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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Geological map of parts of the state of Sao Paulo based on LANDSAT images

The author has identified the following significant results. Interpretation of LANDSAT images revealed the subdivision of the Bauru formation into three distinct lithofacies. Delineation of structural features yielded new information on paleoenvironmental reconstitution and hydrogeology. Structural features and photogeological units were revealed in the precambrian basement at the eastern portion of the state.

Dejususparada, N.↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Primitive Cubic Cation-Disordered Niobium Tungsten Oxides

In recent years, metastable cation-disordered oxides have had a significant impact on both fundamental and application-driven materials science research. Along this direction, developing new and simple structural types that can accommodate cation disorder at the same crystallographic site has yet to receive extensive research focus. In this work, we use niobium tungsten oxides (NbWOs), a series of materials encompassing diverse structural features, as a material platform to explore new cation-disordered structural types. Relying on mechanochemistry, we realized primitive cubic cation-disordered NbWOs with a ReO 3 -type structure, featuring unique electronic and vibrational properties. Furthermore, when applied as a Li-ion battery anode, the materials undergo a unique perovskite-rock salt structural change mechanism, different from that of complex ordered NbWOs. All these advancements suggest a rich opportunity in developing other new material structural types and realizing new material properties based on the methodology of the work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The structural basis of bacterial manganese import

Metal ions are essential for all forms of life. In prokaryotes, ATP-binding cassette (ABC) permeases serve as the primary import pathway for many micronutrients including the first-row transition metal manganese. However, the structural features of ionic metal transporting ABC permeases have remained undefined. Here, we present the crystal structure of the manganese transporter PsaBC from Streptococcus pneumoniae in an open-inward conformation. The type II transporter has a tightly closed transmembrane channel due to “extracellular gating” residues that prevent water permeation or ion reflux. Below these residues, the channel contains a hitherto unreported metal coordination site, which is essential for manganese translocation. Mutagenesis of the extracellular gate perturbs manganese uptake, while coordination site mutagenesis abolishes import. These structural features are highly conserved in metal-specific ABC transporters and are represented throughout the kingdoms of life. Collectively, our results define the structure of PsaBC and reveal the features required for divalent cation transport.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Novel ethylenediamine-gallium phosphate containing 6-fold coordinated gallium atoms with unusual four equatorial Ga–N bonds

A novel ethylenediamine-gallium phosphate, formulated as Ga(H{sub 2}NCH{sub 2}CH{sub 2}NH{sub 2}){sub 2}PO{sub 4}·2H{sub 2}O, was synthesized under hydrothermal conditions. The crystal structure, including hydrogen positions, was determined using single-crystal X-ray diffraction data (monoclinic, a=9.4886(3) Å, b=6.0374(2) Å, c=10.2874(3) Å, and β=104.226(3)°, space group Pc) and the bulk was characterized by chemical (Ga–P–C–H–N) and thermal analysis (TG–MS and DSC), including activation energy data of its thermo-oxidative degradation, powder X-ray diffraction (PXRD), solid-state nuclear magnetic resonance (SS-NMR) measurements, and transmission electron microscopy (TEM, SAED/NBD, and STEM BF-EDX). The crystal structure is built up of infinite zig-zag chains running along the c-axis, formed by vertex-shared (PO{sub 4}) and (GaO{sub 2}N{sub 4}) polyhedra. The new compound is characterized by unusual four equatorial Ga–N bonds coming from two nonequivalent ethylenediamine molecules and exhibits strong blue emission at 430 nm (λ{sub ex}=350 nm) in the solid state at room temperature. - Graphical abstract: Single crystals of a new ethylenediamine-gallium phosphate, Ga(H{sub 2}NCH{sub 2}CH{sub 2}NH{sub 2}){sub 2}PO{sub 4}·2H{sub 2}O, were obtained and the structural features presented. This structure is one of the scarce examples of GaPO with Ga–N bonds reported. - Highlights: • A novel ethylenediamine-gallium phosphate was hydrothermally synthesized. • The new compound is characterized by unusual four equatorial Ga–N bonds. • Void-volume analysis shows cages and channels with sizes ideally suited to accommodate small molecules. • The new compound exhibits strong blue emission.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Facile synthesis of interconnected mesoporous ZnMn2O4 nano-peanuts for Li-storage via distinct structure design

Highlights: • Interconnected mesoporous ZnMn{sub 2}O{sub 4} nano-peanuts were successfully synthesized. SS • Novel ZnMn{sub 2}O{sub 4} have three structural features: special mesoporous structure, high specific surface and interconnected walls. • The interconnected mesoporous structure can buffer volume change and avoid particle aggregation. • The ZnMn{sub 2}O{sub 4} nano-peanuts delivered high stable reversible lithium storage properties. - Abstract: The ZnMn{sub 2}O{sub 4} nano-peanuts with diameters of 16–25 nm have been successfully prepared through a simple one-step surfactant-assisted solvothermal process. The as-synthesized ZnMn{sub 2}O{sub 4} exhibits novel interconnected mesoporous hierarchical architecture, which possesses three advantages: special mesoporous structure, high specific surface and interconnected walls. On one hand, the interconnected structure can provide sufficient surface reaction sites to fully contact Li{sup +} with the electrolyte and improve Li{sup +} diffusion and electron transfer; On the other hand, the mesoporous hierarchical networks can buffer the volume change, accommodate or alleviate the strain during charge-discharge process, thus enhancing the conductivity and stability of the electrodes. Benefiting from the mesoporous design, the interconnected ZnMn{sub 2}O{sub 4} delivers a stable capacity (812 mA h g{sup −1} at 100 mA g{sup −1} after 200 cycles), and maintains a high capacity of 516 mA h g{sup −1} even at 2 A g{sup −1} after 200 cycles, which shows that the nanostructured ZnMn{sub 2}O{sub 4} has excellent cycling stability.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

R -Nb 2 O 5 has an ‘idealized’ V 2 O 5 structure and Wadsley–Roth-like structural stability during Li-ion battery cycling

The adoption of batteries across diverse applications requires electrode materials with a wider range of performance metrics, such as cost, safety, and material availability. Along the path to discovering new commercially viable materials, a fundamental understanding of chemical and atomic structure features that provide structural stability and effective ion transport is essential. In support of new understanding, we report the cycling behavior of metastable R-Nb 2 O 5 . R-Nb 2 O 5 adopts an ‘idealized’ V 2 O 5 structure, in which [NbO 6 ] octahedra alternate in edge- and corner-sharing resulting in ReO 3 -like slabs, whereas Wadsley–Roth materials have ReO 3 -like blocks, linked through edge-sharing octahedra at intersecting crystallographic shear planes. We find that this slab structure is stable during cycling, with minor atomic structure changes and cycling curves that are symmetric on discharge and charge, resembling the behavior of Wadsley–Roth materials more than other related materials, such as ReO 3 , V 2 O 5 , or Nb 3 O 7 F. Based on our findings, R-Nb 2 O 5 can serve as a ‘structural bridge’ between Wadsley–Roth block structures and V 2 O 5 , through which we can relate inter- and intra-polyhedral structures to cycling behavior and structural stability during cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

Gaussian windows: A tool for exploring multivariate data

Presented here is a method for interactively exploring a large set of quantitative multivariate data, in order to estimate the shape of the underlying density function. It is assumed that the density function is more or less smooth, but no other specific assumptions are made concerning its structure. The local structure of the data in a given region may be examined by viewing the data through a Gaussian window, whose location and shape are chosen by the user. A Gaussian window is defined by giving each data point a weight based on a multivariate Gaussian function. The weighted sample mean and sample covariance matrix are then computed, using the weights attached to the data points. These quantities are used to compute an estimate of the shape of the density function in the window region. The local structure of the data is described by a method similar to the method of principal components. By taking many such local views of the data, we can form an idea of the structure of the data set. The method is applicable in any number of dimensions. The method can be used to find and describe simple structural features such as peaks, valleys, and saddle points in the density function, and also extended structures in higher dimensions. With some practice, we can apply our geometrical intuition to these structural features in any number of dimensions, so that we can think about and describe the structure of the data. Since the computations involved are relatively simple, the method can easily be implemented on a small computer.

Jaeckel, Louis A.↗
DOE OSTI2023

Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure’s fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features, and interface this method with the XTALOPT evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g., the coordination number or chemical environment), and symmetry (e.g., Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN 8 , with a two-dimensional polymeric nitrogen sublattice, brookite TiO 2 , and a high pressure BaH 4 phase that was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1¯WC 3 N 6 , was found to possess an energy that is lower than two phases proposed in a recent computational study. Here, the method presented here could help in identifying the structures of compounds that have already been synthesized, and developing new synthesis targets with desired properties.

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

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2019

Convergent Structures Illuminate Features for Germline Antibody Binding and Pan-Lassa Virus Neutralization

Lassa virus (LASV) causes hemorrhagic fever and is endemic in West Africa. Protective antibody responses primarily target the LASV surface glycoprotein (GPC), and GPC-B competition group antibodies often show potent neutralizing activity in humans. However, which features confer potent and broadly neutralizing antibody responses is unclear. Here, we compared three crystal structures of LASV GPC complexed with GPC-B antibodies of varying neutralization potency. Each GPC-B antibody recognized an overlapping epitope involved in binding of two adjacent GPC monomers and preserved the prefusion trimeric conformation. Differences among GPC-antibody interactions highlighted specific residues that enhance neutralization. Using structure-guided amino acid substitutions, we increased the neutralization potency and breadth of these antibodies to include all major LASV lineages. The ability to define antibody residues that allow potent and broad neutralizing activity, together with findings from analyses of inferred germline precursors, is critical to develop potent therapeutics and for vaccine design and assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

3D-equivariant graph neural networks for protein model quality assessment

Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Volcanology and morphology

Apollo 15 photographs of the southern parts of Serenitatis and Imbrium were used for a study of the morphology and distribution of wrinkle ridges. Volcanic and structural features along the south margin of Serenitatis were also studied, including the Dawes basalt cinder cones. Volcanic and structural features in crater Aitken were investigated as well. Study of crater Goclenius showed a close relationship between morphology of the impact crater and grabens which tend to parallel directions of the lunar grid. Similar trends were observed in the walls of crater Tsiolkovsky and other linear structures. Small craters of possible volcanic origin were also studied. Possible cinder cones were found associated with the Dawes basalt and in the floor of craters Aitken and Goclenius. Small pit craters were observed in the floors of these craters. Attempts were made to obtain contour maps of specific small features and to compare Orbiter and Apollo photographs to determine short term changes associated with other processes.

Bryan, W. B.↗
DOE OSTI2014

Data mining for materials design: A computational study of single molecule magnet

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn{sup 4+} Mn {sub 3}{sup 3+} single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn{sup 4+} and Mn{sup 3+} ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Structural lineament and pattern analysis of Missouri, using LANDSAT imagery

The author has identified the following significant results. Major linear, circular, and arcuate traces were observed on LANDSAT imagery of Missouri. Lineaments plotted within the state boundaries range from 20 to nearly 500 km in length. Several extend into adjoining states. Lineaments plots indicate a distinct pattern and in general reflect structural features of the Precambrian basement of the platform. Coincidence of lineaments traced from the imagery and known structural features in Missouri is high, thus supporting a causative relation between them. The lineament pattern apparently reveals a fundamental style of the deformation of the intracontinental craton. Dozens of heretofore unknown linear features related to epirogenic movements and deformation of this segment of the continental crust were delineated. Lineaments and mineralization are interrelated in a geometrically classifiable pattern.

Martin, J. A.↗
DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗