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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicate that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Cholesterol-induced suppression of Kir2 channels is mediated by decoupling at the inter-subunit interfaces

Cholesterol is a major regulator of multiple types of ion channels. Although there is increasing information about cholesterol binding sites, the molecular mechanisms through which cholesterol binding alters channel function are virtually unknown. In this study, we used a combination of Martini coarse-grained simulations, a network theory-based analysis, and electrophysiology to determine the effect of cholesterol on the dynamic structure of the Kir2.2 channel. We found that increasing membrane cholesterol reduced the likelihood of contact between specific regions of the cytoplasmic and transmembrane domains of the channel, most prominently at the subunit-subunit interfaces of the cytosolic domains. This decrease in contact was mediated by pairwise interactions of specific residues and correlated to the stoichiometry of cholesterol binding events. The predictions of the model were tested by site-directed mutagenesis of two identified residues—V265 and H222—and high throughput electrophysiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

MjCyc: Rediscovering the pathway-genome landscape of the first sequenced archaeon, Methanocaldococcus (Methanococcus) jannaschii

The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Synthesis, structural and electrical properties of [C{sub 2}H{sub 10}N{sub 2}][(SnCl(NCS){sub 2}]{sub 2}

Synthesis, structural and electrical properties are given for a new organic stannous pseudo halide material. The structure of the [C{sub 2}H{sub 10}N{sub 2}][(SnCl(NCS){sub 2}]{sub 2} reveals that the adjacent Sn(II) centres are bridged by a pair of SCN{sup -} anions to form a 1-D array giving rise to the anionic chains (SnCl(NCS){sub 2}){sub n}{sup n-}. These chains are themselves interconnected by means of N-H Horizontal-Ellipsis Cl(S) hydrogen bonds originating from the organic cation [(NH{sub 3}){sub 2}(CH{sub 2}){sub 2}]{sup 2+}. The AC impedance measurements were performed as a function of both frequency and temperature. The electrical conduction and dielectric relaxation have been studied. The activation energy associated with the electrical relaxation determined from the electric modulus spectra was found close to that of the activation energy obtained for DC conductivity. The conduction mechanisms are attributed to the quantum mechanical tunneling model in phase I and to the proton hopping among hydrogen vacancies in phase II. - Graphical abstract: Atomic coordination in [C2H10N2][SnCl(NCS)2)2]. Highlights: Black-Right-Pointing-Pointer X-ray diffraction analysis shows the 1D network character of the structure. Black-Right-Pointing-Pointer DSC experiments show a phase transition at 336 K. Black-Right-Pointing-Pointer The AC conductivity is interpreted in terms of Jonsher's law. Black-Right-Pointing-Pointer Two conduction mechanisms are proposed for phase I and II.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Synthesis and characterization of aluminophosphate glasses with unique blue emission

Highlights: • Unique blue emission from aluminophosphate glass due to the phosphorus defect. • Tune the blue emission intensity by addition of SiO{sub 2}, ZnF{sub 2}, or Li{sub 2}O to the base glass. • Study the structural defects in the glass by PALS. • Energy transfer from phosphorus defect to Mn{sup 2+} in Mn-doped aluminophosphate glass. - Abstract: The aluminophosphate glasses were prepared by the melting-quenching technique and their optical and structural properties were investigated. The 56P{sub 2}O{sub 5}-20Al{sub 2}O{sub 3} glass exhibits unique blue emission at 406 nm due to the phosphorus defect in the glass matrix. Addition of SiO{sub 2}, ZnF{sub 2}, or Li{sub 2}O to 56P{sub 2}O{sub 5}-20Al{sub 2}O{sub 3} glass matrix has different influence on formation of phosphorus defect resulting in different intensities of blue emission at 406 nm, though SiO{sub 2}, ZnF{sub 2} or Li{sub 2}O mostly enters the glass network as modifier. Structural analysis of P{sub 2}O{sub 5}-Al{sub 2}O{sub 3} based aluminophosphate glasses by positron annihilation lifetime spectroscopy indicates that values of positron lifetimes and the corresponding intensities may be connected with the sizes and number of structural defects. Under excitation of 251 nm, a broad blue emission band and a red emission band were observed in the Mn-doped 56P{sub 2}O{sub 5}-20Al{sub 2}O{sub 3}-15SiO{sub 2}-10ZnF{sub 2} glass, indicating the operation of energy transfer from the phosphorus defect to octahedrally coordinated Mn in the glass host.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Copper–Carbon Nanotube Composites Enabled by Electrospinning for Advanced Conductors

The power losses associated with the electrical resistance of copper (Cu) have generated considerable interest in the development of advanced conductors that incorporate carbon nanotubes (CNTs) into the Cu matrix—ultraconductive Cu (UCC) composites—to increase energy efficiency in various industrial and residential applications, ranging from electric power transmission and rotating machinery to electronic devices. To meet this demand, we describe an electrospinning-based polymer nanofiber templating strategy for the fabrication of UCC composites with electrical and mechanical performance exceeding that of Cu. Our approach involves electrospinning of polyvinylpyrrolidone (PVP)-based solutions containing CNTs into aligned PVP/CNT nanofibers onto Cu foil substrates, followed by vacuum-assisted thermal removal of organic solvent/polymer from the CNT matrix to achieve a uniformly distributed CNT layer on the Cu surface. Following additional Cu deposition, the Cu–CNT–Cu composites demonstrated similar electrical conductivity, higher current carrying capacity, and improved mechanical properties compared with those obtained from reference Cu. Importantly, after the heat treatment, Raman analysis of the CNT network displayed an increased metallic character that supports the enhanced electrical properties of the UCC composites. Thus, we believe that these performance characteristics together with the commercial viability of the present approach could open new possibilities in designing advanced conductors for a broad range of electrical systems and industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automated Electric Vehicle Fleet Operations for On-Demand Service: Challenges and Opportunities

Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Design methodology and performance analysis of a wideband 90° phase switch for radiometer applications

This paper presents the analysis, design, and characterization of a wideband 90° phase switch in Ka-band. The phase switch is based on two microstrip bandpass filters in which the commutation is performed by a novel single-pole double-throw (SPDT) switch. The analysis of π-network bandpass filters is provided, obtaining the phase difference and amplitude imbalance between filters and their scattering parameters; tested results show an average phase difference of 88.9° ± 5° and an amplitude imbalance of 0.15 dB from 24 to 37 GHz. The new broadband SPDT switch is based on a coplanar waveguide-to-slotline-to-microstrip structure, which enables a full planar integration with shifting branches. PIN diodes are used to perform the switching between outputs. The SPDT shows isolation better than 19 dB, insertion loss of around 1.8 dB, and return loss better than 15 dB. The full integration of the phase switch achieves a return loss better than 11 dB and insertion loss of around 4 dB over the band 26–36 GHz, with an average phase difference of 87.1° ± 4° and an average amplitude imbalance of 0.3 dB. It provides an excellent performance for this frequency range, suitable for radio-astronomy receivers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Complete characterization by Raman spectroscopy of the structural properties of thin hydrogenated diamond-like carbon films exposed to rapid thermal annealing

We have demonstrated that multi-wavelength Raman and photoluminescence spectroscopies are sufficient to completely characterize the structural properties of ultra-thin hydrogenated diamond-like carbon (DLC:H) films subjected to rapid thermal annealing (RTA, 1 s up to 659 °C) and to resolve the structural differences between films grown by plasma-enhanced chemical vapor deposition, facing target sputtering and filtered cathodic vacuum arc with minute variations in values of mass density, hydrogen content, and sp³ fraction. In order to distinguish unequivocally between films prepared with different density, thickness, and RTA treatment, a new method for analysis of Raman spectra was invented. This newly developed analysis method consisted of plotting the position of the Raman G band of carbon versus its full width at half maximum. Moreover, we studied the passivation of non-radiative recombination centers during RTA by performing measurements of the increase in photoluminescence in conjunction with the analysis of DLC:H networks simulated by molecular dynamics. The results show that dangling bond passivation is primarily a consequence of thermally-induced sp² clustering rather than hydrogen diffusion in the film.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Three-dimensional analysis by serial sectioning of cubic zirconia sinters

Three-dimensional electron backscatter diffraction technique was used for the characterization of grain boundary geometry and pore morphology in cubic zirconia. A set of three samples composed of cubic yttria stabilized zirconia, sintered under different conditions was investigated. Analysis of grain boundaries and pore structure was carried out in a dual-beam scanning electron microscope. For each sample, a volume of 15·10{sup 3} μm{sup 3} was investigated. The results of three-dimensional microstructure analysis were compared to the results derived from regular, two-dimensional maps of the area of 2500 µm{sup 2}. Based on two-dimensional and three-dimensional data the average grain diameter, number of grains, average number of neighbors and porosity were calculated. The average grain diameter varied in the range from 2.39 µm to 2.91 µm and from 3.00 µm to 3.79 µm, while the level of porosity varied in the range from 1.22% to 1.77% and from 1.30% to 5.61% for two-dimensional and three-dimensional data, respectively. The analysis of grain boundary networks revealed a strong dependence between grain boundary density and sample preparation parameters. The parameters of the sintering process affected also the size and distribution of pores. The comparison of the results of 2D and 3D materials characterization revealed significant differences in the values of calculated microstructure parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks

We study the 2D Orszag–Tang vortex magnetohydrodynamics (MHD) problem through the use of physics-constrained convolutional neural networks (PCNNs) for forecasting the density, ρ, and the magnetic field, B, as well as the prediction of B given the velocity field v of the fluid. In addition to translation equivariance from the convolutional architecture, other physics constraints were embedded: absence of magnetic monopoles, non-negativity of ρ, use of only relevant variables, and the periodic boundary conditions of the problem. The use of only relevant variables and the hard constraint of non-negative ρ were found to facilitate learning greatly. The divergenceless condition ∇·B=0 was implemented as a hard constraint up to machine precision through the use of a magnetic potential to define B=∇×A. Residual networks and data augmentation were also used to improve performance. This allowed for some of the residual models to function as surrogate models and provide reasonably accurate simulations. For the prediction task, the PCNNs were evaluated against a physics-informed neural network, which had the ideal MHD induction equation as a soft constraint. Several models were able to generate highly accurate fields, which are visually almost indistinguishable and have low mean squared error. Only methods with built-in hard constraints produced physical fields with ∇·B=0. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.

2D materials↗
DOE OSTI2024

Using convolutional neural networks to detect edge localized modes in DIII-D from Doppler backscattering measurements

In H-mode tokamak plasmas, the plasma is sometimes ejected beyond the edge transport barrier. These events are known as edge localized modes (ELMs). ELMs cause a loss of energy and damage the vessel walls. Understanding the physics of ELMs, and by extension, how to detect and mitigate them, is an important challenge. In this paper, we focus on two diagnostic methods—deuterium-alpha (D α ) spectroscopy and Doppler backscattering (DBS). The former detects ELMs by measuring Balmer alpha emission, while the latter uses microwave radiation to probe the plasma. DBS has the advantages of having a higher temporal resolution and robustness to damage. These advantages of DBS diagnostic may be beneficial for future operational tokamaks, and thus, data processing techniques for DBS should be developed in preparation. In sight of this, we explore the training of neural networks to detect ELMs from DBS data, using D α data as the ground truth. With shots found in the DIII-D database, the model is trained to classify each time step based on the occurrence of an ELM event. The results are promising. When tested on shots similar to those used for training, the model is capable of consistently achieving a high f1-score of 0.93. Furthermore, this score is a performance metric for imbalanced datasets that ranges between 0 and 1. We evaluate the performance of our neural network on a variety of ELMs in different high confinement regimes (grassy ELM, RMP mitigated, and wide-pedestal), finding broad applicability. Beyond ELMs, our work demonstrates the wider feasibility of applying neural networks to data from DBS diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗