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Results for “structural identification”

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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Restructuring and Optimizing Reactor Building Lifting Processes & Designing a Solution for an Overhead Door Handling Forklift Attachment

This poster presents two innovative projects aimed at enhancing operational efficiency and safety at the Advanced Test Reactor (ATR) Complex. The first project focuses on restructuring the existing lift book used for hoisting operations within the ATR Reactor Building. The current lift book, a cumbersome 150+ page document, is being transformed into a more efficient format using charts that allow for quick identification of maximum lift heights based on object footprint and weight. This new method also considers various parameters such as floor integrity and reactor status, dividing entries into four distinct charts to improve usability and safety during lifts. The second project involves designing a specialized forklift attachment for handling an overhead door at the ATR Reactor Building. The attachment is engineered to securely lift and lower a 1200lb, 14ft long door, ensuring safe replacement operations. The design process included research on forklift attachment codes, modeling in Autodesk Inventor, and testing through Finite Element Analysis (FEA) and hand calculations to confirm the attachment's structural integrity. Future work includes completing detailed drawings and an Engineering Calculations Analysis and Review (ECAR) document to proceed with manufacturing and assembly. Together, these projects demonstrate a commitment to optimizing reactor building operations through innovative engineering solutions and safety considerations.

42 - ENGINEERING↗
DOE OSTI2025

Graphene Activated Magnesium Diboride for Moderate Pressure and Temperature Hydrogenation to Magnesium Borohydride

The hydrogenation conditions of magnesium diboride (MgB2) to magnesium borohydride (Mg(BH4)2) can be significantly enhanced through the discovery of improved modifiers. This study demonstrates that the modification of MgB2 by mechanical milling with graphene nanoplatelets significantly reduces the hydrogenation conditions of MgB2 from 900 bar and 400 degrees C for pure MgB2 to 400 bar and 300 degrees C while achieving 77% conversion to Mg(BH4)2. The introduction of the graphene additives coupled with milling leads to a reduction of the temperature and pressure required for bulk hydrogenation by 100 degrees C and 500 bar, respectively, from that of pure MgB2. The identification of graphene additives that drastically improve the hydrogenation conditions of MgB2 represents an important step toward improving hydrogen uptake kinetics to Mg(BH4)2.

complex hydrides↗
DOE OSTI2023

On the BPS Sector in AdS 3 /CFT 2 Holography

Abstract The BPS sector in duality has been fertile ground for the exploration of gauge/gravity duality, from the match between black hole entropy and the CFT elliptic genus to the construction of large families of geometrical microstates and the identification of the corresponding states in the CFT. Worldsheet methods provide a tool to further explore the relation between string theory in the bulk and corresponding CFT quantities. We show how to match individual BPS strings to their counterparts in the symmetric product orbifold CFT. In the process, we find an exact match between known constructions of microstate geometries and condensates of BPS supergraviton strings, and discuss their role in the broader collection of BPS states. In particular, we explore how microstate geometries develop singularities; and how string theory resolves these singularities through the appearance of “tensionless” string dynamics, which is the continuation of structures found in the weak‐coupling CFT into the strongly coupled regime described by string theory in the bulk. We argue that such “tensionless” strings are responsible for black hole microstructure in the bulk description.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Direct Evidence of Charge Transfer upon Anion Intercalation in Graphite Cathodes through New Electronic States: An Experimental and Theoretical Study of Hexafluorophosphate

Graphite intercalation compounds continue to be central to technologies for electrochemical energy storage from anodes in established Li-ion batteries to cathodes in beyond Li-ion concepts paired with multivalent anodes. When used as a cathode, graphite intercalates a variety of anions with PF 6 - being among the most common. Paired with Li intercalation at the anode, the corresponding dual carbon battery yields high energy and power densities. Given the available choice of anions as intercalants, it is important to elucidate how the graphite structure accommodates them in order to tailor the molecular species to maximize charge and reversibility. However, the changes in electronic structure of the host graphite lattice upon anion intercalation are poorly understood compared to cations, which represent a fundamentally different reaction. Herein, PF 6 -intercalated graphite has been studied using techniques sensitive to electronic structure, namely, X-ray Raman spectroscopy (XRS), X-ray absorption near-edge spectroscopy (XANES), and X-ray emission spectroscopy (XES). Complementary full-potential, all-electron density functional theory calculations yielded excellent agreement with the spectra, thus providing insight into charge compensation in the graphite lattice. In particular, a pre-$π^*$ feature emerged in XRS/XANES, which is direct evidence of the removal of charge from the host lattice to compensate the intercalated anions, leading to an overall lowering of the Fermi energy level. This is expected to be characteristic of many intercalants in anion-intercalated graphite. The unambiguous identification of the origin of the pre-$π^*$ spectral feature, which is frequently seen in graphitic systems, is of broad interest to the spectroscopy of graphitic systems beyond the practical implications of anion-induced changes in the electronic properties for real devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Identification of the molecular-weight growth reaction network in counterflow flames of the C 3 H 4 isomers allene and propyne

The reaction networks responsible for aromatics formation in counterflow flames of the C 3 H 4 isomers allene and propyne are identified through a combined experimental and modeling study. Mole fraction profiles of near-atmospheric pressure (933 mbar) diffusion flames fueled by the C 3 H 4 isomers are analyzed by means of a newly assembled, chemically detailed kinetic mechanism. The experiment consists of a counterflow burner system that is coupled to a high-resolution time-of-flight molecular-beam mass spectrometer with single-photon ionization via synchrotron-generated vacuum-ultraviolet photons. Flame-sampled, mass-specific photoionization efficiency curves are used to identify the presence of aliphatically substituted aromatic species in addition to the commonly considered pericondensed ring structures. The new mechanism describes the formation and growth of aromatics through repetitive sequences of radical–radical and radical–molecule reactions that include C 1 6 intermediates. Higher concentrations of aromatic species are observed in the allene flame and the new mechanism captures the observed experimental trends very accurately. The results indicate the importance of the aliphatically substituted aromatics and of ring-enlargement reactions for the growth reactions. According to the model simulations, radical+radical recombination and PAH-radical+molecule reactions play an important role in PAH growth.

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

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Re-evaluating the Systematics of Dendrolycopodium Using Restriction-Site Associated DNA-Sequencing

The genus Dendrolycopodium (Lycopodiaceae) includes four to five species across North America and East Asia. Species identification in Dendrolycopodium is difficult due to limited or inconsistent characters. In addition, plants with intermediate morphologies regularly occur, potentially indicative of interspecific hybridization. To determine the species relationships in Dendrolycopodium and investigate the existence of hybrids, we generated a draft genome assembly for D. obscurum and carried out double-digest restriction-site associated DNA sequencing (RADSeq) on 86 Dendrolycopodium specimens. Our sampling includes all the described species and 11 individuals with intermediate morphology. We find that the genus can be divided into four clades that largely correspond to the described taxa, as well as evidence of interspecific hybridization. Within these clades, our STRUCTURE analysis suggests that there are multiple finer subgroups, with evidence of hybridization and introgression between these subgroups. Given the limited availability of specimens collected from Asia, the status of the various Asian species remains uncertain and will require further study. In summary, our study confirms several hybrid relationships in Dendrolycopodium and provides a clear phylogenetic framework for future taxonomic revision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-Defect Detection in Additively Manufactured Lattice Structures Using 3D Electrical Resistance Tomography

Cellular lattice structures possess high strength-to-weight ratios suitable for advanced lightweight engineering applications. However, their quality and mechanical performance can degrade because of defects introduced during manufacturing or in-service. Their complexity and small length scale features make defects difficult to detect using conventional nondestructive evaluation methods. Here we propose a current injection-based method, electrical resistance tomography (ERT), that can be used to detect damaged struts in conductive cellular lattice structures with their intrinsic electromechanical properties. The reconstructed conductivity distributions from ERT can reveal the severity and location of damaged struts without having to probe each strut. However, the low central sensitivity of ERT may result in image artifacts and inaccurate localization of damaged struts. To address this issue, this study introduces an absolute, high throughput, conductivity reconstruction algorithm for 3D ERT. The algorithm incorporates a strut-based normalized sensitivity map to compensate for lower interior sensitivity and suppresses reconstruction artifacts. Numerical simulations and experiments on fabricated representative cellular lattice structures were performed to verify the ability of ERT to quantitatively identify single and multiple damaged struts. The improved performance of this method compared with classical ERT was observed, based on greatly decreased imaging and reconstructed value errors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Active site identification and CO oxidation in UiO-66-XX thin films

Metal-organic frameworks (MOFs) offer an intrinsically porous and chemically tunable platform for gas adsorption, separation, and catalysis. Herein we investigate thin film derivatives of the well-studied Zr–O based MOF powders to understand their adsorption properties and reactivity with their adaption to thin films, involving diverse functionality with the incorporation of different linker groups and the inclusion of embedded metal nanoparticles: UiO-66, UiO-66-NH 2 , and Pt@UiO-66-NH 2 . Using transflectance IR spectroscopy, we determine the active sites in each film upon consideration of the acid-base properties of the adsorption sites and guest species, and perform metal-based catalysis with CO oxidation of a Pt@UiO-66-NH 2 film. Our study shows how surface science characterization techniques can be used to characterize the reactivity and the chemical and electronic structure of MOFs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Characterization of the Entry Steps in Diterpenoid Alkaloid Biosynthesis

The diterpenoid alkaloids are a group of specialized metabolites where the terpenoid and alkaloid classes intersect, and which are found primarily within the Aconitum (Wolf’s-Bane) and Delphinium (Larkspur) genera. While there is considerable interest in these compounds for their wide range of bioactivities, their structural complexity poses a significant challenge for chemical synthesis. Biosynthesis offers an appealing alternative for production, however, little progress has been made towards elucidation of their biosynthetic pathways. Here, we employ a comparative transcriptomics approach to identify six enzymatic steps in the biosynthesis of atisinium, conserved across both Delphinium grandiflorum and Aconitum plicatum . Key to this pathway is a reductase which selectively incorporates ethanolamine over ethylamine into the diterpenoid scaffold. While the majority of diterpenoid alkaloids contain an ethylamine moiety, we demonstrate through isotope labeling in Aconitum callus cultures and a computational metabolomics approach that ethanolamine is, unintuitively, the preferred source of nitrogen for these metabolites. Identification of these enzymes and production of a key intermediate in a heterologous host paves the way for biosynthetic production of this group of metabolites with promise for medicinal applications.

biosynthesis↗
DOE OSTI2023

Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides

The computational design of suitable multiferroic double perovskite oxides requires finding materials that exhibit sizable polarization, magnetization, and coupling between them. Oxides with the chemical formula of AA'BB'O 6 with building blocks of ABO 3 single perovskite oxides in centrosymmetric Pnma symmetry are strong candidates that have been reported to satisfy such criteria. The system lowers to noncentrosymmetric, polar P2 1 symmetry if A/A' layered and B/B' rocksalt cation orderings are imposed. A detailed compositional search over a variety of chemical spaces followed by evaluating their polarization may lead to the identification of more of these compounds with ferroelectric ordering. The standard density functional theory practices to estimate polarization within the Berry phase formalism require the systems to be perfectly insulating. The number of compounds that can be evaluated using this method is therefore limited. In this work, we introduce a predictive learning strategy based on importance sampling to build a series of machine learning models using results from first-principles simulations to predict polarization and the corresponding switching barrier. The geometry-driven features related to charge states and cationic radii play key roles in predicting the switching barrier with complementary contributions from the key structural mode-based order parameters. These modes become important to draw reasonable predictions of polarization components from machine learning models. In conclusion, our predictive models identify candidates with high polarizations and low switching barriers from a pool of double perovskite oxides, suitable for future investigation for their potential applications in spintronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Expression profiling of MADS-box gene family revealed its role in vegetative development and stem ripening in S. spontaneum

Sugarcane is the most important sugar and biofuel crop. MADS-box genes encode transcription factors that are involved in developmental control and signal transduction in plants. Systematic analyses of MADS-box genes have been reported in many plant species, but its identification and characterization were not possible until a reference genome of autotetraploid wild type sugarcane specie, Saccharum spontaneum is available recently. We identified 182 MADS-box sequences in the S. spontaneum genome, which were annotated into 63 genes, including 6 (9.5%) genes with four alleles, 21 (33.3%) with three, 29 (46%) with two, 7 (11.1%) with one allele. Paralogs (tandem duplication and disperse duplicated) were also identified and characterized. These MADS-box genes were divided into two groups; Type-I (21 Mα, 4 Mβ, 4 Mγ) and Type-II (32 MIKCc, 2 MIKC*) through phylogenetic analysis with orthologs in Arabidopsis and sorghum. Structural diversity and distribution of motifs were studied in detail. Chromosomal localizations revealed that S. spontaneum MADS-box genes were randomly distributed across eight homologous chromosome groups. The expression profiles of these MADS-box genes were analyzed in leaves, roots, stem sections and after hormones treatment. Important alleles based on promoter analysis and expression variations were dissected. qRT-PCR analysis was performed to verify the expression pattern of pivotal S. spontaneum MADS-box genes and suggested that flower timing genes ( SOC1 and SVP ) may regulate vegetative development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning

Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.

deep learning↗
DOE OSTI2023

cotH Genes Are Necessary for Normal Spore Formation and Virulence in Mucor lusitanicus

Mucormycosis is an invasive fungal infection caused by certain members of the fungal order of Mucorales. The species most frequently identified as the etiological agents of mucormycosis belong to the genera Rhizopus , Lichtheimia , and Mucor . The frequency of systemic mucormycosis has been increasing, mainly because of increasing numbers of susceptible patients. Furthermore, Mucorales display intrinsic resistance to the majority of routinely used antifungal agents (e.g., echinocandins and short-tailed azoles), which limits the number of possible therapeutic options. All the above-mentioned issues urge the improvement of molecular identification methods and the discovery of new antifungal targets and strategies. Spore coat proteins (CotH) constitute a kinase family present in many pathogenic bacteria and fungi and participate in the spore formation in these organisms. Moreover, some of them can act as virulence factors being receptors of the human GRP78 protein during Rhizopus delemar-induced mucormycosis. We identified 17 cotH -like genes in the Mucor lusitanicus genome database. Successful disruption of five cotH genes in Mucor was performed using the CRISPR-Cas9 system. The CotH3 and CotH4 proteins play a role in adaptation to different temperatures as well as in developing the cell wall structure. We also show CotH4 protein is involved in spore wall formation by affecting the total chitin content and, thus, the composition of the spore wall. The role of CotH3 and CotH4 proteins in virulence was confirmed in two invertebrate models and a diabetic ketoacidosis (DKA) mouse model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An effective QWBA/UHPLC-MS/tissue punch approach: solving a pharmacokinetic issue via quantitative Met-ID

Methods to provide absolute quantitation of the administered drug and corresponding metabolites in tissue in a spatially resolved manner is a challenging but much needed necessity in pharmaceutical research. Quantitative whole-body autoradiography (QWBA) after a single-dose intravenous (3 mg/kg) and extravascular (30 mg/kg) administrations of an in vitro metabolically stable test compound (structure not reported here) indicated quick tissue distribution and excretion. Good bioavailability and short in vivo half-lives were determined formerly for the same test compound. For closing gaps in the understanding of pharmacokinetic data and in vitro results, radioactive hot spots on whole-body tissue sections had been profiled. Punches from selected tissue regions containing high radioactivity in the tissue sections previously analyzed by QWBA were extracted by a highly organic solvent and analyzed without any consecutive sample preparation step, applying ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS) and off-line radioanalysis to maximize signal levels for metabolite identification and profiling. The analysis revealed that the test compound was metabolized intensively by phase I reactions in vivo and the metabolites formed were excreted in bile and in urine. The predominant metabolites showed abundant signal intensities both by MS and by radioanalysis but the MS signal intensities generally underestimated the real abundances of metabolites relative to the unchanged drug. In this work we illustrate that maximizing the sensitivity of tissue punch radioanalysis and the combination with UHPLC-MS leads to a better insight of pharmacokinetic processes by providing quantitative data with high molecular selectivity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Guaranteed Phase & Topology Identification in Three Phase Distribution Grids

We present a method for joint phase identification and topology recovery in unbalanced three phase radial networks using only voltage measurements. By recovering phases and topology jointly, we utilize all three phase voltage measurements and can handle networks where some buses have a subset of three phases. Our method is theoretically justified by a novel linearized formulation of unbalanced three phase power flow and makes precisely defined and reasonable assumptions on line impedances and load statistics. We validate our method on three IEEE test networks simulated under realistic conditions in OpenDSS, comparing our performance to the state of the art. In addition to providing a new method for phase and topology recovery, our intuitively structured linearized model will provide a foundation for future work in this and other applications.

42 ENGINEERING↗
DOE OSTI2023

Reducing uncertainty of high-latitude ecosystem models through identification of key parameters

Abstract Climate change is having significant impacts on Earth’s ecosystems and carbon budgets, and in the Arctic may drive a shift from an historic carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic changes demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions. One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. We identify that mismatch for three TBMs, DVM-DOS-TEM, SIPNET and ED2, and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parametrization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, we develop a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes. We map model parameters to processes in the CLD and assess parameter vulnerability via the internal network structure. One important substructure, feed forward loops (FFLs), describe processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty. We find that the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, we identify the key parameters responsible for limited model accuracy. They should be prioritized for future data sampling to reduce model uncertainty.

54 ENVIRONMENTAL SCIENCES↗