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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Topological graph-based analysis of solid-state ion migration

To accelerate the development of ion conducting materials, we present a general graph-theoretic analysis framework for ion migration in any crystalline structure. The nodes of the graph represent metastable sites of the migrating ion and the edges represent discrete migration events between adjacent sites. Starting from a collection of possible metastable migration sites, the framework assigns a weight to the edges by calculating the individual migration energy barriers between those sites. Connected pathways in the periodic simulation cell corresponding to macroscopic ion migration are identified by searching for the lowest-cost cycle in the periodic migration graph. To exemplify the utility of the framework, we present the automatic analyses of Li migration in different polymorphs of VO(PO 4 ), with the resulting identification of two distinct crystal structures with simple migration pathways demonstrating overall <300 meV migration barriers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

CFM-ID 4.0 – a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ~22% better and a compound identification accuracy that is ~35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network

Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Crystallographic Characterization of Sodium Ions in a Bacterial Leucine/Sodium Symporter

Na + is the most abundant ion in living organisms and plays essential roles in regulating nutrient uptake, muscle contraction, and neurotransmission. The identification of Na + in protein structures is crucial for gaining a deeper understanding of protein function in a physiological context. LeuT, a bacterial homolog of the neurotransmitter:sodium symporter family, uses the Na + gradient to power the uptake of amino acids into cells and has been used as a paradigm for the study of Na + -dependent transport systems. We have devised a low-energy multi-crystal approach for characterizing low-Z (Z ≤ 20) anomalous scattering ions such as Na + , Mg 2+ , K + , and Ca 2+ by combining Bijvoet-difference Fourier syntheses for ion detection and f” refinements for ion speciation. Using the approach, we experimentally identify two Na + bound near the central leucine binding site in LeuT. Using LeuT microcrystals, we also demonstrate that Na + may be depleted to study conformational changes in the LeuT transport cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Development of High Energy Density Diaminocyclopropenium‐Phenothiazine Hybrid Catholytes for Non‐Aqueous Redox Flow Batteries

Abstract This report describes the design of diaminocyclopropenium‐phenothiazine hybrid catholytes for non‐aqueous redox flow batteries. The molecules are synthesized in a rapid and modular fashion by appending a diaminocyclopropenium (DAC) substituent to the nitrogen of the phenothiazine. Combining a versatile C‐N coupling protocol (which provides access to diverse derivatives) with computation and structure‐property analysis enabled the identification of a catholyte that displays stable two‐electron cycling at potentials of 0.64 and 1.00 V vs. Fc/Fc + as well as high solubility in all oxidation states (≥0.45 M in TBAPF 6 /MeCN). This catholyte was deployed in a high energy density two‐electron RFB, exhibiting >90 % capacity retention over 266 hours of flow cell cycling at >0.5 M electron concentration.

Yan, Yichao↗
DOE OSTI2021

Development of High Energy Density Diaminocyclopropenium-Phenothiazine Hybrid Catholytes for Non-Aqueous Redox Flow Batteries

Our work describes the design of diaminocyclopropenium-phenothiazine hybrid catholytes for non-aqueous redox flow batteries. The molecules are synthesized in a rapid and modular fashion by appending a diaminocyclopropenium (DAC) substituent to the nitrogen of the phenothiazine. Combining a versatile C-N coupling protocol (which provides access to diverse derivatives) with computation and structure-property analysis enabled the identification of a catholyte that displays stable two-electron cycling at potentials of 0.64 and 1.00 V vs. Fc/Fc + as well as high solubility in all oxidation states (≥0.45 M in TBAPF 6 /MeCN). This catholyte was deployed in a high energy density two-electron RFB, exhibiting >90 % capacity retention over 266 hours of flow cell cycling at >0.5 M electron concentration.

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

Mineralogy-dependent sulfide oxidation via polysulfide and thiosulfate pathways during weathering of mixed-sulfide bearing mine waste rock

Tracking the S oxidation pathway in sulfide-bearing mine waste-rock piles is complicated by variations in water content, O 2 and Fe 3+ concentrations, microbial diversity, mineralogy, the occurrence of a variety of S species associated with incomplete oxidation, and nonlinear coupling between physicochemical processes. Synchrotron-based S K-edge X-ray absorption near edge structure (XANES) spectroscopy facilitates the identification and quantification of S species (i.e., S 2– , $S_{2}O_{2}^{2–}$, S 8 , $S_{2}O_{3}^{2–}$, and $S_{2}O_{4}^{2–}$) in a weathered mine waste-rock pile containing pyrite, sphalerite, galena, pyrrhotite, and chalcopyrite. Mineralogy-dependent polysulfide and thiosulfate pathways both affect the S oxidation within the waste-rock pile. Currently, the polysulfide pathway, with S 8 as the intermediate S species, generates high concentrations of dissolved metals (from the rapid oxidation of monosulfides including sphalerite, galena, pyrrhotite, and chalcopyrite). As these monosulfides are depleted, the thiosulfate pathway of pyrite oxidation, including intermediate oxidation products (i.e., S 8 and $S_{2}O_{3}^{2–}$), will become the dominant oxidation pathway, resulting in the potential for generation of additional acidic drainage. This information highlights the importance of identifying the sulfide-mineral oxidation reaction pathways when attempting to estimate acid generation potential, and when developing remediation strategies for the storage and management of sulfide-bearing waste rock.

58 GEOSCIENCES↗
DOE OSTI2026

Atomic-Scale Imaging of Lithium Vacancies in a Battery Cathode by Multislice Electron Ptychography

Atomic-resolution imaging of battery materials is critical for identification of local defects and structural variations, which are tied to battery performance. However, since battery materials are, by design, optimized to allow ion motion in response to an applied electric field, they are also very sensitive to radiation damage by an electron beam. Image resolution is therefore severely constrained by the dose applied. Here, we show that multislice electron ptychography (MEP) can provide sub-ångström lateral resolution images of both light and heavy elements of a Li-ion battery cathode, along with nanometer-scale depth information and greater dose efficiency than conventional electron microscopy methods. Using the depth-sectioning capability of MEP, we have been able to obtain direct visualizations of Li vacancy clusters, atom column by atom column, in Li x Ni 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) cathodes. This capability to track Li distributions will be valuable in understanding, informing, and optimizing electrode material design for ion storage and transfer.

batteries↗
DOE OSTI2022

Smart Spectral Matching (SSM)

Smart Spectral Matching (SSM) catalogs spectroscopic data and, within the platform, investigates subtle attributes of spectral signatures from Raman and infrared spectroscopic data and enables statistical identification of connections between underlying structural units and spectroscopic information, particularly in fuel cycle materials that are amorphous or a mixture of several phases. Catalogs spectroscopic data, provides UIs for machine learning training either via JupyterHub for notebooks or domain scientist-specific views, machine learning and catalog REST API Python client libraries, and ability to identify features in files uploaded using pre-trained machine learning models. This is a "service-based" architecture with multiple applications represented by each repository in the group https://github.com/smart-spectral-matching

McDonnell, Marshall [Oak Ridge National Lab. (ORNL↗
DOE OSTI2022

Two new marine hermit crabs allied with the Paguristes tortugae complex (Crustacea: Decapoda: Anomura) from the western Atlantic

A recent molecular phylogenetic analysis that focused on selected species of western Atlantic Paguristes Dana, 1851, Areopaguristes Rahayu & McLaughlin, 2010, and Pseudopaguristes McLaughlin, 2002 was somewhat inconclusive regarding relationships among those genera, but it revealed two new unrecognized species genetically related to members of the Paguristes tortugae complex. One of the new species is sister to A. hummi (Wass, 1955), which is readily separated from Wass’ taxon by significant differences in coloration. However, no definitive characters have been found for its identification on the basis of structural morphology. A second new species is genetically sister to P. tortugae Schmitt, 1933, even though it was regarded in earlier literature as no more than an ecomorphic variant expressing protective coloration related to habitat substrate color. In addition to its unique coloration, subtle distinctions are evident in structural morphology. Both species are formally named with accompanying morphological and color descriptions.

Zoology↗
DOE OSTI2025

Machine learning guided selection of broad-spectrum epitope-specific functional antibodies for "Disease X"

Our project established and demonstrated a transfer learning framework that enables prediction of antibody–antigen interactions across related viruses. The approach focused on three major activities: 1. Conserved region and epitope identification – We compared viral protein structures and sequences to identify shared receptor-binding domains and neutralizing epitope regions across variants and related viruses. These conserved features formed the foundation for discovering broadly functional antibodies. 2. Machine learning model development – We built neural network–based models that integrate epitope features with antibody sequence information. Instead of relying solely on structural or physical properties, the models learned transferable patterns that describe antibody binding potential across different viral families. 3. Transfer learning and validation – Using SARS-CoV-2 and Ebola as source systems, we successfully transferred learned epitope features to predict antibody interactions for SARS CoV-1 and Marburg virus. Iterative cycles of dataset generation, retraining, and evaluation improved generalization and predictive power, ensuring the framework can adapt to new threats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance

Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A machining digital twin for hybrid manufacturing

Hybrid manufacturing consisting of metal additively manufactured preforms and computer numerical control (CNC) machining has been established to be an effective method for high material use rates. However, hybrid manufacturing introduces unique challenges. Near-net shape designs are typically selected, which result in a smaller margin for part placement within the stock and stringent requirements for work coordinate system identification. Additionally, less stock material reduces the preform stiffness, which limits the material removal rates during machining. This paper demonstrates a digital twin for CNC machining of a wire arc additively manufactured preform that implements: 1) structured light scanning for stock model identification and tool path generation; 2) a fused filament fabrication apparatus to attach temporary fiducials and scan targets to the preform that enable coordinate system definition for both the CAM and CNC machine; 3) preform and tool tip frequency response function measurements to enable stable milling parameter selection; and 4) post-manufacturing measurements of geometry, surface finish, and structural dynamics to confirm designer intent. These efforts define key components of the machining digital twin for hybrid manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

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

The catalytic domains of Streptococcus mutans glucosyltransferases: a structural analysis

Streptococcus mutans, found in the human oral cavity, is a significant contributor to the pathogenesis of dental caries. This bacterium expresses three genetically distinct types of glucosyltransferases named GtfB (GTF-I), GtfC (GTF-SI) and GtfD (GTF-S) that play critical roles in the development of dental plaque. The catalytic domains of GtfB, GtfC and GtfD contain conserved active-site residues for the overall enzymatic activity that relate to hydrolytic glycosidic cleavage of sucrose to glucose and fructose, release of fructose and generation of a glycosyl-enzyme intermediate in the reducing end. In a subsequent transglycosylation step, the glucosyl moiety is transferred to the nonreducing end of an acceptor to form a growing glucan polymer chain made up of glucose molecules. It has been proposed that both sucrose breakdown and glucan synthesis occur in the same active site of the catalytic domain, although the active site does not appear to be large enough to accommodate both functions. These three enzymes belong to glycoside hydrolase family 70 (GH70), which shows homology to glycoside hydrolase family 13 (GH13). GtfC synthesizes both soluble and insoluble glucans (α-1,3 and α-1,6 glycosidic linkages), while GtfB and GtfD synthesize only insoluble or soluble glucans, respectively. Here, crystal structures of the catalytic domains of GtfB and GtfD are reported. These structures are compared with previously determined structures of the catalytic domain of GtfC. With this work, apo structures and inhibitor-complex structures with acarbose are now available for the catalytic domains of GtfC and GtfB. The structure of GtfC with maltose allows further identification and comparison of active-site residues. A model of sucrose binding to GtfB is also included. The new structure of the catalytic domain of GtfD affords a structural comparison of the three S. mutans glycosyltransferases. Unfortunately, the catalytic domain of GtfD is not complete since crystallization resulted in the structure of a truncated protein lacking approximately 200 N-terminal residues of domain IV.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗