardized VPD Drought Index (SVDI) with NASA North American Land Data Assimilation System (NLDAS) data. Previous studies have shown that SVDI accurately identifies the timing and magnitude short-term droughts in the United States (U.S). In the present study, SVDI is now used to identify large-scale drought patterns between 1980 and 2021 and drought variability driven by selected global teleconnections originating in the Pacific and Atlantic Oceans. Spatial drought characteristics were extracted from SVDI using empirical orthogonal function (EOF) analysis. Then a k-means clustering algorithm was applied to both EOF principal components and primary teleconnections, including the El Nino-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) to identify drought events driven by the Pacific Ocean. Results show that the SVDI is useful in evaluating large-scale drought variability in the U.S. related to global teleconnections, and that mechanisms influencing summer drought patterns in the Western and Southwestern U.S. are driven by a tropical-extratropical interactions originating in the equatorial Pacific Ocean related to ENSO dynamics with interdecadal variability modulated by PDO. The large-scale droughts in the Central and Southern U.S., like those in 2011 and 2012, on the other hand, are driven by the North Pacific Ocean warm pool during a strong negative PDO, which subsequently influenced variability in the Bermuda-Azores High in the Atlantic Ocean. In summer 2011, the Bermuda-Azores High weakened, reducing the onshore winds and moisture transport along the eastern Gulf of Mexico and contributing to ongoing drought in the region. The Northern Pacific and Atlantic Ocean sea surface temperatures (SSTs) have increased between 1980 and 2021. In conclusion, as SSTs continue to rise in the Northern Pacific Ocean, one consequence of the coupled North Pacific warm pool and atmospheric dynamics, is to increase summer drought variability over a large region in the southern and midwestern U.S. under global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Influence of Y-Rich Compounds on High-Cycle Fatigue Performance of Y-Doped M951 Superalloy

The performance of Y-doped M951 superalloy under high-cycle fatigue was investigated experimentally to determine the effect of Y-rich compounds on this behavior. The selected testing temperatures were 700 and 900 °C, and the stress ratio was R = 0.1. The fatigue life was shown to decrease as the stress amplitude increased, according to a typical Whöler S-N diagram. Two types of crack initiation sites were observed on the fractured surfaces: microshrinkage clusters and Y-rich compounds. Analysis by TEM indicated that the Y-rich compounds consisted of Al{sub 2}Ni{sub 6}Y{sub 3} particles with BCC structure and Ni{sub 5}Y particles with hexagonal structure. Cracks had initiated and propagated along the interface between Al{sub 2}Ni{sub 6}Y{sub 3} phase and the matrix. These Y-rich compounds varied considerably in size, from 1 to 769 μm, and the large-size Y-rich compounds were shown to be the preferred site for fatigue crack origin. The ability of Y in the removal of sulfur was considered. And the small-size Y-S product from the reaction between Y and S was shown to exhibit no deleterious effect on the fatigue property of the M951 Ni-based superalloy.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Molecular dynamics simulation studies of the wild type and E92Q/N155H mutant of Elvitegravir-resistance HIV-1 integrase

Although Elvitegravir (EVG) is a newly developed antiretrovirals drug to treat the acquired immunodeficiency syndrome (AIDS), drug resistance has already been found in clinic, such as E92Q/N155H and Q148H/G140S. Several structural investigations have already been reported to reveal the molecular mechanism of the drug resistance. As full length crystal structure for HIV-1 integrase is still unsolved, we use in this paper the crystal structure of the full length prototype foamy virus (PFV) in complex with virus DNA and inhibitor Elvitegravir as a template to construct the wild type and E92Q/N155H mutant system of HIV-1 integrase. Molecular dynamic simulations was used to revel the binding mode and the drug resistance of the EVG ligand in E92Q/N155H. Several important interactions were discovered between the mutated residues and the residues in the active site of the E92Q/N155H double mutant pattern, and cross correlation and clustering methods were used for detailed analysis. The results from the MD simulation studies will be used to guide the experimental efforts of developing novel inhibitors against drug-resistant HIV integrase mutants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

We report that obtaining surrogate models for various types of materials has long been a goal of computational material scientists and physicists, as they allow the calculation of thermodynamic quantities and properties of interest far more efficiently than DFT. Many surrogate models, including interatomic potentials generated by machine learning algorthims and cluster expansions, rely on incorporating many-body interactions such as three-body or four-body interactions. When utilizing machine learning algorithms for interatomic potentials, it is common to start with two-body interactions and systematically include three- and four-body clusters to improve the fit, stopping when including the additional clusters does not measurably change the predictive power of the model. In cluster expansions for FCC and related lattices, it is often enough to include mostly two-body interactions and a handful of three-body interactions. However, it remains to be seen whether those will be enough in the case of lattices with a lower packing fraction, such as BCC.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Noise-aware optimization in nominally identical manufacturing and measuring systems for high-throughput parallel workflows

Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While manageable in small labs, such variability can escalate into serious risks at larger scales, such as architectural 3D printing, where noise may cause structural or economic failures. This contribution presents a noise-aware decision-making algorithm that quantifies and models device-specific noise profiles to manage variability adaptively. It uses distributional analysis and pairwise divergence metrics with clustering to choose between single-device and robust multi-device Bayesian optimization strategies. Unlike conventional methods that assume homogeneous devices or enforce generic robustness, the proposed framework explicitly determines whether shared optimization across devices is appropriate based on the degree of inter-device noise heterogeneity. This enables improved performance, reproducibility, and efficiency. An experimental case study involving three nominally identical 3D printers (same brand, model, and close serial numbers) demonstrates reduced redundancy, lower resource usage, and improved reliability, along with improved convergence stability and solution quality through the selection of the appropriate optimization strategy based on the degree of inter-device noise heterogeneity. Overall, this framework establishes a general approach for precision- and resource-aware optimization in scalable, automated experimental platforms, demonstrated here on a representative multi-device 3D printing case study.

Schenk, Christina↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cluster characterization in atom probe tomography: Machine learning using multiple summary functions

In this work, we develop a machine learning-based method to characterize intracluster concentration (ρ c ), background concentration (ρ b ), clustering radius (r̄), and radius dispersity (δ r ) in simulated atom probe tomography data using multiple spatial statistics summary functions to train a Bayesian regularized neural network. Here, we build upon previous work that utilized Ripley’s K-function by incorporating additional features from nearest-neighbor spatial statistics summary functions to better characterize concentration-based metrics. The addition of nearest-neighbor based features allows for highly accurate estimates of ρ c and ρ b , both with 90% of the predictions within 4.0% of the real value; the root-mean-square errors are reduced by 81.5% and 92.8% from predictions using only K-function based features, respectively. Additionally, including these nearest-neighbor based features improves the ability to differentiate between r̄ and δ r .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Expanding the genomic encyclopedia of Actinobacteria with 824 isolate reference genomes

The phylum Actinobacteria includes important human pathogens like Mycobacterium tuberculosis and Corynebacterium diphtheriae and renowned producers of secondary metabolites of commercial interest, yet only a small part of its diversity is represented by sequenced genomes. Here, we present 824 actinobacterial isolate genomes in the context of a phylum-wide analysis of 6,700 genomes including public isolates and metagenome-assembled genomes (MAGs). We estimate that only 30%–50% of projected actinobacterial phylogenetic diversity possesses genomic representation via isolates and MAGs. A comparison of gene functions reveals novel determinants of host-microbe interaction as well as environment-specific adaptations such as potential antimicrobial peptides. We identify plasmids and prophages across isolates and uncover extensive prophage diversity structured mainly by host taxonomy. Analysis of >80,000 biosynthetic gene clusters reveals that horizontal gene transfer and gene loss shape secondary metabolite repertoire across taxa. Our observations illustrate the essential role of and need for high-quality isolate genome sequences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Proton Dissociation and Delocalization under Stepwise Hydration of Zeolite HZSM-5

The protonation behavior of zeolite Brønsted acid sites (BAS) in the presence of water is important for the performance of these widely used catalysts. Despite extensive study, the number of water molecules necessary for deprotonation is not well understood, in large part because experiments have been unable to access this information. In this work, we report experimental evidence for full deprotonation of the BAS in the presence of two or more water molecules, with a deprotonation energy of 1.6 kcal/mol. Linear IR absorption and 2D IR spectra were measured over a wide range of controlled hydration levels from 0.5 to 8.0 equivalents of H 2 O/Al at a constant temperature. Distinct spectral signatures of the protonated BAS and excess proton are identified, and their hydration dependence is analyzed quantitatively. Using the experiment as a benchmark, ab initio molecular dynamics simulations are reported that reproduce the experimental trends in the protonation state and IR spectra. The proton charge position and delocalization are quantified in clusters of 1–8 H 2 O molecules using the recently developed rCEC method. This analysis provides insight into the proton structure in confined water clusters, showing that the excess charge remains relatively localized between two oxygen atoms across the hydration range.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Crystal structure of SgcJ, an NTF2-like superfamily protein involved in biosynthesis of the nine-membered enediyne antitumor antibiotic C-1027

Comparative analysis of the enediyne biosynthetic gene clusters revealed sets of conserved genes serving as outstanding candidates for the enediyne core. Here we report the crystal structures of SgcJ and its homologue NCS-Orf16, together with gene inactivation and site-directed mutagenesis studies, to gain insight into enediyne core biosynthesis. Gene inactivation in vivo establishes that SgcJ is required for C-1027 production in Streptomyces globisporus. SgcJ and NCS-Orf16 share a common structure with the nuclear transport factor 2-like superfamily of proteins, featuring a putative substrate binding or catalytic active site. Site-directed mutagenesis of the conserved residues lining this site allowed us to propose that SgcJ and its homologues may play a catalytic role in transforming the linear polyene intermediate, along with other enediyne polyketide synthase-associated enzymes, into an enzyme-sequestered enediyne core intermediate. In conclusion, these findings will help formulate hypotheses and design experiments to ascertain the function of SgcJ and its homologues in nine-membered enediyne core biosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Experimental evolution reveals nitrate tolerance mechanisms in Desulfovibrio vulgaris

Elevated nitrate in the environment inhibits sulfate reduction by important microorganisms of sulfate-reducing bacteria (SRB). Several SRB may respire nitrate to survive under elevated nitrate, but how SRB that lack nitrate reductase survive to elevated nitrate remains elusive. To understand nitrate adaptation mechanisms, we evolved 12 populations of a model SRB (i.e., Desulfovibrio vulgaris Hildenborough, DvH) under elevated NaNO 3 for 1000 generations, analyzed growth and acquired mutations, and linked their genotypes with phenotypes. Nitrate-evolved (EN) populations significantly (p < 0.05) increased nitrate tolerance, and whole-genome resequencing identified 119 new mutations in 44 genes of 12 EN populations, among which six functional gene groups were discovered with high mutation frequencies at the population level. In this work, we observed a high frequency of nonsense or frameshift mutations in nitrosative stress response genes (NSR: DVU2543, DVU2547, and DVU2548), nitrogen regulatory protein C family genes (NRC: DVU2394-2396, DVU2402, and DVU2405), and nitrate cluster (DVU0246-0249 and DVU0251). Mutagenesis analysis confirmed that loss-of-functions of NRC and NSR increased nitrate tolerance. Also, functional gene groups involved in fatty acid synthesis, iron regulation, and two-component system (LytR/LytS) known to be responsive to multiple stresses, had a high frequency of missense mutations. Additionally, mutations in those gene groups could increase nitrate tolerance through regulating energy metabolism, barring entry of nitrate into cells, altering cell membrane characteristics, or conferring growth advantages at the stationary phase. This study advances our understanding of nitrate tolerance mechanisms and has important implications for linking genotypes with phenotypes in DvH.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Predicting the chemical space of fungal polyketides by phylogeny-based bioinformatics analysis of polyketide synthase-nonribosomal peptide synthetase and its modification enzymes

Fungal polyketide synthase (PKS)–nonribosomal peptide synthetase (NRPS) hybrids are key enzymes for synthesizing structurally diverse hybrid natural products (NPs) with characteristic biological activities. Predicting their chemical space is of particular importance in the field of natural product chemistry. However, the unexplored programming rule of the PKS module has prevented prediction of its chemical structure based on amino acid sequences. Here, we conducted a phylogenetic analysis of 884 PKS–NRPS hybrids and a modification enzyme analysis of the corresponding biosynthetic gene cluster, revealing a hidden relationship between its genealogy and core structures. This unexpected result allowed us to predict 18 biosynthetic gene cluster (BGC) groups producing known carbon skeletons (number of BGCs; 489) and 11 uncharacterized BGC groups (171). The limited number of carbon skeletons suggests that fungi tend to select PK skeletons for survival during their evolution. The possible involvement of a horizontal gene transfer event leading to the diverse distribution of PKS–NRPS genes among fungal species is also proposed. This study provides insight into the chemical space of fungal PKs and the distribution of their biosynthetic gene clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

An energy decomposition analysis for intermolecular interactions from an absolutely localized molecular orbital reference at the coupled-cluster singles and doubles level

We propose a wave function-based method for the decomposition of intermolecular interaction energies into chemically-intuitive components, isolating both mean-field- and explicit correlation-level contributions. We begin by solving the locally-projected self-consistent field for molecular interactions equations for a molecular complex, obtaining an intramolecularly polarized reference of self-consistently optimized, absolutely-localized molecular orbitals (ALMOs), determined with the constraint that each fragment MO be composed only of atomic basis functions belonging to its own fragment. As explicit inter-electronic correlation is integral to an accurate description of weak forces underlying intermolecular interaction potentials, namely, coordinated fluctuations in weakly interacting electronic densities, we add dynamical correlation to the ALMO polarized reference at the coupled-cluster singles and doubles level, accounting for explicit dispersion and charge-transfer effects, which map naturally onto the cluster operator. We demonstrate the stability of energy components with basis set extension, follow the hydrogen bond-breaking coordinate in the C{sub s}-symmetry water dimer, decompose the interaction energies of dispersion-bound rare gas dimers and other van der Waals complexes, and examine charge transfer-dominated donor-acceptor interactions in borane adducts. We compare our results with high-level calculations and experiment when possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A NOVEL, FULLY AUTOMATED PIPELINE FOR PERIOD ESTIMATION IN THE EROS 2 DATA SET

We present a new method to discriminate periodic from nonperiodic irregularly sampled light curves. We introduce a periodic kernel and maximize a similarity measure derived from information theory to estimate the periods and a discriminator factor. We tested the method on a data set containing 100,000 synthetic periodic and nonperiodic light curves with various periods, amplitudes, and shapes generated using a multivariate generative model. We correctly identified periodic and nonperiodic light curves with a completeness of ∼90% and a precision of ∼95%, for light curves with a signal-to-noise ratio (S/N) larger than 0.5. We characterize the efficiency and reliability of the model using these synthetic light curves and apply the method on the EROS-2 data set. A crucial consideration is the speed at which the method can be executed. Using a hierarchical search and some simplification on the parameter search, we were able to analyze 32.8 million light curves in ∼18 hr on a cluster of GPGPUs. Using the sensitivity analysis on the synthetic data set, we infer that 0.42% of the sources in the LMC and 0.61% of the sources in the SMC show periodic behavior. The training set, catalogs, and source code are all available at http://timemachine.iic.harvard.edu.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Photo-z outlier self-calibration in weak lensing surveys

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. In this work, we show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state w0, but with a 65% degradation when both w0 and wa are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3× 2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3× 2 analyses. Despite contributing only ~ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to LCDM, is publicly available at \href{https://github.com/EmmanuelSchaan/LaSSI}{https://github.com/EmmanuelSchaan/LaSSI}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Cosmological constraints from a joint DESI DR1 Full-Shape and DR2 BAO

We present a cosmological analysis combining full-shape (FS) clustering measurements from the Dark Energy Spectroscopic Instrument (DESI) DR1 with baryon acoustic oscillation (BAO) measurements from DESI DR2. To achieve a robust combination that accounts for the correlation between the two data releases, we employ the ShapeFit compression method and estimate the joint covariance using EZmocks. This compressed approach inherently mitigates the prior volume effects that have previously dominated Bayesian constraints from DESI data with minimal external priors. Consequently, we obtain — for the first time within a Bayesian framework — reliable DESI-only constraints on extensions to ΛCDM using only a Big Bang Nucleosynthesis prior on the baryon density and a wide prior on the spectral index. In flat ΛCDM, we find Ω m = 0.3035 ± 0.0085, h = 0.6876 ± 0.0059, and σ 8 = 0.822 ± 0.034. For the w 0 w a CDM dynamical dark energy model, we measure w 0 = -0.49 ± 0.25 and w a = -1.52 ± 0.77, improving constraints by ∼ 30% relative to the analogous DR1 measurement and reducing the discrepancy with ΛCDM to 1.4σ when compared to BAO only analyses. We also report competitive limits on the sum of neutrino masses and spatial curvature. This work demonstrates that the ShapeFit compression provides a prior-robust and computationally efficient pathway to constrain beyond-ΛCDM physics with large-scale structure.

baryon acoustic oscillations↗