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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
DOE OSTI2023

Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.

composites↗
DOE OSTI2022

Synthetic aromatic kerosene property prediction improvements with isomer specific characterization via GCxGC and vacuum ultraviolet spectroscopy

This research explores an advanced method of fuel composition determination and builds upon typical hydrocarbon group type analyses performed with two-dimensional gas chromatography (GCxGC). In this study, structural information of individual species within Virent’s Synthetic Aromatic Kerosene (SAK) is identified by vacuum ultraviolet (VUV) spectroscopy. By mass, 71.3% of the components elute within six peaks of the chromatogram, from which 12 unique species are identified through a novel deconvolution method. Overall, the identification of 93.6%m across 26 structural isomers is made by the methods described in this work. With 93.6%m ascribed to specific isomers, the precision of fuel property predictions improves dramatically. For example, the absolute error of the viscosity prediction is reduced by 90% because of this advancement in diagnostic capability, and its 95-percentile confidence interval (precision only) is reduced by 93%. Additionally, the properties of SAK, blended with hydro processed esters fatty acids (HEFA), are demonstrated to have blended properties consistent with conventional jet fuel.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Identification of Novel Microcystins Using High-Resolution MS and MS n with Python Code

Cyanotoxins called microcystins (MCs) are highly toxic and can be present in drinking water sources. Determining the structure of MCs is paramount because of its effect on toxicity. Though over 300 MC congeners have been discovered, many remain unidentified. In this work, a method is described for the putative identification of MCs using liquid chromatography (LC) coupled with high-resolution (HR) Orbitrap mass spectrometry (MS) and a new bottom-up sequencing strategy. Maumee River water samples were collected during a harmful algal bloom and analyzed by LC–MS with simultaneous HRMS and MS/MS. Unidentified ions with characteristic MC fragments (135 and 213 m/z) were recognized as possible novel MC congeners. An innovative workflow was developed for the putative identification of these ions. Python code was written to generate the potential structures of unidentified MCs and to assign ions after the fragmentation for structural confirmation. The workflow enabled the putative identification of eight previously reported MCs for which standards are not available and two newly discovered congeners, MC-HarR and MC-E(OMe)R.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep Green Unannotated Protein Structures

The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology. References: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589. Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

09 BIOMASS FUELS↗
DOE OSTI2022

Physics-informed neural networks for identification of material properties using standing waves

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

Rathod, Vivek↗
DOE OSTI2021

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts

Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically dispersed platinum supported on crystalline MgO (chosen for its well-defined bonding sites) as a prototypical example, we demonstrate in this work how systematic density functional theory calculations for assessing all the potentially stable platinum sites, combined with automated analysis of extended X-ray absorption fine structure (EXAFS) spectra, leads to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging and EXAFS and high-energy resolution fluorescence detection X-ray absorption near edge spectroscopy. The proposed platinum sites are in agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active site than what is currently possible with conventional EXAFS analyses. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities.

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

Dual factors required for cytochrome-P450-mediated hydrocarbon ring contraction in bacterial gibberellin phytohormone biosynthesis

Cytochromes P450 (CYPs) are heme-thiolate monooxygenases that prototypically catalyze the insertion of oxygen into unactivated C–H bonds but are capable of mediating more complex reactions. One of the most remarked-upon alternative reactions occurs during biosynthesis of the gibberellin A (GA) phytohormones, involving hydrocarbon ring contraction with coupled aldehyde extrusion of ent-kaurenoic acid to form the first gibberellin intermediate. While the unusual nature of this reaction has long been noted, its mechanistic basis has remained opaque. Building on identification of the relevant CYP114 from bacterial GA biosynthesis, detailed structure–function studies are reported here, including development of in vitro assays as well as crystallographic analyses both in the absence and presence of substrate. These structures provided insight into enzymatic catalysis of this unusual reaction, as exemplified by identification of a key role for the “missing” acid from an otherwise highly conserved acid–alcohol pair of residues. Notably, the results demonstrate that ring contraction requires dual factors, both the use of a dedicated ferredoxin and absence of the otherwise conserved acidic residue, with exclusion of either limiting turnover to just the initiating and more straightforward hydroxylation. The results provide detailed insight into the enzymatic structure–function relationships underlying this fascinating reaction and support the use of a semipinacol mechanism for the unusual ring contraction reaction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

The study of multimetallic alloys and the multitude of possible surface compositions have sparked a tremendous interest in engineering low-cost materials with high activity and selectivity in heterogeneous catalysis. Multimetallic systems provide complementary functionalities and an unprecedented tunability when designing catalyst formulations. However, due to their immense structural and compositional complexity, the investigation and identification of an optimal catalyst is a tedious and time-consuming process, both experimentally and theoretically. Therefore, theoretical design principles are highly desirable to accelerate the screening of catalyst structures across the vast compositional space. In this paper, we introduce a simple and general model for predicting the site stability of multimetallic surfaces and nanoparticles, which is based on physical principles. The model requires only a small set of density functional theory (DFT) calculations of metal atom binding energies on monometallic and dilute alloy surface slabs to optimize the parameters in the simple model. The resulting model allows for the quantification of the stability of any particular atom site in any conceivable chemical environment across a wide range of morphologies, sizes, and arrangements by interpolating the derived parameters from a monometallic system to a completely diluted alloyed system. Herein, we demonstrate the robustness of the model across an extensive data set of transition metal alloy surfaces and 147-atoms cuboctahedral nanoparticles (NPs) composed of IrRhRu and PtPdRu. In conclusion, our approach yields mean absolute errors of ≈0.15 (IrRhRu), 0.20 (PtPdRu), 0.19 (IrRhRu NP), and 0.26 (PtPdRu NP) eV relative to site binding energies calculated using DFT.

36 MATERIALS SCIENCE↗
DOE OSTI2023

RNA structures and dynamics with Å resolution revealed by x-ray free-electron lasers

RNA macromolecules, like proteins, fold to assume shapes that are intimately connected to their broadly recognized biological functions; however, because of their high charge and dynamic nature, RNA structures are far more challenging to determine. We introduce an approach that exploits the high brilliance of x-ray free-electron laser sources to reveal the formation and ready identification of angstrom-scale features in structured and unstructured RNAs. Previously unrecognized structural signatures of RNA secondary and tertiary structures are identified through wide-angle solution scattering experiments. With millisecond time resolution, we observe an RNA fold from a dynamically varying single strand through a base-paired intermediate to assume a triple-helix conformation. While the backbone orchestrates the folding, the final structure is locked in by base stacking. This method may help to rapidly characterize and identify structural elements in nucleic acids in both equilibrium and time-resolved experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation

Protein sequence similarity search is fundamental to biology research, but current methods are typically not able to consider crucial genomic context information indicative of protein function, especially in microbial systems. Here, we present Gaia (Genomic AI Annotator), a sequence annotation platform that enables rapid, context-aware protein sequence search across genomic datasets. Gaia leverages gLM2, a mixed-modality genomic language model trained on both amino acid sequences and their genomic neighborhoods to generate embeddings that integrate sequence-structure-context information. This approach allows for the identification of functionally and/or evolutionarily related genes that are found in conserved genomic contexts, which may be missed by traditional sequence- or structure-based search alone. Gaia enables real-time search of a curated database comprising more than 85 million protein clusters from 131,744 microbial genomes. We compare the homolog retrieval performance of Gaia search against other embedding and alignment-based approaches. We provide Gaia as a web-based, freely available tool.

Jha, Nishant↗
DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A single diiron enzyme catalyses the oxidative rearrangement of tryptophan to indole nitrile

Abstract Nitriles are uncommon in nature and are typically constructed from oximes through the oxidative decarboxylation of amino acid substrates or from the derivatization of carboxylic acids. Here we report a third nitrile biosynthesis strategy featuring the cyanobacterial nitrile synthase AetD. During the biosynthesis of the eagle-killing neurotoxin, aetokthonotoxin, AetD transforms the 2-aminopropionate portion of 5,7-dibromo-l-tryptophan to a nitrile. Employing a combination of structural, biochemical and biophysical techniques, we characterized AetD as a non-haem diiron enzyme that belongs to the emerging haem-oxygenase-like dimetal oxidase superfamily. High-resolution crystal structures of AetD together with the identification of catalytically relevant products provide mechanistic insights into how AetD affords this unique transformation, which we propose proceeds via an aziridine intermediate. Our work presents a unique template for nitrile biogenesis and portrays a substrate binding and metallocofactor assembly mechanism that may be shared among other haem-oxygenase-like dimetal oxidase enzymes.

Chemistry↗
DOE OSTI2020

Identification and Quantification of Technetium Species in Hanford Waste Tank AN-102

Technetium-99 (Tc) generated from the fission of 235U and 239Pu in high yields is one of the most difficult contaminants to be addressed at the U.S. Department of Energy Hanford Site. In strongly alkaline solutions typifying Hanford tank waste, Tc exists as pertechnetate (TcO4-) (oxidation state VII) as well as in reduced forms (oxidation state < VII) collectively known as non-pertechnetate species. Designing strategies for effective Tc management, including separation and immobilization, necessitates understanding the molecular structure of the non- pertechnetate species and their identification in the actual tank waste samples, which would facilitate development of new treatment technologies effective for dissimilar Tc species. Toward this objective, a spectroscopic library of the Tc(I) [fac-Tc(CO)3]+ and Tc(IV, VII) compounds was generated using a range of techniques and applied to the characterization of the actual tank waste supernatant collected from the tank 241-AN-102 at Hanford, WA. A sample of the 241-AN-102 tank waste supernatant was processed to adjust Na concentration to about 5.6 M and remove 137Cs by spherical resorcinol-formaldehyde (sRF) ion exchange resin. Cesium-loaded sRF column was eluted with 0.5 M HNO3. As received AN-102, Cs-depleted AN-102 effluent, and sRF eluate fractions were comprehensively characterized for chemical composition and speciation of Tc using 99Tc nuclear magnetic resonance spectroscopy and X-ray absorption spectroscopy. It was demonstrated for the first time that non-pertechnetate Tc present in the 241-AN-102 tank waste is composed of several low-valent Tc species, including the Tc(I) [fac-Tc(CO)3]+ and Tc(IV) compounds. This is the second experimental observation of the [fac-Tc(CO)3]+ species in the Hanford tank waste and the first demonstration of multiple forms of non-pertechnetate species existing simultaneously in the waste, cumulatively highlighting their importance for the waste processing.

Low Activity Waste (LAW), High Level Waste, nuclea↗
DOE OSTI2021

Resonant X-ray scattering of biological assemblies

Understanding the relationship between structure and function for biological assemblies can guide identification of new therapeutics, design of biomaterials, and development of biotechnological processes. Resonant X-ray scattering provides a chemically-specific approach to characterize complex biological structures based on anomalous or resonant scattering from a specific element or chemical moiety. Anomalous or resonant diffraction can provide structural details with high atomic resolution, while resonant X-ray scattering can provide structural details with lower resolution through tender or soft X-rays. Furthermore, we review applications, challenges, and opportunities for resonant X-ray scattering in the field of structural biology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Free Energies of Hydrated Halide Anions: High Through-Put Computations on Clusters to Treat Rough Energy-Landscapes

With a longer-term goal of addressing the comparative behavior of the aqueous halides F−, Cl−, Br−, and I− on the basis of quasi-chemical theory (QCT), here we study structures and free energies of hydration clusters for those anions. We confirm that energetically optimal (H2O)nX clusters, with X = Cl−, Br−, and I−, exhibit surface hydration structures. Computed free energies, based on optimized surface hydration structures utilizing a harmonic approximation, typically (but not always) disagree with experimental free energies. To remedy the harmonic approximation, we utilize single-point electronic structure calculations on cluster geometries sampled from an AIMD (ab initio molecular dynamics) simulation stream. This rough-landscape procedure is broadly satisfactory and suggests unfavorable ligand crowding as the physical effect addressed. Nevertheless, this procedure can break down when n≳4, with the characteristic discrepancy resulting from a relaxed definition of clustering in the identification of (H2O)nX clusters, including ramified structures natural in physical cluster theories. With ramified structures, the central equation for the present rough-landscape approach can acquire some inconsistency. Extension of these physical cluster theories in the direction of QCT should remedy that issue, and should be the next step in this research direction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Level structure of light neutron-rich La isotopes beyond the 𝑁 = 82 shell closure

Here, the high spin excited states of Lanthanum isotopes 140–143 La, above the 𝑁 = 82 closed shell, were populated in fission reactions. The prompt 𝛾-ray transitions were measured using two complementary methods: (a) in coincidence with the isotopically identified fragments produced in the fission of the 238 U + 9 Be system using the Variable Mode Spectrometer (VAMOS++) and the Advanced Gamma Tracking Array (AGATA) spectrometer, and (b) high statistics threefold 𝛾−𝛾−𝛾 and fourfold 𝛾−𝛾−𝛾−𝛾 coincidence data from the spontaneous fission of 252 Cf using the Gammasphere. This work reports the first identification of a pair of parity doublet structures in 143 La and the new high spin level structure in 140–142 La from prompt 𝛾-ray spectroscopy. The level structures are interpreted in terms of the systematics of neighboring odd-𝑍 nuclei above the 𝑍 = 50 shell closure and large-scale shell model calculations. The present results indicate the presence of stable octupole deformation in 143 La. The excitation energy pattern and their comparison with neighboring isotones, moving away from the 𝑁 = 82 closed shell, point towards a transition from single-particle structures to an alternating parity rotational band structure in the La isotopic chain.

Navin, A. [Centre National de la Recherche Scienti↗
DOE OSTI2020

Revealing the structure of a catalytic combustion active-site ensemble combining uniform nanocrystal catalysts and theory insights

Supported metal catalysts are extensively used in industrial and environmental applications. To improve their performance, it is crucial to identify the most active sites. This identification is, however, made challenging by the presence of a large number of potential surface structures that complicate such an assignment. Often, the active site is formed by an ensemble of atoms, thus introducing further complications in its identification. Being able to produce uniform structures and identify the ones that are responsible for the catalyst performance is a crucial goal. Here, we utilize a combination of uniform Pd/Pt nanocrystal catalysts and theory to reveal the catalytic active-site ensemble in highly active propene combustion materials. Using colloidal chemistry to exquisitely control nanoparticle size, we find that intrinsic rates for propene combustion in the presence of water increase monotonically with particle size on Pt-rich catalysts, suggesting that the reaction is structure dependent. We also reveal that water has a near-zero or mildly positive reaction rate order over Pd/Pt catalysts. Theory insights allow us to determine that the interaction of water with extended terraces present in large particles leads to the formation of step sites on metallic surfaces. These specific step-edge sites are responsible for the efficient combustion of propene at low temperature. This work reveals an elusive geometric ensemble, thus clearly identifying the active site in alkene combustion catalysts. These insights demonstrate how the combination of uniform catalysts and theory can provide a much deeper understanding of active-site geometry for many applications

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗