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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗
DOE OSTI2022

Chemical crystallography by serial femtosecond X-ray diffraction

Abstract Inorganic–organic hybrid materials represent a large share of newly reported structures, owing to their simple synthetic routes and customizable properties 1 . This proliferation has led to a characterization bottleneck: many hybrid materials are obligate microcrystals with low symmetry and severe radiation sensitivity, interfering with the standard techniques of single-crystal X-ray diffraction 2,3 and electron microdiffraction 4–11 . Here we demonstrate small-molecule serial femtosecond X-ray crystallography (smSFX) for the determination of material crystal structures from microcrystals. We subjected microcrystalline suspensions to X-ray free-electron laser radiation 12,13 and obtained thousands of randomly oriented diffraction patterns. We determined unit cells by aggregating spot-finding results into high-resolution powder diffractograms. After indexing the sparse serial patterns by a graph theory approach 14 , the resulting datasets can be solved and refined using standard tools for single-crystal diffraction data 15–17 . We describe the ab initio structure solutions of mithrene (AgSePh) 18–20 , thiorene (AgSPh) and tethrene (AgTePh), of which the latter two were previously unknown structures. In thiorene, we identify a geometric change in the silver–silver bonding network that is linked to its divergent optoelectronic properties 20 . We demonstrate that smSFX can be applied as a general technique for structure determination of beam-sensitive microcrystalline materials at near-ambient temperature and pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Observing the onset of pressure-driven K-shell delocalization

The gravitational pressure in many astrophysical objects exceeds one gigabar (one billion atmospheres), creating extreme conditions where the distance between nuclei approaches the size of the K shell. This close proximity modifies these tightly bound states and, above a certain pressure, drives them into a delocalized state. Both processes substantially affect the equation of state and radiation transport and, therefore, the structure and evolution of these objects. Still, our understanding of this transition is far from satisfactory and experimental data are sparse. Here, in this work, we report on experiments that create and diagnose matter at pressures exceeding three gigabars at the National Ignition Facility where 184 laser beams imploded a beryllium shell. Bright X-ray flashes enable precision radiography and X-ray Thomson scattering that reveal both the macroscopic conditions and the microscopic states. The data show clear signs of quantum-degenerate electrons in states reaching 30 times compression, and a temperature of around two million kelvins. At the most extreme conditions, we observe strongly reduced elastic scattering, which mainly originates from K-shell electrons. We attribute this reduction to the onset of delocalization of the remaining K-shell electron. With this interpretation, the ion charge inferred from the scattering data agrees well with ab initio simulations, but it is significantly higher than widely used analytical models predict.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Smooth trends in fermium charge radii and the impact of shell effects

The quantum-mechanical nuclear-shell structure determines the stability and limits of the existence of the heaviest nuclides with large proton numbers Z ≳ 100. Shell effects also affect the sizes and shapes of atomic nuclei, as shown by laser spectroscopy studies in lighter nuclides. However, experimental information on the charge radii and the nuclear moments of the heavy actinide elements, which link the heaviest naturally abundant nuclides with artificially produced superheavy elements, is sparse. Here we present laser spectroscopy measurements along the fermium (Z = 100) isotopic chain and an extension of data in the nobelium isotopic chain (Z = 102) across a key region. Multiple production schemes and different advanced techniques were applied to determine the isotope shifts in atomic transitions, from which changes in the nuclear mean-square charge radii were extracted. A range of nuclear models based on energy density functionals reproduce well the observed smooth evolution of the nuclear size. Both the remarkable consistency of model prediction and the similarity of predictions for different isotopes suggest a transition to a regime in which shell effects have a diminished effect on the size compared with lighter nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Computation-guided optimization of split protein systems

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. Furthermore, this new technology will streamline the generation and use of split protein systems for diverse applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor

Abstract The emerging field of material-based data science requires information-rich databases to generate useful results which are currently sparse in the stress engineering domain. To this end, this study uses the’materials-aware’ text-mining toolkit, ChemDataExtractor, to auto-generate databases of yield-strength and grain-size values by extracting such information from the literature. The precision of the extracted data is 83.0% for yield strength and 78.8% for grain size. The automatically-extracted data were organised into four databases: a Yield Strength, Grain Size, Engineering-Ready Yield Strength and Combined database. For further validation of the databases, the Combined database was used to plot the Hall-Petch relationship for, the alloy, AZ31, and similar results to the literature were found, demonstrating how one can make use of these automatically-extracted datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Year-round trace gas measurements in the central Arctic during the MOSAiC expedition

Despite the key role of the Arctic in the global Earth system, year-round in-situ atmospheric composition observations within the Arctic are sparse and mostly rely on measurements at ground-based coastal stations. Measurements of a suite of in-situ trace gases were performed in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These observations give a comprehensive picture of year-round near-surface atmospheric abundances of key greenhouse and trace gases, i.e., carbon dioxide, methane, nitrous oxide, ozone, carbon monoxide, dimethylsulfide, sulfur dioxide, elemental mercury, and selected volatile organic compounds (VOCs). Redundancy in certain measurements supported continuity and permitted cross-evaluation and validation of the data. This paper gives an overview of the trace gas measurements conducted during MOSAiC and highlights the high quality of the monitoring activities. In addition, in the case of redundant measurements, merged datasets are provided and recommended for further use by the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurements of aerosol microphysical and chemical properties in the central Arctic atmosphere during MOSAiC

The Arctic environment is transforming rapidly due to climate change. Aerosols’ abundance and physicochemical characteristics play a crucial, yet uncertain, role in these changes due to their influence on the surface energy budget through direct interaction with solar radiation and indirectly via cloud formation. Importantly, Arctic aerosol properties are also changing in response to climate change. Despite their importance, year-round measurements of their characteristics are sparse in the Arctic and often confined to lower latitudes at Arctic land-based stations and/or short high-latitude summertime campaigns. Here, we present unique aerosol microphysics and chemical composition datasets collected during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in the central Arctic. These datasets, which include aerosol particle number concentrations, size distributions, cloud condensation nuclei concentrations, fluorescent aerosol concentrations and properties, and aerosol bulk chemical composition (black carbon, sulfate, nitrate, ammonium, chloride, and organics) will serve to improve our understanding of high-Arctic aerosol processes, with relevance towards improved modelling of the future Arctic (and global) climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes

The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational method development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2020

Valence band electronic structure of the van der Waals ferromagnetic insulators: VI 3 and CrI 3

Ferromagnetic van der Waals (vdW) insulators are of great scientific interest for their promising applications in spintronics. It has been indicated that in the two materials within this class, CrI 3 and VI 3 , the magnetic ground state, the band gap, and the Fermi level could be manipulated by varying the layer thickness, strain or doping. To understand how these factors impact the properties, a detailed understanding of the electronic structure would be required. However, the experimental studies of the electronic structure of these materials are still very sparse. Here, we present the detailed electronic structure of CrI 3 and VI 3 measured by angle-resolved photoemission spectroscopy (ARPES). Our results show a band-gap of the order of 1 eV, sharply contrasting some theoretical predictions such as Dirac half-metallicity and metallic phases, indicating that the intra-atomic interaction parameter (U) and spin-orbit coupling (SOC) were not properly accounted for in the calculations. We also find significant differences in the electronic properties of these two materials, in spite of similarities in their crystal structure. In CrI 3 , the valence band maximum is dominated by the I 5p, whereas in VI 3 it is dominated by the V 3d derived states. Our results represent valuable input for further improvements in the theoretical modeling of these systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Effects of canopy midstory management and fuel moisture on wildfire behavior

Abstract Increasing trends in wildfire severity can partly be attributed to fire exclusion in the past century which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk, while restoring fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a simple fire line and propagating through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a dense midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Long-term trends in storm surge climate derived from an ensemble of global surge reconstructions

We address the challenge, due to sparse observational records, of investigating long-term changes in the storm surge climate globally. We use two centennial and three satellite-era daily storm surge time series from the Global Storm Surge Reconstructions (GSSR) database and assess trends in the magnitude and frequency of extreme storm surge events at 320 tide gauges across the globe from 1930, 1950, and 1980 to present. Before calculating trends, we perform change point analysis to identify and remove data where inhomogeneities in atmospheric reanalysis products could lead to spurious trends in the storm surge data. Even after removing unreliable data, the database still extends existing storm surge records by several decades for most of the tide gauges. Storm surges derived from the centennial 20CR and ERA-20C atmospheric reanalyses show consistently significant positive trends along the southern North Sea and the Kattegat Bay regions during the periods from 1930 and 1950 onwards and negative trends since 1980 period. When comparing all five storm surge reconstructions and observations for the overlapping 1980–2010 period we find overall good agreement, but distinct differences along some coastlines, such as the Bay of Biscay and Australia. We also assess changes in the frequency of extreme surges and find that the number of annual exceedances above the 95th percentile has increased since 1930 and 1950 in several regions such as Western Europe, Kattegat Bay, and the US East Coast.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

BLINK enables ultrafast tandem mass spectrometry cosine similarity scoring

Metabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. By bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations, BLINK is over 3000 times faster than MatchMS, a widely used loop-based alignment and scoring implementation. Using a similarity cutoff of 0.7, BLINK and MatchMS had practically equivalent identification agreement, and greater than 99% of their scores and matching ion counts were identical. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Correlative chemical and elemental nano-imaging of morphology and disorder at the nacre-prismatic region interface in Pinctada margaritifera

Abstract Understanding biomineralization relies on imaging chemically heterogeneous organic–inorganic interfaces across a hierarchy of spatial scales. Further, organic minority phases are often responsible for emergent inorganic structures from the atomic arrangement of different polymorphs, to nano- and micrometer crystal dimensions, up to meter size mollusk shells. The desired simultaneous chemical and elemental imaging to identify sparse organic moieties across a large field-of-view with nanometer spatial resolution has not yet been achieved. Here, we combine nanoscale secondary ion mass spectroscopy (NanoSIMS) with spectroscopic IR s -SNOM imaging for simultaneous chemical, molecular, and elemental nanoimaging. At the example of Pinctada margaritifera mollusk shells we identify and resolve ~ 50 nm interlamellar protein sheets periodically arranged in regular ~ 600 nm intervals. The striations typically appear ~ 15 µm from the nacre-prism boundary at the interface between disordered neonacre to mature nacre. Using the polymorph distinctive IR-vibrational carbonate resonance, the nacre and prismatic regions are consistently identified as aragonite ( $${\overline{\nu }}_{a}=860$$ ν ¯ a = 860 cm −1 ) and calcite ( $${\overline{\nu }}_{c}=880$$ ν ¯ c = 880 cm −1 ), respectively. We observe previously unreported morphological features including aragonite subdomains encapsulated in extensions of the prism-covering organic membrane and regions of irregular nacre tablet formation coincident with dispersed organics. We also identify a ~ 200 nm region in the incipient nacre region with less well-defined crystal structure and integrated organics. These results show with the identification of the interlamellar protein layer how correlative nano-IR chemical and NanoSIMS elemental imaging can help distinguish different models proposed for shell growth in particular, and how organic function may relate to inorganic structure in other biomineralized systems in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Community detection in hypergraphs via mutual information maximization

Abstract The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

97 MATHEMATICS AND COMPUTING↗