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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Neurocognitive and physical functioning in the Seveso Women's Health Study

Highlights: • Little research exists on the neurotoxic effects of dioxins in older women. • TCDD was associated with grip strength but not other measures of physical function. • TCDD exposure was not adversely associated with working memory. • This relationship was not modified by menopause nor timing of TCDD exposure around menarche. • TCDD susceptibility may increase as the women age, warranting further research. 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is neurotoxic in animals but few studies have investigated its effects on the human brain. Related dioxin-like compounds have been linked to poorer cognitive and motor function in older adults, with effects more pronounced in women, perhaps due to the loss of neuro-protective estrogen in menopause. On 10 July 1976, a chemical explosion in Seveso, Italy, resulted in one of the highest known residential exposures to TCDD. In 1996, we initiated the Seveso Women's Health Study, a retrospective cohort study of the health of the women who were newborn to 40 years old in 1976. Here, we investigate whether TCDD exposure is associated with physical functioning and working memory more than 20 years later. Individual TCDD concentration (ppt) was measured in archived serum collected soon after the explosion. In 1996 and 2008, we measured physical functioning (n=154) and working memory (n=459), respectively. We examined associations between serum TCDD and motor and cognitive outcomes with multivariate linear regression and semi-parametric estimators. A 10-fold increase in serum TCDD was not associated with walking speed (adjusted β=0.0006 ft/s, 95% Confidence Interval (CI): −0.13, 0.13), upper body mobility (adjusted β=−0.06, 95% CI: −0.36, 0.23), or manual dexterity (adjusted β=0.34, 95% CI: −0.65, 1.33). We observed an inverted U-shaped association in grip strength, with poorer strength in the lowest and highest TCDD exposure levels. There was no association between TCDD and the Wechsler digit and spatial span tests. Neither menopause status at assessment nor developmental timing of exposure modified associations between TCDD and working memory. Our findings, in one of the only studies of TCDD's effects on neuropsychological and physical functioning in women, do not indicate an adverse effect on these domains, with the exception of a U-shaped relationship with grip strength. Given the limited assessment and relative youth of the women at this follow-up, future work examining additional neuropsychological outcomes is warranted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding fundamental effects of biofuel structure on ignition and physical fuel properties

The development of biochemicals and biofuels with advantaged properties of higher combustion efficiency at lower emission levels can be aided by a priori prediction of critical global characteristics based on molecular structure. Existing predictive techniques are incomplete for comprehensive ignition and physical fuel property prediction. Here this investigation focuses on filling this knowledge gap by developing a priori prediction techniques for predicting important ignition as well as physical fuel properties of promising advantaged biofuels. We demonstrate the utility of this a priori prediction technique on saturated (≤C 5 ) alcohols, both linear and branched in nature by correlating their molecular structure with both important ignition and physical properties of the fuels. This work utilizes a semi-automated method of predicting ignition characteristics via accurate fundamental-based calculations of the fuel radical cascades that govern low-temperature combustion. This numerical methodology is supported by the relevant fuel ignition delay time (IDT) and research octane number (RON) data experimentally measured by the Advanced Fuel Ignition Delay Analyzer (AFIDA). Phyiscal fuel property trends are also analyzed by correlating the molecular structure of the fuel to its physical characteristics. Based on their molecular structure, recommendations are made on an a priori method for selecting biofuels that have advantaged properties for the selected fuel application. Lastly, the utility of this investigation is extended beyond the subset of alcohols considered by analyzing the IDT trends of i-Pentanol and n-Hexanol, revealing the fidelity of the novel kinetic modelling approach and the accuracy of the predicted trends in this study.

09 BIOMASS FUELS↗
DOE OSTI2020

Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches

Permeability prediction has been an important problem since the time of Darcy. Most approaches to solve this problem have used either idealized physical models or empirical relations. In recent years, machine learning (ML) has led to more accurate and robust, but less interpretable empirical models. Using 211 core samples collected from 12 wells in the Garn Sandstone from the North Sea, this study compared idealized physical models based on the Carman-Kozeny equation to interpretable ML models. We found that ML models trained on estimates of physical properties are more accurate than physical models. The results show evidence of a threshold of about 10% volume fraction, above which pore-filling cement strongly affects permeability.

03 NATURAL GAS↗
DOE OSTI2016

Physical and hydraulic properties of modern sinter deposits: El Tatio, Atacama

We report sinters are siliceous, sedimentary deposits that form in geothermal areas. Formation occurs in two steps. Hot water circulates in the subsurface and dissolves silica from the host rock, usually rhyolites. Silica then precipitates after hot water is discharged and cools. Extensive sinter formations are linked to up-flow areas of fluids originating from high temperature (> 175 °C) deep reservoirs. Fluid geochemistry, microbial communities, and environmental conditions of deposition determine the texture of sinter and pore framework. Porosity strongly influences physical and hydraulic properties of rocks. To better understand the properties controlling the transport of fluids, and interpret geophysical observations in geothermal systems, here we studied 17 samples of modern geyserite sinter deposits (< 10 ka) from the active El Tatio geothermal field in northern Chile. We measured the physical properties (hydraulic, seismic, and electrical), and internal microstructure (using μX-Ray computed tomography). We find that the pore structure, and thus hydraulic and physical properties, is controlled by the distribution of microbial matter. Based on velocity-porosity relationships, permeability-porosity scaling, and image analysis of the 3D pore structure; we find that the physical and hydraulic properties of sinter more closely resemble those of vesicular volcanic rocks and other material formed by precipitation in geothermal settings (i.e., travertine) than clastic sedimentary rocks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Physical and absorption properties of titanium nanoparticles incorporated into zinc magnesium phosphate glass

We report the influences of Titania (TiO{sub 2}) nanoparticles (NPs) on the physical and optical properties of melt quench synthesized zinc magnesium phosphate glasses. Five glass samples with composition (42 − x)P{sub 2}O{sub 5}–50ZnO–8MgO–xTiO{sub 2}, where x = 0, 1, 2, 3, 4 mol% are prepared and characterized. XRD pattern verified the amorphous nature of all samples. TEM images manifested the growth of Ti NPs of average size ≈ 5.78 nm. TiO{sub 2} NP concentration dependent variation in the physical properties including glass density, molar volume, molar refractivity, electronic polarizability and ionic packing density are determined. The values of glass refractive indices, density and ionic packing density are increased with the increase of TiO{sub 2} NP contents. Conversely, the Urbach energy, direct and indirect optical band gap are found to decrease with the increase of TiO{sub 2} NP concentration. These glass compositions may be potential for various solid state devices including laser. - Highlights: • TiO{sub 2} NP embedded self-cleaning phosphate glass are synthesized for the first time. • Well dispersed and uniform sized TiO{sub 2} NPs are grown. • Absorption spectra revealed prominent peak in the UV region. • TiO{sub 2} NPs strongly influenced the physical and absorption features of synthesized glasses. • The effects of TiO{sub 2} NPs on the physical and optical properties of these glasses are determined.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Micrometer-Scale Physical Structure and Microbial Composition of Soil Macroaggregates

Soil macroaggregates are discrete, separable units of soil that we hypothesize contain smaller assemblages of microorganisms than bulk soil, and represent a scale potentially consistent with naturally occurring microbial communities. We posed two questions to explore microbial community composition in the context of the macroaggregate: 1) Is there a relationship between macroaggregate physical structure and microbial community composition in individual macroaggregates? And, 2) How similar are the bacterial communities in individual sub-millimeter soil macroaggregates sampled from the same 5-cm core? To address these questions, individual macroaggregates of three arbitrary size classes (250–425, 425–841, and 841–1000 μm) were sampled from a grassland field. The physical structures of 14 individual macroaggregates were characterized using synchrotron-radiation based transmission X-ray tomography, revealing that a greater proportion of the pore space in the small- and medium-sized macroaggregates is as relatively smaller pores, resulting in greater overall porosity and pore–mineral interface area in these smaller macroaggregates. Microbial community composition was characterized using 16S rRNA pyrosequencing data. Rarefaction analyses indicated that the membership of each macroaggregate was sufficiently sampled with only a few thousand sequences; in addition, the community membership varied widely between macroaggregates and the structure varied from those communities strongly dominated by a few phylotypes to communities that were evenly distributed among several phylotypes. We found no strong relationship of physical structure with community membership; this may be due to the low number of aggregates (10) for which we have both physical and biological data. Our results do support our initial expectation that individual macroaggregate communities were significantly less diverse than bulk soil from the same grassland field site.

16S↗
DOE OSTI2017

Rapid Assessment of the Physical Stability of Amorphous Solid Dispersions

Amorphous solid dispersions (ASDs) can potentially increase the apparent solubility and thereby the oral bioavailability of poorly soluble compounds. Furthermore, their physical instability, i.e., potential to crystallize, is a major concern. Our objective was to explore methods for rapid assessment of the physical stability of ASDs. Ketoconazole ASDs were prepared with each of the three polymers, poly(acrylic acid) (PAA), poly(2-hydroxyethyl methacrylate) (PHEMA), and polyvinylpyrrolidone (PVP). The physical stability of these ASDs was evaluated after exposure to 90% RH (25 °C) in an automated water sorption analyzer. The onset time for ketoconazole crystallization, induced by water sorption, was rank ordered as PAA > PHEMA > PVP. Additionally, the ability of the polymers to inhibit crystallization on contact with aqueous medium was studied by powder X-ray diffractometry using synchrotron radiation. In this case, the rank ordering was PAA ~ PHEMA > PVP. To check the validity of our approach, the long-term stability of ketoconazole in ASDs was evaluated in the glassy state, both at 25 and at 40 °C, and was rank ordered as PAA > PHEMA > PVP. Crystallization, accelerated by water vapor sorption, can serve as an effective preliminary screening tool to rapidly evaluate and rank order the physical stability of ASDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Physically Informed Machine Learning Prediction of Electronic Density of States

The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. Still, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

Development of new generation electronic devices requires understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite–cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Finally and furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Phases and interfaces from real space atomically resolved data: Physics-based deep data image analysis

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ~1–10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. Furthermore, this method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure–property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois

Particle chemical composition affects aerosol optical and physical properties in ways important for the fate, transport, and impact of atmospheric particulate matter. For example, hygroscopic constituents take up water to increase the physical size of a particle, which can alter the extinction properties and atmospheric lifetime. At the collocated AERosol RObotic NETwork (AERONET) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) network monitoring stations in rural Bondville, Illinois, we employ a novel cloudiness determination method to compare measured aerosol physicochemical properties on predominantly cloudy and clear sky days from 2010 to 2019. On cloudy days, aerosol optical depth (AOD) is significantly higher than on clear sky days in all seasons. Measured angstrom ngstro''m exponents are significantly smaller on cloudy days, indicating physically larger average particle size for the sampled populations in all seasons except winter. Mass concentrations of fine particulate matter that include estimates of aerosol liquid water (ALW) are higher on cloudy days in all seasons but winter. More ALW on cloudy days is consistent with larger particle sizes inferred from angstrom ngstro''m exponent measurements. Aerosol chemical composition that affects hygroscopicity plays a determining impact on cloudy versus clear sky differences in AOD, angstrom ngstro''m exponents, and ALW. This work highlights the need for simultaneous collocated, high-time-resolution measurements of both aerosol chemical and physical properties, in particular at cloudy times when quantitative understanding of tropospheric composition is most uncertain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Physical Orphaning versus Chemical Instability: Is Dendritic Electrodeposition of Li Fatal?

The dendritic electrodeposition of lithium, leading to physical orphaning and chemical instability, is considered responsible for the poor reversibility and premature failure of electrochemical cells that utilize Li metal anodes. Herein we critically assess the roles of physical orphaning and chemical instability of electrodeposited Li on electrode reversibility using planar and non-planar electrode architectures. The non-planar electrodes allow the morphology of electrodeposited Li to be interrogated in detail and in the absence of complications associated with cell stacking pressure. We find that physical orphaning is a key determinant of the poor reversibility of Li. We report further that fiber-like, dendritic electrodeposition is an intrinsic characteristic of Li —irrespective of the electrolyte solvent chemistry. With guaranteed electronic access to prevent physical loss, we finally show that a Li metal electrode exhibits high levels of reversibility (99.4% CE), even when the metal electrodeposits are in obvious, dendritic morphologies. We take advantage of these findings to create high-loading (7 mAh/cm 2 ) Li||LFP full cells with nearly unity N:P ratio and demonstrate that these cells exhibit good reversibility.

25 ENERGY STORAGE↗
DOE OSTI2014

Physical insight into switchgrass dissolution in the ionic liquid 1-ethyl-3-methylimidazolium acetate

Small-angle neutron scattering was used to characterize solutions of switchgrass and the constituent biopolymers cellulose, hemicellulose, and lignin, as well as a physical mixture of them mimicking the composition of switchgrass, dissolved in the ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate. The results demonstrate that the IL dissolves the cellulose fibrils of switchgrass, although a supramolecular biopolymer network remains that is not present in solutions of the individual biopolymers and that does not self-assemble in a solution containing the physical mixture of the individual biopolymers. The persistence of a network-like structure indicates that dissolving switchgrass in the IL does not disrupt all of the physical entanglements and covalent linkages between the biopolymers created during plant growth. Reconstitution of the IL-dissolved switchgrass yields carbohydrate-rich material containing cellulose with a low degree of crystallinity, as determined by powder X-ray diffraction, which impacts potential down-stream uses of the biopolymers produced by the process. The data suggests that the use of chemical additives which would break bonds that exist between the lignin and hemicellulose might improve the purity of the resulting product, but may not be able to disrupt the highly physically-entangled biopolymer network sufficiently to facilitate their separation.

Wang, Hui↗
DOE OSTI2024

The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM

Abstract A significant portion of surface melt on the Greenland Ice Sheet (GrIS) is due to dark ice regions in the ablation zone, where solar absorption is influenced by the physical properties of the ice, light absorbing constituents (LACs), and the overlying crustal surface or melt ponds. Earth system models (ESMs) typically prescribe the albedo of ice surfaces as a constant value in the visible and near‐infrared spectral regions. This work advances ESM ice radiative transfer modeling by (a) incorporating a physically based radiative transfer model (SNow, ICe and Aerosol Radiation model Adding‐Doubling Version 4; SNICAR‐ADv4) into the Energy Exascale Earth System Model (E3SM), (b) determining spatially and temporally varying bare ice physical properties over the GrIS ablation zone from satellite observations to inform SNICAR‐ADv4, and (c) assessing the impacts on simulated GrIS albedo and surface mass balance associated with modeling of more realistic bare ice albedo. GrIS‐wide bare ice albedo in E3SMv2 is overestimated by ∼4% in the visible and ∼7% in the near‐infrared wavelengths compared to the Moderate Resolution Imaging Spectroradiometer. Our bare ice physical property retrieval method found that LACs, ice crustal surfaces, and melt ponds reduce visible albedo by 30% in the bare ice region of the GrIS ablation zone. The realistic bare ice albedo reduces surface mass balance by ∼145 Gt, or 0.4 mm of sea‐level equivalent between 2000 and 2021 compared to the default E3SM. This work highlights the importance of simulating bare ice albedo accurately and realistically to improve our ability to quantify changes in the GrIS surface mass and radiative energy budgets.

58 GEOSCIENCES↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2024

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Sub genome anchored physical frameworks of the allotetraploid Upland cotton (Gossypium hirsutum L.) genome, and an approach toward reference-grade assemblies of polyploids

Like those of many agricultural crops, the cultivated cotton is an allotetraploid and has a large genome (~2.5 gigabase pairs). The two sub genomes, A and D, are highly similar but unequally sized and repeat-rich, which pose significant challenges for accurate genome reconstruction using standard approaches. Here we report the development of BAC libraries, sub genome specific physical maps, and a new-generation sequencing approach that will lead to a reference-grade genome assembly for Upland cotton. Three BAC libraries were constructed, fingerprinted, and integrated with BAC-end sequences (BES) to produce a de novo whole-genome physical map. The BAC map was partitioned by sub genomes through alignment to the diploid progenitor D-genome reference sequence with densely spaced BES anchor points and computational filtering. The physical maps were validated with FISH and genetic mapping of SNP markers derived from BES. Two pairs of homeologous chromosomes, A11/D11 and A12/D12, were used to assess multiplex sequencing approaches for completeness and scalability. The results represent the first sub genome anchored physical maps of Upland cotton, and a new-generation approach to the whole-genome sequencing, which will lead to the reference-grade assembly of allopolyploid cotton and serve as a general strategy for sequencing other polyploid species.

59 BASIC BIOLOGICAL SCIENCES↗