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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Study of γ γ → γ ψ ( 2 S ) at Belle

Using 980 fb -1 of data at and around the Υ(nS)(n = 1, 2, 3, 4, 5) resonances collected with the Belle detector at the KEKB asymmetric-energy e+e- collider, the two-photon process γγ → γψ(2S) is studied from the threshold to 4.2 GeV for the first time. Two structures are seen in the invariant mass distribution of γψ(2S): one at M R 1 = 3922.4 ± 6.5 ± 2.0 MeV/c 2 with a width of Γ R 1 = 22 ± 17 ± 4 MeV, and another at M R 2 = 4014.3 ± 4.0 ± 1.5 MeV/c 2 with a width of Γ R 2 = 4 ± 11 ± 6 MeV; the signals are parametrized with the incoherent sum of two Breit- Wigner functions. The first structure is consistent with the X(3915) or the χ c2 (3930), and the local statistical significance is determined to be 3.1σ with the systematic uncertainties included. The second matches none of the known charmonium or charmonium like states, and its global significance is determined to be 2.8σ including the look-elsewhere effect. The production rates are Γ γγ B(R 1 → γψ(2S)) = 9.8 ± 3.6 ± 1.2 eV assuming (J PC , |λ|) = (0 ++ , 0) or 2.0 ± 0.7 ± 0.2 eV with (2 ++ , 2) for the first structure and Γ γγ B(R 2 → γψ(2S)) = 6.2 ± 2.2 ± 0.8 eV with (0 ++ , 0) or 1.2 ± 0.4 ± 0.2 eV with (2 ++ , 2) for the second one. Furthermore, the first errors are statistical and the second systematic, and λ is the helicity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Unbiased particle conformation extraction from scattering spectra using orthonormal basis expansions

A strategy is outlined for quantitatively evaluating the particle density profiles from small-angle scattering spectra of dilute solutions. The approach employs an orthonormal basis function expansion method, enabling the determination of characteristic mass distributions in self-assembled structures without the need for a specific structural model. Through computational benchmarking, the efficacy of this approach is validated by effectively reconstructing the density profile of soft-ball systems with varying fuzziness from their scattering signatures. Further, the feasibility of the method is demonstrated by fitting small-angle neutron scattering data obtained from Pluronic L64 micelles at different temperatures. This proposed approach is both simple and analytical, eliminating the requirement for a presumptive structural model in scattering analysis. The new method could therefore facilitate quantitative descriptions of complex nanoscopic structures inherent to numerous soft-matter systems using small-angle scattering techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-G-IeP4-13: PET Image Noise Variability and Its Consequences for Quantifying Tumor Hypoxia

Purpose: The values in a PET image which represent activity concentrations of a radioactive tracer are influenced by a large number of parameters including patient conditions as well as image acquisition and reconstruction. This work investigates noise characteristics in PET images for various image acquisition and image reconstruction parameters. Methods: Different phantoms with homogeneous activity distributions were scanned using several acquisition parameters and reconstructed with numerous sets of reconstruction parameters. Images from six PET scanners from different vendors were analyzed and compared with respect to quantitative noise characteristics. Local noise metrics, which give rise to a threshold value defining the metric of hypoxic fraction, as well as global noise measures in terms of noise power spectra (NPS) were computed. In addition to variability due to different reconstruction parameters, spatial variability of activity distribution and its noise metrics were investigated. Patient data from clinical trials were mapped onto phantom scans to explore the impact of the scanner’s intrinsic noise variability on quantitative clinical analysis. Results: Local noise metrics showed substantial variability up to an order of magnitude for different reconstruction parameters. Investigations of corresponding NPS revealed reconstruction dependent structural noise characteristics. For the acquisition parameters, noise metrics were guided by Poisson statistics. Large spatial non-uniformity of the noise was observed in both axial and radial direction of a PET image. In addition, activity concentrations in PET images of homogeneous phantom scans showed intriguing spatial fluctuations for most scanners. The clinical metric of the hypoxic fraction was shown to be considerably influenced by the PET scanner’s spatial noise characteristics. Conclusion: We showed that a hypoxic fraction metric based on noise characteristics requires careful consideration of the various dependencies in order to justify its quantitative validity. This work may result in recommendations for harmonizing QA of PET imaging for multi-institutional clinical trials.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Detection of defects in atomic-resolution images of materials using cycle analysis

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Transport Studies and Modeling in PEM Fuel Cells

This project’s aim was to develop fuel cell components (i.e. membranes, gas-diffusion media (GDM), bipolar plates and flow fields) that possess specific properties (i.e. water transport and conductivity). A computational fluid dynamics model was developed to elucidate the effect of certain parameters on these specific properties. Ultimately, the model will be used to determine sensitivity of fuel cell performance to component properties to determine limiting components and to guide research. We have successfully reached our objectives and achieved most of the milestones of this project. We have designed and synthesized a variety of hydrocarbon block polymer membranes with lower equivalent weight, structure, chemistry, phase separation and process conditions. These membranes provide a broad selection with optimized water transport properties. We have also designed and constructed a variety of devices that are capable of accurately measuring the water transport properties (water uptake, water diffusivity and electro-osmatic drag) of these membranes. These transport properties are correlated to the membranes’ structures derived from X-ray and microscopy techniques to determine the structure-property relationship. We successfully integrated hydrocarbon membrane MEAs with a current distribution board (CBD) to study the impact of hydrocarbon membrane on water transport in fuel cells. We have designed and fabricated various GDM with varying substrate, diffusivity and micro-porous layers (MPL) and characterized their pore structure, tortuosity and hydrophobicity. We have derived a universal chart (MacMullin number as function of wet proofing and porosity) that can be used to characterize various GDM. The abovementioned GDMs have been evaluated in operating fuel cells; their performance is correlated to various pore structure, tortuosity and hydrophobicity of the GDM. Unfortunately, determining a universal relationship between the MacMullin number and these properties was not achieved. We have simulated fuel cell performance, current distribution and water distribution at various values of the water uptake, membrane diffusivity, and electro-osmotic drag coefficient (EODC) and compared modeling results with segmented-cell data for both serpentine and parallel flow-fields. We have developed iterations of fuel cell flow fields to achieve specific water transport and thermal management targets. This work demonstrated the importance of membrane diffusivity on fuel cell performance, the necessity of a high membrane diffusion coefficient, and the desirability of a low EODC at low levels of relative humidity.

08 HYDROGEN↗
DOE OSTI2021

Preventing Failures By Dataset Shift Detection in Safety-Critical Graph Applications

Dataset shift refers to the problem where the input data distribution may change over time (e.g., between training and test stages). Since this can be a critical bottleneck in several safety-critical applications such as healthcare, drug-discovery, etc., dataset shift detection has become an important research issue in machine learning. Though several existing efforts have focused on image/video data, applications with graph-structured data have not received sufficient attention. Therefore, in this paper, we investigate the problem of detecting shifts in graph structured data through the lens of statistical hypothesis testing. Specifically, we propose a practical two-sample test based approach for shift detection in large-scale graph structured data. Our approach is very flexible in that it is suitable for both undirected and directed graphs, and eliminates the need for equal sample sizes. Using empirical studies, we demonstrate the effectiveness of the proposed test in detecting dataset shifts. We also corroborate these findings using real-world datasets, characterized by directed graphs and a large number of nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Faint dwarfs in nearby groups

The number and distribution of dwarf satellite galaxies remain a critical test of cold dark matter-dominated structure formation on small scales. Until recently, observational information about galaxy formation on these scales has been limited mainly to the Local Group. We have searched for faint analogues of Local Group dwarfs around nearby bright galaxies, using a spatial clustering analysis of the photometric catalog of the Sloan Digital Sky Survey (SDSS) Data Release 8. Several other recent searches of SDSS have detected clustered satellite populations down to Δm{sub r} ≡ (m{sub r,} {sub sat} – m{sub r,} {sub main}) ∼ 6-8, using photometric redshifts to reduce background contamination. SDSS photometric redshifts are relatively imprecise, however, for faint and nearby galaxies. Instead, we use angular size to select potential nearby dwarfs and consider only the nearest isolated bright galaxies as primaries. As a result, we are able to detect an excess clustering signal from companions down to Δm{sub r} = 12, 4 mag fainter than most recent studies. We detect an overdensity of objects at separations <400 kpc, corresponding to about 4.6 ± 0.5 satellites per central galaxy, consistent with the satellite abundance expected from the Local Group, given our selection function. Although the sample of satellites detected is incomplete by construction, since it excludes the least and most compact dwarfs, this detection provides a lower bound on the average satellite luminosity function, down to luminosities corresponding to the faintest ''classical'' dwarfs of the Local Group.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Geometric frustration in the myosin superlattice of vertebrate muscle

Geometric frustration results from an incompatibility between minimum energy arrangements and the geometry of a system, and gives rise to interesting and novel phenomena. Here, we report geometric frustration in a native biological macromolecular system---vertebrate muscle. We analyse the disorder in the myosin filament rotations in the myofibrils of vertebrate striated (skeletal and cardiac) muscle, as seen in thin-section electron micrographs, and show that the distribution of rotations corresponds to an archetypical geometrically frustrated system---the triangular Ising antiferromagnet. Spatial correlations are evident out to at least six lattice spacings. The results demonstrate that geometric frustration can drive the development of structure in complex biological systems, and may have implications for the nature of the actin--myosin interactions involved in muscle contraction. Identification of the distribution of myosin filament rotations with an Ising model allows the extensive results on the latter to be applied to this system. It shows how local interactions (between adjacent myosin filaments) can determine long-range order and, conversely, how observations of long-range order (such as patterns seen in electron micrographs) can be used to estimate the energetics of these local interactions. Furthermore, since diffraction by a disordered system is a function of the second-order statistics, the derived correlations allow more accurate diffraction calculations, which can aid in interpretation of X-ray diffraction data from muscle specimens for structural analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2018

nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure

We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Wide-field Hubble space telescope observations of the globular cluster system in NGC 1399

We present a comprehensive high spatial resolution imaging study of globular clusters (GCs) in NGC 1399, the central giant elliptical cD galaxy in the Fornax galaxy cluster, conducted with the Advanced Camera for Surveys (ACS) aboard the Hubble Space Telescope (HST). Using a novel technique to construct drizzled point-spread function libraries for HST/ACS data, we accurately determine the fidelity of GC structural parameter measurements from detailed artificial star cluster experiments and show the superior robustness of the GC half-light radius, r{sub h} , compared with other GC structural parameters, such as King core and tidal radius. The measurement of r{sub h} for the major fraction of the NGC 1399 GC system reveals a trend of increasing r{sub h} versus galactocentric distance, R {sub gal}, out to about 10 kpc and a flat relation beyond. This trend is very similar for blue and red GCs, which are found to have a mean size ratio of r {sub h,} {sub red}/r {sub h,} {sub blue} = 0.82 ± 0.11 at all galactocentric radii from the core regions of the galaxy out to ∼40 kpc. This suggests that the size difference between blue and red GCs is due to internal mechanisms related to the evolution of their constituent stellar populations. Modeling the mass density profile of NGC 1399 shows that additional external dynamical mechanisms are required to limit the GC size in the galaxy halo regions to r{sub h} ≈ 2 pc. We suggest that this may be realized by an exotic GC orbit distribution function, an extended dark matter halo, and/or tidal stress induced by the increased stochasticity in the dwarf halo substructure at larger galactocentric distances. We compare our results with the GC r{sub h} distribution functions in various galaxies and find that the fraction of extended GCs with r{sub h} ≥ 5 pc is systematically larger in late-type galaxies compared with GC systems in early-type galaxies. This is likely due to the dynamically more violent evolution of early-type galaxies. We match our GC r{sub h} measurements with radial velocity data from the literature and split the resulting sample at the median r{sub h} value into compact and extended GCs. We find that compact GCs show a significantly smaller line-of-sight velocity dispersion, (σ{sub cmp}) = 225 ± 25 km s{sup –1}, than their extended counterparts, (σ{sub ext}) = 317 ± 21 km s{sup –1}. Considering the weaker statistical correlation in the GC r{sub h} color and the GC r{sub h} -R {sub gal} relations, the more significant GC size-dynamics relation appears to be astrophysically more relevant and hints at the dominant influence of the GC orbit distribution function on the evolution of GC structural parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data from: "Ecophysiological variation in two provenances of Pinus flexilis seedlings across an elevation gradient from forest to alpine"

This archive contains data used to support conclusions drawn in “Ecophysiological variation in two provenances of Pinus flexilis seedlings across an elevation gradient from forest to alpine”, by Reinhardt et al., 2011. Data were collected over one summer season in plots within the Alpine Treeline Warming Experiment (ATWE), before climate manipulations began. The experiment was located on Niwot Ridge, in the Front Range of the Colorado Rocky Mountains. This data package includes five comma-separated-values (.csv) files, five Microsoft Excel (.xlsx) files, one .pdf file, and two types of geospatial files: keyhole markup language (.kml), and ESRI shapefiles (.shp). .csv files can be opened using any simple text-editing software (such as Notepad and TextEdit), R, and Microsoft Excel. .xlsx files can only be opened using Microsoft Excel. The .pdf file can be opened using Adobe Acrobat Reader or any other compatible file viewing software. The .kml file can be opened using Google Earth and Google Maps, and shapefiles can be opened using any software compatible with the file type, such as ESRI’s ArcGIS suite and QGIS.Data archived contain gas exchange and plant physiology measurements, non-structural carbohydrate data, among others. Geospatial files are also provided for additional locational context. The files and their contents in this data package are summarized under "Data Summary" in the included Data User's Guide. All files (excluding geospatial) are available in both Microsoft Excel and in .csv format, and are indicated in the Data Summary list as well.-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Climate change is predicted to cause upward shifts in forest tree distributions, which will require seedling recruitment beyond current forest boundaries. However, predicting the likelihood of successful plant establishment beyond current species’ ranges under changing climate is complicated by the interaction of genetic and environmental controls on seedling establishment. To determine how genetics and climate may interact to affect seedling establishment, we transplanted recently germinated seedlings from high- and low-elevation provenances (HI and LO, respectively) of Pinus flexilis in common gardens arrayed along an elevation and canopy gradient from subalpine forest into the alpine zone and examined differences in physiology and morphology between provenances and among sites. Plant dry mass, projected leaf area and shoot:root ratios were 12–40% greater in LO compared with HI seedlings at each elevation. There were no significant changes in these variables among sites except for decreased dry mass of LO seedlings in the alpine site. Photosynthesis, carbon balance (photosynthesis/respiration) and conductance increased >2× with elevation for both provenances, and were 35–77% greater in LO seedlings compared with HI seedlings. There were no differences in dark-adapted chlorophyll fluorescence (Fv/Fm) among sites or between provenances. Our results suggest that for P. flexilis seedlings, provenances selected for above-ground growth may outperform those selected for stress resistance in the absence of harsh climatic conditions, even well above the species’ range limits in the alpine zone. This indicates that forest genetics may be important to understanding and managing species’ range adjustments due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Onion-Like nanoparticles of the metal–organic framework UiO-66 synthesized by sequential spike crystal growth

Reproducible size and morphology of metal–organic framework (MOF) particles are essential for tuning these materials for applications like heterogeneous catalysis. Particle size measurements from electron micrographs provide information about the impact of changing synthetic parameters on MOF growth, but reported data are usually limited to averages and statistical distributions. Further elucidating synthetic control parameters could facilitate studies of facet-dependent and non-classical growth mechanisms. Here, in this work, phase-pure nanoparticles of the MOF UiO-66 were synthesized using alternating spikes of zirconium and hafnium-based nodes, forming an onion-like structure with alternating layers of Zr-UiO-66 and Hf-UiO-66. In conventional, bright-field transmission electron micrographs, Hf-UiO-66 and Zr-UiO-66 layers appeared darker and lighter, respectively. Alternating layers of Hf-UiO-66 and Zr-UiO-66 were also apparent in elemental maps obtained using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy. Average particle sizes increased monotonically with each spike, and no Zr-only or Hf-only particles were observed after the first spike, indicating that new material was consistently incorporated onto existing seeds. Surface area of the onion-like particles and their uptake of Ni 2+ from solution were both between the values measured for Zr-UiO-66 and Hf-UiO-66. Results demonstrate the use of sequential spikes of nodes in monitoring crystal growth in MOFs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Direct observation of diamond formation in a shock-compressed high explosive

Understanding the formation timescale and structure of carbonaceous reaction products is critical for modeling the high-pressure equation-of-state of organic materials. We use the National Ignition Facility to shock-compress polycrystalline TATB (C 6 H 6 N 6 O 6 ) samples to ~70–130 GPa and ~4000–5500 K, employing in situ nanosecond X-ray diffraction to probe reaction products and velocimetry to measure transmitted compression wave profiles. Our diffraction data is consistent with the formation of diamond over timescales less than ~60 ns. This represents carbon condensation from a molecular explosive on timescales three times faster than previously reported and the earliest observation of diamond produced from reacting TATB. Reactive flow simulations with explicit chemistry reproduce the observed temporal structure within wave profiles to inform the distribution of P-T states. These findings provide direct evidence of ultrafast diamond formation in a reactive system at extreme conditions and provide new constraints for models of shock and detonation chemistry.

Clarke, Samantha M. [Lawrence Livermore National L↗
DOE OSTI2014

Electrodynamics of a ring-shaped spiral resonator

We present analytical, numerical, and experimental investigations of electromagnetic resonant modes of a compact monofilar Archimedean spiral resonator shaped in a ring, with no central part. Planar spiral resonators are interesting as components of metamaterials for their compact deep-subwavelength size. Such resonators couple primarily to the magnetic field component of the incident electromagnetic wave, offering properties suitable for magnetic meta-atoms. Surprisingly, the relative frequencies of the resonant modes follow the sequence of the odd numbers as f{sub 1}:f{sub 2}:f{sub 3}:f{sub 4}… = 1:3:5:7…, despite the nearly identical boundary conditions for electromagnetic fields at the extremities of the resonator. In order to explain the observed spectrum of resonant modes, we show that the current distribution inside the spiral satisfies a particular Carleman type singular integral equation. By solving this equation, we obtain a set of resonant frequencies. The analytically calculated resonance frequencies and the current distributions are in good agreement with experimental data and the results of numerical simulations. By using low-temperature laser scanning microscopy of a superconducting spiral resonator, we compare the experimentally visualized ac current distributions over the spiral with the calculated ones. Theory and experiment agree well with each other. Our analytical model allows for calculation of a detailed three-dimensional magnetic field structure of the resonators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

3D distribution of interstellar medium in the Galaxy: Preparation for analysis of Gaia observations

Accurate and detailed three-dimensional (3D) maps of Galactic interstellar medium (ISM) are still lacking. One way to obtain such 3D descriptions is to record a large set of individual absorption or reddening measurements toward target stars located at various known distances and directions. The inversion of these measurements using a tomographic method can produce spatial distribution of the ISM. Until recently absorption data were very limited and distances to the target stars are still uncertain, but the situation will greatly improve thanks to current and future massive stellar surveys from ground, and to Gaia mission. To prepare absorption data for inversion from a huge number of stellar spectra, automated tools are needed. We have developed various spectral analysis tools adapted to different type of spectra, early- or late- type star. We also have used diffuse interstellar bands (DIBs) to trace IS structures and kinematics. Although we do not know yet their carriers, they can be a promising tool to trace distant interstellar clouds or Galactic arms. We present some examples of the interstellar fitting and show the potentiality of DIBs in tracing the ISM. We will also briefly show and comment the latest 3D map of the local ISM which reveal nearby cloud complexes and cavities.

79 ASTRONOMY AND ASTROPHYSICS↗