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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Microbeam synchrotron radiation diffraction and fluorescence of oxide layers formed on zirconium alloys at different corrosion temperatures

Autoclave corrosion tests of Zircaloy-4 were performed at temperatures ranging from 274°C to 427 °C to determine how the characteristics of the oxide layer changed with corrosion temperature. Corroded samples were prepared in cross-section and examined at the Advanced Photon Source using x-ray diffraction and fluorescence with sub-micron spatial resolution. These measurements provide high fidelity data for characterization of the oxide layer grown on zirconium alloys over a range of temperatures to better understand the fundamental mechanisms of corrosion. It was found that the size of the monoclinic oxide grains increases with distance from the metal-oxide interface, while the average grain size of the tetragonal oxide phase decreases. This is consistent with the continuous nucleation of a mixture of equiaxed tetragonal and monoclinic grains at the metal-oxide interface, of which only the monoclinic oxide grains that are properly aligned to minimize stresses grow into long columnar oxide grains. The tetragonal phase oxide grains transform to monoclinic oxide once a critical size is reached. While the monoclinic oxide grain size increases with corrosion temperature, the tetragonal phase grain size remains below a maximum value. Evolution of the tetragonal fraction, calculated with the Garvie-Nicholson formula, was observed, with local maxima seen just before transition and local minima seen at oxide locations corresponding to the oxide kinetic transition. No difference was observed in the tetragonal phase fraction in the oxide layers formed at different corrosion temperatures. Detailed fluorescence and diffraction mapping of one sample showed the evolution of the diffraction intensity over a short range in the oxide. In conclusion, this work provides a systematic study revealing fundamental characteristics of the microstructure of Zircaloy-4 oxide layers to further understanding of corrosion resistance of these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Effect of the both texture and electrical properties of activated carbon on the CO{sub 2} adsorption capacity

Highlights: • A series of activated carbon pellet without binder was prepared by chemical activation. • Carbon dioxide storage isotherm at 30 °C and up to 25 bars was measured for the microporous carbon. • Adsorption enthalpies have been correlated with the carbon dioxide uptake. • Pyrolysis temperature effect on the electrical conductivity of the samples. • Impact of the both texture and electrical properties on CO{sub 2} adsorption capacity have been deducted - Abstract: A series of activated carbon pellets (ACP) based on olive stones were studied for CO{sub 2} storage application. The surface area, pore volume, and pore diameter were evaluated from the analysis of N{sub 2} adsorption isotherm data. The characterization of carbon materials was performed by scanning electron microscopy (SEM), the powder X-ray diffraction (PXRD) and transmission electron microscopy (TEM). The adsorption enthalpies were obtained by microcalorimetry. The effect of pyrolysis temperature on textural, electrical conductivity and gas adsorption capacities of the ACP were investigated by adsorbing CO{sub 2} at 303 K in the pressure range of 0–2.3 MPa. In fact the electrical conductivity is strongly affected by the microporosity of the samples and the size of the micropore. It increases when the pore size decreases which affect the CO{sub 2} adsorption. Also with increases temperature the free electrons concentration on the surface increases which affect the interaction of the adsorbed gas molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Competing Pathways for Nucleation of the Double Perovskite Structure in the Epitaxial Synthesis of La 2 MnNiO 6

Over the past decades a confluence of advanced synthesis techniques, data-driven characterization, and rapid increases in computing power has sparked a renaissance in materials engineering. The emerging “materials-by-design” approach, while radically transforming the development of multicomponent systems, has tended to overlook the complex kinetic pathways that define materials synthesis. Although we are able to envision almost limitless materials combinations, we are unable to synthesize all of them in practice, since existing characterization and modeling approaches often fail to capture the inherent complexity of such systems. There is currently a disconnect between highly local structural characterization and macroscale properties measurements, resulting in oversimplified or incomplete structure-property models. Here we describe a multi-technique approach that combines aberration-corrected transmission electron microscopy with emerging oxide atom probe tomography to measure chemical ordering and extended defects in the model complex oxide, La2MnNiO6. We visualize cation ordering, as well as a three-dimensional network of secondary phases, which we describe in terms of ab initio structure calculations. We propose a defect model in which these phases alter their surrounding octahedral environment, severely disrupting cation superexchange. Through this array of experimental and theoretical techniques, we uncover fundamental structure-property relationships and illustrate a new approach to engineer complex, multicomponent systems.

atom probe tomography↗
DOE OSTI2018

Evaluation of Nanoscale Accessible Pore Structures for Improved Prediction of Gas Production Potential in Chinese Marine Shales

The Lower Cambrian Niutitang and Lower Silurian Longmaxi shales in the Upper Yangtze Platform (UYP) are the most promising strata for shale gas exploration in China. Knowledge of the nanoscale pore structure may improve the prediction of the gas production potential in Chinese marine shales. A systematic investigation of the pore accessibility and its impact on methane adsorption capacity has been conducted on shale samples using various techniques including geochemical and mineralogical analyses, field-emission scanning electron microscopy (FE-SEM), small-angle neutron scattering (SANS), helium porosimetry, and methane adsorption. The results show that organic matter (OM) pores with various shapes dominate the pore systems of these shales. OM tended to mix with clay minerals and converted to organoclay complexes, developing plentiful micro- and mesopores. A unified fit model with two pore structures, fractal pores and finite pores, was used to model the SANS data to characterize the pore structure of the shales. Both mass and surface fractals are identified for each pore structure. The total porosity estimated by the Porod invariant method ranges between 2.35 and 16.40%, of which the porosity for finite pores ranges between 0.35 and 6.36%, and the porosity for the fractal pores ranges between 2.07 and 8.51%. The fraction of open pores was evaluated by comparing the porosities estimated by He porosimetry and SANS. We find that the fraction of open pores is higher than 64% for most of these shales. Correlation analyses suggest that clay and total organic carbon (TOC) have opposite effects on pore structure and methane adsorption capacity. Samples with higher clay contents have higher pore accessibility and lower total porosity, surface area, and maximum methane adsorption, whereas samples with higher TOC content show the inverse relationships. The high percentage of open pores may reduce methane adsorption capacity in these shales, whereas low pore accessibility may reduce methane production at specific pressure differences. Furthermore, both TOC and pore accessibility may be essential controlling factors in methane production from shale gas reservoirs.

03 NATURAL GAS↗
DOE OSTI2025

Fluoropolymer Composites from Partially Perfluoroalkylated Waste Polyethylene

Chemically modified plastics have emerged as practical solutions to plastic waste increases. Here, the inherent novelty of decorating polymer chains with chemical functionality results in distinct properties that expand the available application space. Nevertheless, developing designer materials for specific applications beyond compatibilization or mild property enhancement is difficult due to the synergistic effects of both the polar functionality imparted and the parent materials' intrinsic properties. By incorporating perfluoro-alkyl side-chains onto the backbone of dehydrogenated waste HDPE, unique surface properties intermediate between polytetrafluoroethylene (PTFE, the model fluoropolymer) and HDPE become apparent, while the overall material mechanical and thermal properties result in more LLDPE-like materials. This is demonstrated through moderate decreases in the surface free energy of the perfluoroalkylated polyolefin surface (increase in H 2 O contact angle of ~ 6°) and increased ordering under shear when blended with PTFE nanoparticles where the crossover point occurred at higher strains. Critically, perfluoroalkylated HDPE possesses improved rheological modification properties at elevated temperatures with PTFE nanoparticles, resulting in more thermally robust and stable composite materials.

fluoropolymer↗
DOE OSTI2019

Passively Sampled Environmental Films Show Geographic Variability and Host a Variety of Microorganisms

Environmental films are ubiquitous coatings that form on virtually all surfaces exposed to the troposphere. This work analyzes large molecular weight species in native environmental films acquired at three collection sites in the vicinity of Iowa City, Iowa, USA that include an urban residence, a wooded area, and a metropolitan building rooftop. The films are developed for up to twelve months on polished silicon wafer substrates housed in flat-plate passive samplers. Film composition was assessed by Fourier transform ion cyclotron resonance secondary ion mass spectrometry (FTICR-SIMS), and spatially resolved chemical morphology was analyzed via timeof- flight secondary ion mass spectrometry (ToF-SIMS) imaging. Mass analysis spanning up to m/z 2,000 included detection of a variety of organic and inorganic compounds. We characterize MS data with PCA to identify variability between the collection sites and the respective films generated therein, where we detected a variety of organic and biotically generated species consistent with fungal and bacterial microorganisms. Here, the film collection site as well as deposition time contribute to spectral variability, suggesting that geographic location as well as seasonal diversity may affect composition. SIMS analyses were complemented with microscopy and SYBR-Safe staining to highlight biological microorganisms within the films, which include evidence of fungi, protozoa, and bacteria. SIMS imaging also reveals domains concentrated with fragments indicative of proteinaceous macromolecules and microbial biomass, characteristic of biological presence within the film.

42 ENGINEERING↗
DOE OSTI2017

31 P NMR Chemical Shifts of Solvents and Products Impurities in Biomass Pretreatments

The identification of chemical impurities is crucial in elucidating the structures of biorefinery products using nuclear magnetic resonance (NMR) spectroscopic analysis. In the current biorefinery platform, contaminants derived from pretreatment solvents and decomposition byproducts may lead to misassignment of the NMR spectra of biorefinery products (e.g, lignin and bio-oils). Therefore, we investigated in this paper 54 commonly reported compounds including alcohols, carbohydrates, organic acids, aromatics, aldehydes, and ionic liquids associated with biomass pretreatment using 31 P NMR. The chemical shifts of these chemicals after derivatizing with 2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane (TMDP) were provided. Finally, the 31 P NMR signals of these derivatives could serve as valuable and informative spectral data in characterizing lignocellulose-based compounds.

09 BIOMASS FUELS↗
DOE OSTI2013

Effective conductivity and permittivity of unsaturated porous materials in the frequency range 1 mHz-1GHz

A model combining low-frequency complex conductivity and high-frequency permittivity is developed in the frequency range from 1 mHz to 1 GHz. The low-frequency conductivity depends on pore water and surface conductivities. Surface conductivity is controlled by the electrical diffuse layer, the outer component of the electrical double layer coating the surface of the minerals. The frequency dependence of the effective quadrature conductivity shows three domains. Below a critical frequency f p , which depends on the dynamic pore throat size Λ, the quadrature conductivity is frequency dependent. Between f p and a second critical frequency f d , the quadrature conductivity is generally well described by a plateau when clay minerals are present in the material. Clay-free porous materials with a narrow grain size distribution are described by a Cole-Cole model. The characteristic frequency f d controls the transition between double layer polarization and the effect of the high-frequency permittivity of the material. The Maxwell-Wagner polarization is found to be relatively negligible. For a broad range of frequencies below 1 MHz, the effective permittivity exhibits a strong dependence with the cation exchange capacity and the specific surface area. At high frequency, above the critical frequency f d , the effective permittivity reaches a high-frequency asymptotic limit that is controlled by the two Archie’s exponents m and n like the low-frequency electrical conductivity. The unified model is compared with various data sets from the literature and is able to explain fairly well a broad number of observations with a very small number of textural and electrochemical parameters. It could be therefore used to interpret induced polarization, induction-based electromagnetic methods, and ground penetrating radar data to characterize the vadose zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. We develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. Here we design a generalized preconditioned Crank-Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank-based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two-dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Samarium: from a distorted-fcc phase to melting under dynamic compression using in-situ x-ray diffraction

Abstract Lattice and electronic structure interactions for f -electrons are fundamental challenges for lanthanide equation of state development. Difficulties in first-principles calculations, such as density functional theory (DFT), emphasize the need for well-characterized experimental data. Here, we measure in-situ x-ray diffraction of shocked samarium (Sm) and temperature along the Hugoniot for the first time, providing direct evidence for phase transitions. We report direct evidence of a distorted fcc (dfcc) phase at 23 GPa. Shocked samarium melts from the dfcc phase starting at 33 GPa (1333 K), with complete melt at 40 GPa (1468 K). Previous work indicated shock melt at 27 GPa (1200 K), underscoring the significance of x-ray measurements for detecting phase transitions. Interestingly, our observed melting is in sharp contrast with the melting reported by a diamond anvil cell study. These experimental data can tightly constrain first principles calculations and serve as key touchstones for equation of state modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2016

Dynamic colloidal assembly pathways via low dimensional models

Here we construct a low-dimensional Smoluchowski model for electric field mediated colloidal crystallization using Brownian dynamic simulations, which were previously matched to experiments. Diffusion mapping is used to infer dimensionality and confirm the use of two order parameters, one for degree of condensation and one for global crystallinity. Free energy and diffusivity landscapes are obtained as the coefficients of a low-dimensional Smoluchowski equation to capture the thermodynamics and kinetics of microstructure evolution. The resulting low-dimensional model quantitatively captures the dynamics of different assembly pathways between fluid, polycrystal, and single crystals states, in agreement with the full N-dimensional data as characterized by first passage time distributions. Numerical solution of the low-dimensional Smoluchowski equation reveals statistical properties of the dynamic evolution of states vs. applied field amplitude and system size. The low-dimensional Smoluchowski equation and associated landscapes calculated here can serve as models for predictive control of electric field mediated assembly of colloidal ensembles into two-dimensional crystalline objects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Modeling single molecule junction mechanics as a probe of interface bonding

Using the atomic force microscope based break junction approach, applicable to metal point contacts and single molecule junctions, measurements can be repeated thousands of times resulting in rich data sets characterizing the properties of an ensemble of nanoscale junction structures. This paper focuses on the relationship between the measured force extension characteristics including bond rupture and the properties of the interface bonds in the junction. We analyzed a set of exemplary model junction structures using density functional theory based calculations to simulate the adiabatic potential surface that governs the junction elongation. The junction structures include representative molecules that bond to the electrodes through amine, methylsulfide, and pyridine links. The force extension characteristics are shown to be most effectively analyzed in a scaled form with maximum sustainable force and the distance between the force zero and force maximum as scale factors. Widely used, two parameter models for chemical bond potential energy versus bond length are found to be nearly identical in scaled form. Furthermore, they fit well to the present calculations of N–Au and S–Au donor-acceptor bonds, provided no other degrees of freedom are allowed to relax. Examination of the reduced problem of a single interface, but including relaxation of atoms proximal to the interface bond, shows that a single-bond potential form renormalized by an effective harmonic potential in series fits well to the calculated results. This, then, allows relatively accurate extraction of the interface bond energy. Analysis of full junction models shows cooperative effects that go beyond the mechanical series inclusion of the second bond in the junction, the spectator bond that does not rupture. Calculations for a series of diaminoalkanes as a function of molecule length indicate that the most important cooperative effect is due to the interactions between the dipoles induced by the donor-acceptor bond formation at the junction interfaces. The force extension characteristic of longer molecules such as diaminooctane, where the dipole interaction effects drop to a negligible level, accurately fit to the renormalized single-bond potential form. Our results suggest that measured force extension characteristics for single molecule junctions could be analyzed with a modified potential form that accounts for the energy stored in deformable mechanical components in series.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Development of an Index of Biotic Integrity for the Sand Hills Ecoregion of the Southeastern United States

Abstract We developed an index of biotic integrity (IBI) for the Sand Hills ecoregion within the upper Coastal Plain of the southeastern United States. This ecoregion exhibits differences in species composition among river basins and possesses a relatively depauperate fish fauna that has been affected by faunal homogenization. We also investigated whether this IBI could be improved by adding benthic macroinvertebrate information. Data collected from 70 wadeable stream sites were used to calculate 45 fish assemblage metrics. Twelve metrics within the categories of species richness, species composition, habitat guild, trophic guild, tolerance level, alien/invasive species, and individual fish condition were retained after screening all metrics for sensitivity to disturbance, redundancy, and ecological content. Heterogeneity of species composition among basins led to a greater reliance on richness, indicator species, trophic guild, and habitat guild metrics rather than on more geographically variable species composition metrics. Characteristic headwater assemblages provided the basis for some metrics, and invader species metrics were included to address faunal homogenization. The Sand Hills IBI (SHIBI) was more accurate ecoregion‐wide than IBIs that were developed for portions of the Sand Hills, correctly classifying about 60% of the disturbed sites compared with less than 30% for the other IBIs. However, the SHIBI was not highly responsive to gradients in site quality, as indicated by an R 2 of 0.30 between SHIBI scores and instream habitat quality scores. This R 2 value was improved to 0.49 by supplementing the SHIBI with a benthic macroinvertebrate index (the Ephemeroptera, Plecoptera, and Trichoptera index). We conclude that ecoregion‐wide IBIs should (1) be based on ecoregion‐wide data that characterize responses to disturbance and natural habitat gradients, (2) maintain a substantial ecological basis by including several metric classes, and (3) include metrics that address faunal homogenization where disturbance has increased the dispersion of nonnative species and native generalist species. In addition, combining information from different assemblages can enhance IBI responsiveness to disturbance and improve performance. Received May 5, 2016; accepted September 19, 2016 Published online December 8, 2016

Paller, Michael H.↗
DOE OSTI2023

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization. There is a lack of understanding on the impact of spatial scale, semantic granularity, and geographic context on POI-based land use modeling, particularly large-scale land use modeling. In this study, we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions. We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources. Then, by combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

58 GEOSCIENCES↗