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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Black holes in Klein space

The analytic continuation of the general signature (1, 3) Lorentzian Kerr-Taub-NUT black holes to signature (2, 2) Kleinian black holes is studied. Their global structure is characterized by a toric Penrose diagram resembling their Lorentzian counterparts. Kleinian black holes are found to be self-dual when their mass and NUT charge are equal for any value of the Kerr rotation parameter a. Remarkably, it is shown that the rotation a can be eliminated by a large diffeomorphism; this result also holds in Euclidean signature. The continuation from Lorentzian to Kleinian signature is naturally induced by the analytic continuation of the S-matrix. Indeed, we show that the geometry of linearized black holes, including Kerr-Taub-NUT, is captured by (2, 2) three-point scattering amplitudes of a graviton and a massive spinning particle. This stands in sharp contrast to their Lorentzian counterparts for which the latter vanishes kinematically and enables a direct link to the S-matrix.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗
DOE OSTI2020

Localized corrosion at nm-scale hardening precipitates in Al-Cu-Li alloys

The localized corrosion of Li-containing nm hardening precipitates in the 3rd generation of Al-Cu-Li alloys was investigated based on a quasi in situ approach by sequentially exposing the material to NaCl solution and characterizing the structural, chemical, and electrochemical evolution at atomic scale using electron microscopy, spectroscopy, 3D tomography, electrochemical measurements, and DFT calculations. Localized corrosion of Al 7.5 Cu 4 Li (TB phase) initiated along {001} family of planes through the dealloying of Al and Li due to a low surface work function. Cu was enriched along the Cu (110) // T B (011) // Al (100) orientations on and around corroded T B precipitates. No strong galvanic interactions were observed at the T B and Al matrix interface due to the formation of a Li-C-O rich passivation layer during electrolyte exposure. Similarities and differences between T B and other common Al-Cu-Li precipitates (Al 2 CuLi, Al 6 CuLi 3 , and Al 3 Li) with respect to corrosion are discussed. Overall, the reported corrosion mechanism can assist in the assessment of the localized corrosion susceptibility of precipitation-hardened Al alloys and assist in the design of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A combined theoretical-experimental investigation of thermal transport in low-dose irradiated thorium dioxide

During reactor operation, nuclear fuels are subject to extreme temperature and irradiation conditions which can significantly degrade the fuel's thermal transport properties. The reduction in thermal conductivity of the fuel as a result of irradiation-induced lattice defects is arguably the most important fuel performance metric in regard to reactor efficiency and safety. Because thorium dioxide (ThO2) is suitable as a model system for more complex materials such as UO2 and its mixed oxides, we present a theoretical investigation of thermal conductivity reduction seen in defect-bearing thorium dioxide and compare directly to experimental measurements. Phonon-mediated thermal transport of the fuel is modeled by a solution to the Boltzmann transport equation (BTE) for phonons. A cluster dynamics (CD) model for lattice defect evolution during irradiation predicts defect densities which are used as input to the BTE for modeling phonon-defect scatterings. Phonon scatterings by lattice defects include those from point defects and vacancy clusters and interstitial clusters of various sizes. The CD model is benchmarked against structural defect characterization of irradiated thorium dioxide using electron microscopy. Thermal conductivity predicted by the BTE model is compared to measured values for irradiated thorium dioxide specimens below room temperature to isolate effects of phonon-defect scattering from intrinsic 3-phonon processes, which dominate at higher temperatures. The computed conductivity values are in partial agreement at temperatures close to room temperature while slight deviations are observed at the lowest measured temperatures, suggesting that implemented phonon-defect scattering cross-section expressions may not be adequate for low temperatures. The presented work provides a necessary investigation of the influence of irradiation induced defects on fuel performance and represents a first step toward a full characterization of phonon mediated thermal transport in irradiated materials with complex defect microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mass spectrometry and spectroscopic characterization of a tetrameric photosystem I supercomplex from Leptolyngbya ohadii , a desiccation-tolerant cyanobacterium

Cyanobacteria inhabiting desert biological soil crusts face the harsh conditions of the desert. They evolved a suite of strategies toward desiccation-hydration cycles mixed with high light irradiations, etc. In this study we purified and characterized the structure and function of Photosystem I (PSI) from Leptolyngbya ohadii, a desiccation-tolerant desert cyanobacterium. We discovered that PSI forms tetrameric (PSI-Tet) aggregate. We investigated it by using sucrose density gradient centrifugation, clear native PAGE, high performance liquid chromatography, mass spectrometry (MS), time-resolved fluorescence (TRF) and time-resolved transient absorption (TA) spectroscopy. MS analysis identified the presence of two PsaB and two PsaL proteins in PSI-Tet and uniquely revealed that PsaLs are N-terminally acetylated in contrast to non-modified PsaL in the trimeric PSI from Synechocystis sp. PCC 6803. Chlorophyll (Chl) a fluorescence decay profiles of the PSI-Tet performed at 77 K revealed two emission bands at ∼690 nm and 725 nm with the former appearing only at early delay time. The main fluorescence emission peak, associated with emission from the low energy Chls a, decays within a few nanoseconds. TA studies demonstrated that the 725 nm emission band is associated with low energy Chls a with absorption band clearly resolved at ∼710 nm at 77 K. As a result, our work suggests that the heterogenous composition of PsaBs and PsaL in PSI-Tet is related with the adaptation mechanisms needed to cope with stressful conditions under which this bacterium naturally grows.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Towards fast-charging high-energy lithium-ion batteries: From nano- to micro-structuring perspectives

Electric vehicles (EVs) have been playing an indispensable role in reducing greenhouse gas emissions for our modern society. However, current EVs are difficult to meet people’s diverse travel needs, especially in long endurance and fast-charging capacities. At the heart of this issue is the physicochemical limit of current lithium-ion batteries (LIBs), which are the core parts for powering the vehicles. Hence, LIBs with simultaneous high energy and power are critically required to further promote the development of EVs. Here, in this review, we first summarize the key electrochemical processes in electrochemical reactions which lead to the corresponding overpotentials in or between multiple battery components. Furthermore, numerical simulations are employed to quantitatively analyze the effects of versatile electrode parameters on electrochemical properties in high-energy NMC811//graphite systems. On the basis of the in-depth understandings from simulation, recent experimental efforts on designing electroactive materials and electrode architectures across multiple length scales are discussed. Among them, nano-structuring can promote local mass transport and stabilize the interfaces at the particle level, while micro-structuring can establish efficient pathways for charge carriers at the electrode level. Finally, we conclude that a tight feedback loop among structure engineering, characterization and simulation should be followed to speed up the understanding of the deficiencies existing in current electrode designs as well as point out the possible electrode optimization routes for next-generation fast-charging LIBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Rapid identification of neutralizing antibodies against SARS-CoV-2 variants by mRNA display

The increasing prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the ability to escape existing humoral protection conferred by previous infection and/or immunization necessitates the discovery of broadly reactive neutralizing antibodies (nAbs). Utilizing mRNA display, we identify a set of antibodies against SARS-CoV-2 spike (S) proteins and characterize the structures of nAbs that recognize epitopes in the S1 subunit of the S glycoprotein. These structural studies reveal distinct binding modes for several antibodies, including the targeting of rare cryptic epitopes in the receptor-binding domain (RBD) of S that interact with angiotensin-converting enzyme 2 (ACE2) to initiate infection, as well as the S1 subdomain 1. Further, we engineer a potent ACE2-blocking nAb to sustain binding to S RBD with the E484K and L452R substitutions found in multiple SARS-CoV-2 variants. We demonstrate that mRNA display is an approach for the rapid identification of nAbs that can be used in combination to combat emerging SARS-CoV-2 variants.

SARS-CoV-2↗
DOE OSTI2020

In-situ X-ray scattering study of isotactic polypropylene/graphene nanocomposites under shear during fused deposition modeling 3D printing

Complementary techniques were applied to characterize the structure property relationships of FDM printed isotactic polypropylene/graphene nanocomposites. Raman spectroscopy indicated that graphene was mostly exfoliated during the nozzle extrusion process. Rheological measurements of the viscosity indicated that the presence of graphene induced shear thinning during extrusion. Azimuthal scans of wide angle x-ray scattering indicate correlated thermal fluctuations of the graphene platelets which stops abruptly at the onset of transcrystallization, or templating of the iPP chains on the graphene. X-ray microbeam SAXS analysis show the formation of ‘shish-kebab’ structures at the core of the unfilled filaments, where the ‘kebab’ component vanishes at the fiber-fiber interface enabling interdiffusion. Weak lamellar structure, and a corresponding decrease in the storage modulus are observed with the addition of 5% graphene. The lamellar structure disappears entirely with the addition of 10% graphene, while the storage modulus is decreased by nearly 50%. Thermal imaging of heat flux dissipation across molded and printed samples shows no orientational effects in pure iPP despite differences in crystal structure. Thermal conductivity increases dramatically in the samples with 10% graphene, and strong orientational effects are observed where the thermal coefficients differ by 180% between samples printed parallel or perpendicular to the heat flux.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Droplet-Gas Interactions in Nanoelectrospray Multiphase Flow

Electrospray (ES) is production of a charged droplet plume that transits from an electrically biased supply capillary to a counter electrode under the influence of an electric field. Drag interactions with the surrounding gas decelerate the droplets while transferring the momentum from droplets to accelerate the surrounding gas, resulting in an induced gas flow. This work seeks to characterize the structure of the resulting gas jet and identify the key mechanisms defining the flow structure. The phenomenon of gas jets produced by momentum transfer from nano-electrospray (nES) plumes is explored with schlieren visualization, thermal anemometry, and numerical simulations. Schlieren visualization experiments provide information on the flow structure in support of simulation predictions, and the hot thermistor anemometry measurements of gas velocities outside the spray demonstrate quantitatively validated simulation results. The study reveals the formation of a moderately high velocity coaxial gas jet within the nES plume and provides insight into the overall flow structure of the induced flow. The multiphase electrohydrodynamic simulations enable numerical experimentation to explore the fundamental physics of coupled droplet-gas transport and the resulting flow structure. The simulations, confirmed by the experiments, reveal gas jetting induced by nES with a narrow (sub hundred micrometers in diameter) core originating from the nES liquid-jet breakup region and a surrounding larger-in-extent zone (several hundred micrometers in diameter) of lower velocity gas flow within the electrospray plume. This behavior is due to nES ejecting a stream of droplets from the tip of a narrow liquid cone-jet, where the combined effect of many small droplets transferring momentum to a confined region of gas yields a narrow but high-velocity gas stream. Furthermore, the micro-jets produced by nES have practical utility for mass spectrometry, 3D printing and fabrication, and thermal management.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

X-ray topographic study of Bridgman-grown CdZnTeSe

We have characterized the structural quality of Cd 0.9 Zn 0.1 Te 0.93 Se 0.07 ingot grown by the vertical Bridgman growth technique. The structural quality of Cd 0.9 Zn 0.1 Te 0.93 Se 0.07 ingot along the length was investigated via X-ray topography in the reflection mode using a white beam synchrotron radiation source. The X-ray topography data revealed the presence of very few sub grain boundaries and no sub-grain boundary network for the CdZnTeSe samples used in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Correlative nano-imaging of metals and proteins in primary neurons by synchrotron X-ray fluorescence and STED super resolution microscopy: Experimental validation

It is becoming increasingly clear that biological metals such as iron, copper or zinc are involved in synaptic functions, and in particular in the mechanisms of synaptogenesis and subsequent plasticity. Understanding the role of metals on synaptic functions is a difficult challenge due to the very low concentration of these elements in neurons and to the submicrometer size of synaptic compartments. To address this challenge we have developed a correlative nano-imaging approach combining metal and protein detection. First, stimulated emission depletion (STED) microscopy, a super resolution optical microscopy technique, is applied to locate fluorescently labeled proteins. Then, synchrotron radiation induced X-ray fluorescence (SXRF) is performed on the same regions of interest, e.g. synaptic compartments. Here, we present here the principle scheme that allows this correlative nano-imaging and its experimental validation. We applied this correlative nano-imaging to the study of the physiological distribution of metals in synaptic compartments of primary rat hippocampal neurons. We thus compared the nanometric distribution of metals with that of synaptic proteins, such as PSD95 or cytoskeleton proteins. Compared to correlative imaging approaches currently used to characterize synaptic structures, such as electron microscopy correlated with optical fluorescence, our approach allows for ultra-sensitive detection of trace metals using highly focused synchrotron radiation beams. We provide proof-of-principle for correlative imaging of metals and proteins at the synaptic scale and discuss the present limitations and future developments in this area.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Thermodynamic stability of elemental boron allotropes with varying numbers of interstitial atoms

Elemental boron exists in multiple allotropes, each characterized by structures containing icosahedral subunits. These structures have interstitial sites between the icosahedral subunits that are partially occupied. The number of atoms in the unit cell varies depending on the occupancy of these interstitial sites. Here we have investigated the thermodynamic stability of boron allotropes for different numbers of atoms in the unit cell by calculating the free energies, including the contribution of phonons. It has been found that β-rhombohedral boron is most stable with 107 atoms/cell at low temperatures, while it becomes most stable with 105 atoms/cell above 1700 K. This suggests the occurrence of a phase transition at this temperature, which could account for the large variation in the number of atoms in the unit cell for β-rhombohedral boron samples. α-tetragonal boron is consistently most stable with 52 atom/cell and becomes unstable with more than 52 atoms/cell. Similar to β-rhombohedral boron, β-tetragonal boron may undergo a phase transition between 192 and 190 atoms/cell configurations. However, the thermodynamic stability of pure β-tetragonal boron has been questioned in comparison with other allotropes.

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

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗
DOE OSTI2022

Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning

In-situ observations of subsurface ocean temperatures are, in many regions, inconsistently distributed in time and space. These spatio-temporal inconsistencies in the observational network lead to difficulties in utilizing those observations effectively for ocean model evaluation or understanding larger-scale ocean characteristics. Model accuracy of subsurface ocean characteristics is especially important within regions that contain complex ocean structures. One such region is the European Arctic which not only contains several types of water masses with unique characteristics, but also wintertime sea ice coverage and complex bathymetry. This study presents an unsupervised neural networking technique that can be used in combination with traditional ocean model evaluation techniques to provide additional information on the accuracy of modeled vertical ocean temperature profiles. Self-organizing maps is an unsupervised machine learning technique that we apply to approximately twenty thousand Argo and CTD temperature profiles from 2012 to 2020 in the European Arctic to categorize the observed vertical ocean temperature structures in the top 150 m. The observed ocean profile categories, or neurons, defined by the self-organizing map show strong spatial and temporal dependencies. We then use the neuron weights, or the learned temperature profile structure of each neuron, to validate the spatial and temporal variability of modeled vertical temperature structures. This analysis gives us new insights about the model’s capabilities to reproduce specific vertical structures of the top-most ocean layer within different regions and seasons. Mapping modeled ocean temperature profiles onto the neuron-space of the observationally-defined self organized map highlights the potential of this method to advance our understanding of model deficiencies in that region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Crystal structure of butenafine hydrochloride, C 23 H 28 NCl

The crystal structure of butenafine hydrochloride has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Butenafine hydrochloride crystallizes in space group P2 1 (#4) with a = 13.94807(5), b = 9.10722(2), c = 16.46676(6) Å, β = 93.9663(5)°, V = 2086.733(8) Å 3 , and Z = 4. Butenafine hydrochloride occurs as a racemic co-crystal of R and S enantiomers of the cation. The crystal structure is characterized by parallel stacks of aromatic rings along the b-axis. Each cation forms a strong discrete N–H∙∙∙Cl hydrogen bond. The chloride anions also act as acceptors in several C–H∙∙∙Cl hydrogen bonds from methylene, methyl, and aromatic groups. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗