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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evolution of the role of molybdenum in duplex stainless steels during thermal aging: From enhancing spinodal decomposition to forming heterogeneous precipitates

Thermal aging of ferrite in duplex stainless steels leads to a complex microstructural evolution that can give way to large deleterious changes in mechanical properties. The microstructural evolution mechanisms that happen concurrently are strongly linked to minor changes in composition. By characterizing duplex stainless steels with various compositions over a range of aging conditions, it was revealed that adding a modest amount of Mo to cast austenitic stainless steel (such as changing from grade CF3 to grade CF3M) not only increases the kinetics of spinodal decomposition and Ni-Si-Mn-rich G-phase precipitation, but it also forms a separate Mo-rich phase. At shorter equivalent aging times, Mo segregates from Fe and towards Cr accelerating spinodal decomposition. At longer aging times, Mo enriches in the Ni-Si-Mn clusters before phase separating into an adjoined Mo-rich cluster. We found that these phase changes and precipitation events result in a rather steep drop in fracture and impact toughness. These results will inform the design of similar alloys for use at high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Time–connectivity superposition and the gel/glass duality of weak colloidal gels

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Measuring cosmological distances using cluster edges as a standard ruler

ABSTRACT The line-of-sight velocity dispersion profile of galaxy clusters exhibits a ‘kink’ corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion profile, we can utilize this feature as a gravity-calibrated standard ruler. Specifically, the amplitude of the velocity dispersion data allows us to infer the physical cluster size. Consequently, observations of the angular scale of the ‘kink’ in the profile can be translated into a distance measurement to the cluster. Assuming the relation between cluster radius and cluster velocity dispersion can be calibrated from simulations, we forecast that with existing data from the Sloan Digital Sky Survey we will be able to measure the Hubble constant with 3.0 per cent precision. Implementing our method with data from the Dark Energy Spectroscopic Instrument (DESI) will result in a 1.3 per cent measurement of the Hubble constant. Adding cosmological supernova data improves the uncertainty of the DESI measurement to 0.7 per cent. While these error estimates are statistical only, they provide strong motivation for pursuing the necessary simulation program required to characterize and calibrate the systematic uncertainties impacting our proposed measurement. Whether or not our proposed measurement can in fact result in competitive H0 constraints will depend on what the eventual systematics floor for this method is.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

An inhomogeneous reference catalogue of identified intervening heavy element systems in spectra of QSOs

Identifications of heavy element line systems, observed in spectra of quasi-stellar objects between 1965 and 1989 inclusive, are collected and tabulated with references. Each system is assigned a quality grade based on the apparent reliability of the data. The highest quality systems are used to characterize the absorbers as a sample. A decrease in C IV line strength with redshift (z), and the corresponding decrease in line density per unit z with z are confirmed. The weakest C IV systems, at high z, are accompanied by relatively stronger Si IV lines, compared to the relative line strengths at low z. The space density of systems with strong lines of first ions is nearly independent of z below z = 3, though it drops at z greater than 3. Preliminary tests for quasar lensing by intervening systems and for clustering of absorbers on large scales are presented.

York, Donald G.↗
NASA NTRS2022

Characterizing Student-Driven Research Investigations Contributed to the GLOBE Program Citizen Science Initiative in a Formal Education Context

The Global Learning and Observations to Benefit the Environment (GLOBE) Program offers citizen science opportunities to participants of all ages, with a focus on youth in formal classroom contexts. This study uses student investigation research reports and posters submitted to the 2018 International Virtual Science Symposium (IVSS) and Student Research Symposium (SRS) as testbeds for characterizing student-driven Earth system citizen science investigations. Secondarily, this study aimed to capture GLOBE’s alignment to existing citizen science outcomes frameworks in the literature, which have primarily focused on adults and non-formal settings. Based on a literature review, the evaluation team identified 89 potential characteristics in 27 categories to typify investigations from both formal education and citizen science perspectives. We coded the artifacts from 207 student projects, conducted quantitative analysis of frequencies, and performed a semantic network analysis. By using this networking approach, we conceptually mapped several clusters of co-occurring characteristics, defining a descriptive framework for GLOBE projects. We identified three tiers of citizen science projects, increasing in the sophistication of participants’ demonstrated science practices. The framework includes additional components that reflect student citizen scientists’ thoughtfulness and connection to context as well as their projects’ reflection of their motivation and self efficacy. Through these findings, we have identified areas where student citizen scientists would benefit from further support, and suggest here further research to incorporate the experiences of students into the broader understanding of citizen science outcomes.

network analysis↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗
DOE OSTI2025

Alkali Counterion-Dependent Crystallization of Uranium(IV)–Chloro Structural Units

The synthesis, structural characterization, and spectroscopic properties of five tetravalent uranium (U) phases including Li 6 [U 4 (μ 3 -O) 2 Cl 18 (H 2 O) 2 ]·10H 2 O (1), [U(H 2 O) 4 Cl 4 ] (2), [U(H 2 O) 4 Cl 4 ]·KCl (3), Rb 2 UCl 6 (4), and Cs 2 UCl 6 (5) are reported. Notably, a change in the U 4+ solid-state structural unit was observed based on the identity of the alkali counterion used in the synthesis. Li 1+ yielded a tetranuclear oxo-bridged cluster, [U 4 (μ 3 -O) 2 Cl 18 (H 2 O) 2 ] 6– , Na 1+ and K 1+ yielded two structurally distinct [U(H 2 O) 4 Cl 4 ] complexes, and Rb 1+ and Cs 1+ resulted in [UCl 6 ] 2– as the dominant phases. The spectroscopic properties of the compounds were analyzed using Raman and UV–vis–NIR absorption spectroscopy. The UV–vis–NIR spectra of compounds 1–5 exhibited transitions consistent with uranium in the +4 oxidation state. Clear differences in the absorption band splitting were observed and are likely attributed to differences in metal ion coordination, crystal field effects, and outer sphere interactions Overall, this work demonstrates the utility of noncovalent interactions in tuning the crystallization of various metal complexes from otherwise identical reaction solutions and provides further evidence that counterions impact the composition and structure of actinide complexes isolated in the solid state. In this way, this work affords important insight into directing and controlling the structure of actinide complexes and clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Structure in the 3D Galaxy Distribution. III. Fourier Transforming the Universe: Phase and Power Spectra

We demonstrate the effectiveness of a relatively straightforward analysis of the complex 3D Fourier transform of galaxy coordinates derived from redshift surveys. Numerical demonstrations of this approach are carried out on a volume-limited sample of the Sloan Digital Sky Survey redshift survey. The direct unbinned transform yields a complex 3D data cube quite similar to that from the Fast Fourier Transform of finely binned galaxy positions. In both cases, deconvolution of the sampling window function yields estimates of the true transform. Simple power spectrum estimates from these transforms are roughly consistent with those using more elaborate methods. The complex Fourier transform characterizes spatial distributional properties beyond the power spectrum in a manner different from (and we argue is more easily interpreted than) the conventional multipoint hierarchy. We identify some threads of modern large-scale inference methodology that will presumably yield detections in new wider and deeper surveys.

Galaxies↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2024

Quest for Multifunctionality: Current Progress in the Characterization of Heterometallic Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are a class of porous, crystalline materials that have been systematically developed for a broad range of applications. Incorporation of two or more metals into a single crystalline phase to generate heterometallic MOFs has been shown to lead to synergistic effects, in which the whole is oftentimes greater than the sum of its parts. Because geometric proximity is typically required for metals to function cooperatively, deciphering and controlling metal distributions in heterometallic MOFs is crucial to establish structure–function relationships. However, determination of short- and long-range metal distributions is nontrivial and requires the use of specialized characterization techniques. Advancements in the characterization of metal distributions and interactions at these length scales is key to rapid advancement and rational design of functional heterometallic MOFs. Further, this perspective summarizes the state-of-the-art in the characterization of heterometallic MOFs, with a focus on techniques that allow metal distributions to be better understood. Using complementary analyses, in conjunction with computational methods, is critical as this field moves toward increasingly complex, multifunctional systems.

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

Highly Durable and Active Pt/Sb-Doped SnO 2 Oxygen Reduction Reaction Electrocatalysts Produced by Atomic Layer Deposition

Platinum supported on mixed-metal oxides (MMOs) are a class of active and durable cathode catalysts for proton exchange membrane fuel cell (PEMFC) due to a combination of the high oxidative stability of the supports and strong-metal-support interactions (SMSI) that enable them to exceed the activity of Pt/C. Herein, we solve a significant remaining challenge with Pt/MMO systems, namely the relatively low surface area and porosity. This is achieved by dispersing nearly-uniform Pt clusters using atomic layer deposition (ALD) on highly conductive (6.2 S/cm) and stable antimony doped tin dioxide (ATO) support. ALD-Pt/ATO exhibited significantly higher electrochemically active surface area (ECSA) (74 m 2 /g) and oxygen reduction reaction (ORR) catalytic activity (102 mA/mg Pt at 0.9 V vs. RHE) compared to Pt/ATO synthesized using ethylene glycol (ECSA=31 m 2 /g Pt , mass activity=52 mA/mg Pt at 0.9 V vs. RHE) and formic acid reduction methods (ECSA=28 m 2 /g Pt , mass activity=46 mA/mg Pt at 0.9 V vs. RHE). Further characterization showed that wet chemical methods resulted in poorer Pt particle dispersion, poor control over Pt particle size distribution and chemical degradation of the support (during Pt deposition). Given the near-ideal Pt particle size distribution of the ALD-Pt/ATO, particle size growth and loss of ECSA was found to be minimal over the course of rigorous potential cycling. Thus, after 10,000 potential cycles between 1V and 1.5V vs. RHE, ALD-Pt/ATO and other Pt/ATOs were found to retain 100% of their initial ECSA compared to 57.6% retention for Pt/C. Upon testing in a H 2 /air PEMFC, following 1,000 potential cycles, the change in ALD-Pt/ATO performance was negligible while Pt/C exhibited a 68.2% loss of initial peak power density. Thus, ALD-Pt/ATO is an active and highly durable ORR electrocatalyst in PEMFCs under start-up-shut down conditions.

25 ENERGY STORAGE↗
DOE OSTI2025

High-significance detection of correlation between the unresolved gamma-ray background and the large-scale cosmic structure

Our understanding of the γ-ray sky has improved dramatically in the past decade, however, the unresolved γ-ray background (UGRB) still has a potential wealth of information about the faintest γ-ray sources pervading the Universe. Statistical cross-correlations with tracers of cosmic structure can indirectly identify the populations that most characterize the γ-ray background. In this study, we analyze the angular correlation between the γ-ray background and the matter distribution in the Universe as traced by gravitational lensing, leveraging more than a decade of observations from the Fermi-Large Area Telescope (LAT) and 3 years of data from the Dark Energy Survey (DES). We detect a correlation at signal-to-noise ratio of 8.9. Most of the statistical significance comes from large scales, demonstrating, for the first time, that a substantial portion of the UGRB aligns with the mass clustering of the Universe as traced by weak lensing. Blazars provide a plausible explanation for this signal, especially if those contributing to the correlation reside in halos of large mass (∼ 10 14 M ⊙ ) and account for approximately 30–40% of the UGRB above 10 GeV. Additionally, we observe a preference for a curved γ-ray energy spectrum, with a log-parabolic shape being favored over a power-law. We also discuss the possibility of modifications to the blazar model and the inclusion of additional γ-ray sources, such as star-forming galaxies, misalinged active galactic nuclei, or particle dark matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Characterizing the target selection pipeline for the Dark Energy Spectroscopic Instrument Bright Galaxy Survey

ABSTRACT We present the steps taken to produce a reliable and complete input galaxy catalogue for the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) using the photometric Legacy Survey DR8 DECam. We analyse some of the main issues faced in the selection of targets for the DESI BGS, such as star–galaxy separation, contamination by fragmented stars and bright galaxies. Our pipeline utilizes a new way to select BGS galaxies using Gaia photometry and we implement geometrical and photometric masks that reduce the number of spurious objects. The resulting catalogue is cross-matched with the Galaxy And Mass Assembly (GAMA) survey to assess the completeness of the galaxy catalogue and the performance of the target selection. We also validate the clustering of the sources in our BGS catalogue by comparing with mock catalogues and the Sloan Digital Sky Survey (SDSS) data. Finally, the robustness of the BGS selection criteria is assessed by quantifying the dependence of the target galaxy density on imaging and other properties. The largest systematic correlation we find is a 7 per cent suppression of the target density in regions of high stellar density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Crystal structure of [Th 3 (Cp*) 3 (O)(OH) 3 ] 2 Cl 2 (N 3 ) 6 : a discrete molecular capsule built from multinuclear organothorium cluster cations

An unusually large and structurally complex charge-neutral polynuclear cluster, hexa-μ 2 -azido-di-μ 3 -chlorido-hexa-μ 2 -hydroxido-di-μ 3 -oxido-hexakis(pentamethylcyclopentadienyl)hexathorium–diethyl ether–tetrahydrofuran (1/0.56/1.44), [Th 3 (C 10 H 15 ) 6 Cl 3 (N 3 ) 6 (OH) 6 O 2 ]·0.56C 4 H 10 O·1.44C 4 H 8 O or [Th 3 (Cp*) 3 (O)(OH) 3 ] 2 Cl 2 (N 3 ) 6 ·0.56C 4 H 10 O·1.44C 4 H 8 O (Cp* = [pentamethylcyclopentadienyl]) − , has been crystallized as a mixed tetrahydrofuran/diethyl ether solvate and structurally characterized. The molecule contains a number of unusual features, the most notable being a finite yet exceptionally long cyclic metal-azido chain. These rare features are the consequence of both sterically protecting Cp* ligands and highly bridging oxide and hydroxide ligands in the same system and illustrate the interesting new possibilities that can arise from combining organometallic and solvothermal f -block element chemistry.

Kelley, Steven P.↗