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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Correlating laser-induced breakdown spectroscopy with neutron activation analysis to determine the elemental concentration in the ionome of the Populus trichocarpa leaf

The black cottonwood poplar (Populus trichocarpa) leaf ionome (inorganic trace elements and mineral nutrients) is an important aspect for determining the physiological and developmental processes contributing to biomass production. A number of techniques are used to measure the ionome, yet characterizing the leaf spatial heterogeneity remains a challenge, especially in solid samples. Laser-induced breakdown spectroscopy (LIBS) has been used to determine the elemental composition of leaves and is able to raster across solid matrixes at 10 μm resolution. Here, we evaluate the use of LIBS for solid sample leaf elemental characterization in relation to neutron activation. In fact, neutron activation analysis is a laboratory-based technique which is used by the National Institute of Standards and Technology (NIST) to certify trace elements in candidate reference materials including plant leaf matrices. Introduction to the techniques used in this research has been presented in this manuscript. Neutron activation analysis (NAA) data has been correlated to the LIBS spectra to achieve quantification of the elements or ions present within poplar leaves. The regression coefficients of calibration and validation using multivariate analysis (MVA) methodology for six out of seven elements have been determined and vary between 0.810 and 0.998. LIBS and NAA data has been presented for the elements such as, calcium, magnesium, manganese, aluminum, copper, and potassium. Chlorine was also detected but it did not show good correlation between the LIBS and NAA techniques. This research shows that LIBS can be used as a fast, high-spatial resolution technique to quantify elements as part of large-scale field phenotyping projects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

A variance-based decomposition and global sensitivity index method for uncertainty quantification: Application to retrieved ice cloud properties

This study develops a novel uncertainty quantification (UQ) method for cloud microphysical property retrievals using variance-based decomposition and global sensitivity index. In this UQ framework, empirical orthogonal function (EOF) analysis is applied to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) ground-based observations, which are the inputs for the cloud retrieval studied here. The principal components (PCs) in the EOF expansion are parameterized as random input variables, and hence, the input dimension is greatly reduced (up to a factor of 50), allowing large ensemble of random samplings. The EOF expansion improves the accuracy of the uncertainty estimation by taking into account the cross correlations in the input data profiles. Here, this method enables a probabilistic representation of a retrieval process by adding normally distributed perturbations into PCs of sample means of input data profiles within a time window. Therefore, it effectively facilitates objective validation of climate models against cloud retrievals under a probabilistic framework for rigorous statistical inferences. Moreover, the variance-based global sensitivity index analysis, part of this method, attributes the output uncertainties to each individual source, thus providing directions for improving retrieval algorithms and observation strategies. For demonstration, we apply this method to quantify the uncertainties of the ARM program's baseline cloud retrieval algorithm for an ice cloud case observed at the Southern Great Plains site on 9 March 2000.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Dense and half-dense NiZnCo ferrite ceramics: Their respective relevance for antenna downsizing, according to their dielectric and magnetic properties at microwave frequencies

Spinel ferrite Ni{sub 0.5}Zn{sub 0.3}Co{sub 0.2}Fe{sub 1.98}O{sub 4−x} nanoparticles were synthesized by co-precipitation method, and samples were realized by moulding and annealing at key temperatures (T{sub M} = 800 °C, 900 °C, 1050 °C, determined beforehand through shrinkage measurements) going with calcining and sintering processes. Annealing at 800 °C and 900 °C led to half-dense ceramics (porosity ∼50 vol. %), whereas bulky ferrite was obtained after annealing at 1050 °C. Elemental analysis, X-ray diffraction and ion chromatography analysis were performed. Complex dielectric permittivity (ε*) and magnetic permeability (μ*) were investigated up to 6 GHz. With increasing T{sub M}, a decreasing amount of Fe{sup 2+} was observed, going with increasing sample density. Coupled effects of the Fe{sup 2+} concentration and of the porosity, both on dielectric and magnetic properties, were chiefly investigated and discussed. The materials show almost constant permittivities (ε′ = 5.0, 6.0, and 14.8 for T{sub M} = 800 °C, 900 °C and 1050 °C, respectively). The bulk value at f = 1 GHz (ε′ = 14.8) can be interpreted well according to Shannon's theory. The permittivities of the half-dense ceramics are discussed on the basis of Bruggeman's Effective Medium Theory. The materials annealed at 800 °C and 900 °C show almost constant magnetic permeabilities in the frequency range from 0.2 to 1 GHz (μ′ = 3.4 and 6.0 for T{sub M} = 800 °C and 900 °C). The observed permeability behavior is typical of monodomain particles, except for the sample annealed at 1050 °C, for which domain wall contribution to μ* is suspected because of non-negligible losses at low frequency (μ″ = 1.3–1.8 at f < 0.3 GHz). This finding is supported by estimations of the upper and lower values for the critical grain size, on the basis of Brown–Van der Zaag's theory. Facing bulk ceramics, and in view of using Ni{sub 0.5}Zn{sub 0.3}Co{sub 0.2}Fe{sub 1.98}O{sub 4−x} ferrite as substrate for antenna miniaturization, the electromagnetic properties of half-dense ceramics materials seem to be very competitive at frequencies beyond 0.2 GHz, and up to 0.7–0.8 GHz.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Effects of Mg Substitution on the Structural and Magnetic Properties of Ni{sub 0.2}Mg{sub x}Co{sub 0.8−x}Fe{sub 2}O{sub 4} Nanoparticle Ferrites

Sol-gel auto-combustion is a method of preparing ferrite by combining combustion with chemical gel. In this study, Ni-Mg-Co ferrite powders are prepared by coprecipitation method, and the nanocrystals of Ni{sub 0.2}Mg{sub x}Co{sub 0.8−x}Fe{sub 2}O{sub 4} are successfully synthesized. The structure and magnetic properties of undoped and Mg-substituted Ni-Co ferrite nanoparticles are systematically investigated. The methods used to characterize the prepared samples are X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and Vibrating sample magnetometry (VSM). The synthesized samples are confirmed by XRD analysis to form a single-phase cubic spinel structure with crystals between 48 and 50 nm. With the increase of Mg ion concentration, the lattice constant decreases. The results of FTIR spectroscopy indicated that a spinel structure was formed. Transmission electron microscopy (TEM) images show spherical cubic microcrystals in the samples. EDX analysis confirms that the synthesized ferrite is pure phase structure, and Mg{sup 2+} is successfully replaced. With the increase of Mg{sup 2+} ion content, the saturation magnetization and remanent magnetization decreased from 70.16 to 39.77 emu/g and 36.40 to 20.20 emu/g at room temperature, respectively. Meanwhile, the coercivity decreases from 1032.16 to 378.50 Oe by increasing Mg{sup 2+} concentration. This also indicates that the Mg-substituted Ni-Co nano-ferrite has a low magnetic of multi-ferric material. The increasing of peak height of dM/dH at H{sub m} indicates that the cubic spinel structure sample has good crystallinity and magnetic stability.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

An Efficient Sampling-Based Method for Sensitivity and Uncertainty Analysis Through RAVEN

Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Atom probe tomography analysis of the reference Zircon GJ-1: An interlaboratory study

In recent years, atom probe tomography (APT) has been increasingly used to study minerals, and in particular the mineral zircon. Zircon (ZrSiO 4 ) is ideally suited for geochronology by utilising the U-Th-Pb isotope systems, and trace element compositions are also widely used to constrain petrogenetic processes. However, while standard geoanalytical techniques provide information at micron scale lengths, the unique combination of chemical/isotopic sensitivity and spatial resolution of APT allows compositional and textural measurements at the nanoscale. This round robin study aims to define the reproducibility of APT data across research facilities and assess the role of different aspects of the atom probe workflow on reproducibility. This is essential to allow correct evaluation of APT results and full utilization of this emerging technique within the geoscience community. In this study, nine samples from the same homogemeous, GJ-1/87 zircon reference grain were sent to nine APT institutes in Europe, USA, Canada and Australia. Each institute conducted three different rounds of APT analyses: using (i) unconstrained analysis parameters, [1] pre-defined analysis parameters, and (iii) a data analysis exercise. Data such as the measured composition, acquisition parameters, or mass spectrum peak identifications, were recorded and analyzed. We observe a significant variation in the measured composition across this round robin study as well as the number of trace elements identified. These differences are thought to directly result from the user's choice of peak identification, ranging and background correction model. The type of instrument does not seem to be a critical factor. Consequently, comparison of absolute trace element data on zircon using APT between laboratories is only valid if the same workflow has been ensured.

round robin, atom probe tomography, reference zirc↗
DOE OSTI2019

Thermo-Mechanical Behavior of Genetically Modified Populus trichocarpa

Wood processing is often performed at elevated temperatures under moisture-saturated conditions; therefore, it is important to understand the impact of the lignin content and lignin chemical structure on the thermo-mechanical properties of wood. In this study, genetically modified Populus trichocarpa wood specimens with down-regulated cinnamyl alcohol dehydrogenase, cinnamate 3-hydroxylase, and cinnamate 4-hydroxylase with altered lignin contents and/or lignin structures were utilized to probe the relationship between the lignin content, lignin monomer composition, and thermo-mechanical properties of solid wood. The thermo-mechanical properties of these unique samples were measured using dynamic mechanical analysis and the nuclear magnetic resonance (NMR) spin-spin relaxation time. The results showed that the transgenic P. trichocarpa samples had decreased storage and loss moduli compared with the wildtype. The solid-state NMR revealed increased lignin molecular mobility in the reduced-lignin transgenic lines. Also, noticeably reduced glass transition temperatures (Tg) were observed in the transgenic lines with reduced lignin contents and altered lignin monomer compositions compared with the wildtype. The increased lignin molecular mobility and reduced Tg in these samples can probably contribute to wood utilization and processing, such as lignin removal for pulp and paper and biofuels production, as well as particle consolidation during wood composite manufacturing.

09 BIOMASS FUELS↗
DOE OSTI2018

A Hybrid Plasma Treatment of H13 Tool Steel by Combining Plasma Nitriding and Post-Oxidation

Pulsed plasma nitriding and post-oxidizing process have been applied to hot work H13 tool steel to enhance the tribological properties (friction coefficient and wear rate). Nitriding was accomplished under specific conditions to generate a free white layer surface in order to avoid an abrasive wear mechanism. Post-oxidizing treatment was carried out in the same reactor after finishing the nitriding treatment; processing time was varied for 30 and 60 min. The morphology, phase’s composition and wear performance of duplex-treated samples were evaluated by scanning electron microscopy, x-ray diffraction analysis and pin-on-ball wear technique. The results show that the duplex treatment produces a bilayer arrangement constituted by an 80 μm diffusion zone with expanded martensite as the main phase underneath a 2–4 μm hematite–magnetite-oxidized layer. The duplex treatment modified the main wear mechanism of the nitrided sample from abrasive to plastic yielding. The changes in wear mechanisms resulted in a noticeable decrease, about 40%, in friction coefficient for oxidized samples in comparison with the nitrided-only sample. It was also established that the lowest friction coefficient values can be obtained when hematite is the main phase in the oxidized layer.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Rapid Quadrupole-Time-of-Flight Mass Spectrometry Method Quantifies Oxygen-Rich Lignin Compound in Complex Mixtures

Complex mixture analysis is a costly and time-consuming task facing researchers with foci as varied as food science and fuel analysis. When faced with the task of quantifying oxygen-rich bio-oil molecules in a complex diesel mixture, we asked whether complex mixtures could be qualitatively and quantitatively analyzed on a single mass spectrometer with mid-range resolving power without the use of lengthy separations. To answer this question, we developed and evaluated a quantitation method that eliminated chromatography steps and expanded the use of quadrupole-time-of-flight mass spectrometry from primarily qualitative to quantitative as well. To account for mixture complexity, the method employed an ionization dopant, targeted tandem mass spectrometry, and an internal standard. This combination of three techniques achieved reliable quantitation of oxygen-rich eugenol in diesel from 300 to 2500 ng/mL with sufficient linearity (R{sup 2} = 0.97 ± 0.01) and excellent accuracy (percent error = 0% ± 5). To understand the limitations of the method, it was compared to quantitation attained on a triple quadrupole mass spectrometer, the gold standard for quantitation. The triple quadrupole quantified eugenol from 50 to 2500 ng/mL with stronger linearity (R{sup 2} = 0.996 ± 0.003) than the quadrupole-time-of-flight and comparable accuracy (percent error = 4% ± 5). This demonstrates that a quadrupole-time-of-flight can be used for not only qualitative analysis but also targeted quantitation of oxygen-rich lignin molecules in complex mixtures without extensive sample preparation. The rapid and cost-effective method presented here offers new possibilities for bio-oil research, including: (1) allowing for bio-oil studies that demand repetitive analysis as process parameters are changed and (2) making this research accessible to more laboratories. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

High-temperature steam oxidation study of irradiated FeCrAl defueled specimens

Post irradiation examinations (PIE) were performed on irradiated iron-chromium-aluminum (FeCrAl) specimens. These FeCrAl specimens were fabricated at the US Department of Energy's Oak Ridge National Laboratory (ORNL). The experimental setup involved subjecting FeCrAl cladding, along with UO 2 pellets, to irradiation in the Idaho National Laboratory Advanced Test Reactor (ATR). In parallel, the FeCrAl alloy tubing without UO 2 pellets was irradiated at ORNL's High Flux Isotope Reactor (HFIR). After irradiation, the ATR-irradiated rodlet was transported to an ORNL hot cell, where it was sectioned into multiple samples for the PIE and severe-accident testing. The sectioning process revealed that the fuel was not bonded to the cladding and could be easily detached from sectioned cladding slices. Microstructural analysis of the fuel cross sections demonstrated no significant interaction between the fuel and the cladding. Additionally, high-temperature steam oxidation tests on defueled cladding segments showed minimal oxygen uptake even at 1200 °C. Here, the ATR-irradiated specimens began to exhibit signs of enhanced oxidation upon reaching a temperature of 1300 °C. Furthermore, enhanced oxidation was observed on the inner surface of the ATR-irradiated FeCrAl specimen, which had been subjected to 1300 °C for a duration of 1 min. By contrast, high-temperature steam oxidation experiments indicated that the HFIR-irradiated FeCrAl cladding provided good thermal stability when exposed to 1300 °C for up to 4 h. Comparative analysis encompassing the oxidation behavior of the ATR-irradiated fueled FeCrAl, HFIR-irradiated unfueled FeCrAl, and unirradiated FeCrAl suggests that the fuel–cladding interaction, although not visible via standard microscale electron microscopy measurements, may accelerate the deterioration of FeCrAl cladding in beyond-design-basis accident scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Machine learning-assisted profiling of a kinked ladder polymer structure using scattering

Ladder polymers consisting of fused rings in the backbone have very limited conformational freedom, which results in very different properties from traditional linear polymers. However, accurately determining their size and chain conformations from solution scattering remains a challenge. Their chain conformations of kinked ladder polymers are largely governed by the structures and relative orientations or configurations of the repeat units, unlike conventional polymer chains whose bending angles between repeat units follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to construct a model that can describe the geometry of a type of kinked CANAL ladder polymers. We first develop a Monte Carlo simulation model for sampling the configuration space of CANAL ladder polymers, where each repeat unit is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a CANAL ladder polymer solution to apply our approach. Our method uncovers structural features of such ladder polymers that conventional methods fail to capture.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2016

CFD simulation of simultaneous monotonic cooling and surface heat transfer coefficient

The monotonic heating regime method for determination of thermal diffusivity is based on the analysis of an unsteady-state (stabilised) thermal process characterised by an independence of the space-time temperature distribution on initial conditions. At the first kind of the monotonic regime a sample of simple geometry is heated / cooled at constant ambient temperature. The determination of thermal diffusivity requires the determination rate of a temperature change and simultaneous determination of the first eigenvalue. According to a characteristic equation the first eigenvalue is a function of the Biot number defined by a surface heat transfer coefficient and thermal conductivity of an analysed material. Knowing the surface heat transfer coefficient and the first eigenvalue the thermal conductivity can be determined. The surface heat transport coefficient during the monotonic regime can be determined by the continuous measurement of long-wave radiation heat flow and the photoelectric measurement of the air refractive index gradient in a boundary layer. CFD simulation of the cooling process was carried out to analyse local convective and radiative heat transfer coefficients more in detail. Influence of ambient air flow was analysed. The obtained eigenvalues and corresponding surface heat transfer coefficient values enable to determine thermal conductivity of the analysed specimen together with its thermal diffusivity during a monotonic heating regime.

42 ENGINEERING↗
DOE OSTI2025

Electrical conductivity of multilayered Cu/Nb composites fabricated by accumulative roll bonding

The electrical conductivity of nanolayered copper/niobium composites fabricated using accumulative roll bonding was investigated as a function of layer thickness. Cu/Nb was used as a model system to evaluate the processing-structure–property relationship stemming from the accumulative roll bonding process. The physical properties were compared against samples of individual average layer heights ranging from 193 to 25 nm. The electrical resistivity was measured over a temperature range of ~3–300 K. Analysis on the role of interfaces on temperature dependence is conducted including the residual resistivity ratio and temperature coefficient of resistivity. It was found that electrical resistivity increases with decreasing layer height.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Temperature structure of the intracluster medium from smoothed-particle hydrodynamics and adaptive-mesh refinement simulations

Analyses of cosmological hydrodynamic simulations of galaxy clusters suggest that X-ray masses can be underestimated by 10%-30%. The largest bias originates from both violation of hydrostatic equilibrium (HE) and an additional temperature bias caused by inhomogeneities in the X-ray-emitting intracluster medium (ICM). To elucidate this large dispersion among theoretical predictions, we evaluate the degree of temperature structures in cluster sets simulated either with smoothed-particle hydrodynamics (SPH) or adaptive-mesh refinement (AMR) codes. We find that the SPH simulations produce larger temperature variations connected to the persistence of both substructures and their stripped cold gas. This difference is more evident in nonradiative simulations, whereas it is reduced in the presence of radiative cooling. We also find that the temperature variation in radiative cluster simulations is generally in agreement with that observed in the central regions of clusters. Around R {sub 500} the temperature inhomogeneities of the SPH simulations can generate twice the typical HE mass bias of the AMR sample. We emphasize that a detailed understanding of the physical processes responsible for the complex thermal structure in ICM requires improved resolution and high-sensitivity observations in order to extend the analysis to higher temperature systems and larger cluster-centric radii.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Inhomogeneous Magnetic State of Ce{sub 2}Fe{sub 17} Evidenced by Mössbauer Spectroscopy

A comparative analysis of the magnetic properties and Mössbauer spectra of two Ce{sub 2}Fe{sub 17} samples prepared by different methods and demonstrating different magnetic behavior is carried out. A model for processing the spectra is proposed, which gives a good description of the Mössbauer spectra of samples in different magnetic states by a superposition of eight subspectra. It is shown that in the samples in the ferromagnetic state there are regions with antiferromagnetic ordering at the local level. The relative fractions of iron atoms in these regions differs for different samples and increases when approaching the temperature of the transition from the ferromagnetic to the antiferromagnetic state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

scANN

SAND2025-00656O scANN, also known as sampling by coinflips artificial neural networks, is a software tool that estimates uncertainty in artificial intelligence by performing Monte Carlo analysis on the weight matrices of feedforward neural networks. This computationally intensive process aims to explore the potential value added by future probabilistic hardware. The program’s output helps researchers gain insights into how probabilistic neural networks work. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗