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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Abstract Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Local and long-range atomic/magnetic structure of non-stoichiometric spinel iron oxide nanocrystallites

Spinel iron oxide nanoparticles of different mean sizes in the range 10–25 nm have been prepared by surfactant-free up-scalable near- and super-critical hydrothermal synthesis pathways and characterized using a wide range of advanced structural characterization methods to provide a highly detailed structural description. The atomic structure is examined by combined Rietveld analysis of synchrotron powder X-ray diffraction (PXRD) data and time-of-flight neutron powder-diffraction (NPD) data. The local atomic ordering is further analysed by pair distribution function (PDF) analysis of both X-ray and neutron total-scattering data. It is observed that a non-stoichiometric structural model based on a tetragonal γ-Fe 2 O 3 phase with vacancy ordering in the structure (space group P 4 3 2 1 2) yields the best fit to the PXRD and total-scattering data. Detailed peak-profile analysis reveals a shorter coherence length for the superstructure, which may be attributed to the vacancy-ordered domains being smaller than the size of the crystallites and/or the presence of anti-phase boundaries, faulting or other disorder effects. The intermediate stoichiometry between that of γ-Fe 2 O 3 and Fe 3 O 4 is confirmed by refinement of the Fe/O stoichiometry in the scattering data and quantitative analysis of Mössbauer spectra. The structural characterization is complemented by nano/micro-structural analysis using transmission electron microscopy (TEM), elemental mapping using scanning TEM, energy-dispersive X-ray spectroscopy and the measurement of macroscopic magnetic properties using vibrating sample magnetometry. Notably, no evidence is found of a Fe 3 O 4 /γ-Fe 2 O 3 core-shell nanostructure being present, which had previously been suggested for non-stoichiometric spinel iron oxide nanoparticles. Finally, the study is concluded using the magnetic PDF (mPDF) method to model the neutron total-scattering data and determine the local magnetic ordering and magnetic domain sizes in the iron oxide nanoparticles. The mPDF data analysis reveals ferrimagnetic collinear ordering of the spins in the structure and the magnetic domain sizes to be ∼60–70% of the total nanoparticle sizes. The present study is the first in which mPDF analysis has been applied to magnetic nanoparticles, establishing a successful precedent for future studies of magnetic nanoparticles using this technique.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Spall and subsequent recompaction of copper under shock loading

Research was undertaken to study spall and subsequent recompaction of oxygen-free high-thermal-conductivity copper using a single-stage large-bore light gas gun capable of planar impacts. Gun experiments were conducted that produced an initial spall in the target with a subsequent recompaction of the spall damage/layer by the use of layered impactors. Symmetric spall experiments at similar conditions were also conducted as a control to the recompaction experiments. Photonic Doppler velocimetry was used to obtain the velocity history of the target’s back surface; these velocity histories were analyzed, and the results were compared to numerical simulations. After recovery of selected samples, the microstructure was analyzed using optical imaging microscopy and electron backscatter diffraction. Recompaction waves were clearly observed in the time-resolved data obtained at the back surface of the targets. Two different series of experiments were performed to understand the effect of damage morphology on the stress required for spall recompaction. In the initial series, the peak pressure of the first shock was systematically increased to alter the amount of damage in the material. While in the second series, the peak pressure of the second shock was increased, keeping the magnitude of the first shock relatively constant to understand the effect of peak stress on the recompaction process. Findings clearly show that recompaction results in a perturbed band of the microstructure at the location of the expected spall plane in all cases except where the peak stress of the second shock is lower than the critical value.

, recompaction↗
DOE OSTI2021

Mutagenesis of Human Cytomegalovirus Glycoprotein L Disproportionately Disrupts gH/gL/gO over gH/gL/pUL128-131

Herein, cell-free and cell-to-cell spread of herpesviruses involves a core fusion apparatus comprised of the fusion protein glycoprotein B (gB) and the regulatory factor gH/gL. The human cytomegalovirus (HCMV) gH/gL/gO and gH/gL/pUL128-131 facilitate spread in different cell types. The gO and pUL128-131 components bind distinct receptors, but how the gH/gL portions of the complexes functionally compare is not understood. We previously characterized a panel of gL mutants by transient expression and showed that many were impaired for gH/gL-gB-dependent cell-cell fusion but were still able to form gH/gL/pUL128-131 and induce receptor interference. Here, the gL mutants were engineered into the HCMV BAC clones TB40/e-BAC4 (TB), TR, and Merlin (ME), which differ in their utilization of the two complexes for entry and spread. Several of the gL mutations disproportionately impacted gH/gL/gO-dependent entry and spread over gH/gL/pUL128-131 processes. The effects of some mutants could be explained by impaired gH/gL/gO assembly, but other mutants impacted gH/gL/gO function. Soluble gH/gL/gO containing the L201 mutant failed to block HCMV infection despite unimpaired binding to PDGFRα, indicating the existence of other important gH/gL/gO receptors. Another mutant (L139) enhanced the gH/gL/gO-dependent cell-free spread of TR, suggesting a “hyperactive” gH/gL/gO. Recently published crystallography and cryo-electron microscopy studies suggest structural conservation of the gH/gL underlying gH/gL/gO and gH/gL/pUL128-131. However, our data suggest important differences in the gH/gL of the two complexes and support a model in which gH/gL/gO can provide an activation signal for gB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

LDRD 22A1059-068FP Tailoring the Properties of Multi-Phase Materials Through the Use of Correlative Microscopy and Machine Learning - Poster

High strength alloys with good ductility, hardness, and toughness are needed to meet stringent design requirements for extreme environments. One complication in this pursuit is the evidence that metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception to this behavior is found in multiphase alloys that form complex microstructures of mixed phases with variable grain sizes and shapes that provide increased fracture toughness by the arrangement of their constituent elements. We propose to explore this phenomenon using state-of-the-art machine learning (ML) techniques in a new and novel manner to identify and correlate the critical microstructural features in a Titanium-10Vanadium-2Iron-3Aluminum (Ti-10V-2Fe-3Al) alloy that is reported to exhibit high strength and fracture toughness. Additionally, we will employ multiple, complementary characterization techniques such as optical microscopy, electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS) and scanning electron microscopy to provide multi-layer, quantitative ground truth measures of the microstructures. This data will be used to train a Convolutional Neural Network (CNN) in a semi-supervised environment to identify key microstructural features such as ? platelet dimensions and locations and ?/? phase boundaries and correlate those features with the strength and toughness. Here the ? and ? nomenclature refers to hexagonal close pack (hcp) and body center cubic (bcc) crystal structures, respectively. Previous work has focused on popular alloys and typically used one characterization technique. This research is focused on a promising titanium alloy, uses multiple complimentary characterization tools to provide precise microstructural information and correlates to improved fracture toughness. The resulting ML tool can be trained for additional microstructural features, different alloy(s), and or target mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Water-Dependent Blending of Pectin Films: The Mechanics of Conjoined Biopolymers

Biodegradable pectin polymers have been recommended for a variety of biomedical applications, ranging from the delivery of oral drugs to the repair of injured visceral organs. A promising approach to regulate pectin biostability is the blending of pectin films. To investigate the development of conjoined films, we examined the physical properties of high-methoxyl pectin polymer-polymer (homopolymer) interactions at the adhesive interface. Pectin polymers were tested in glass phase (10–13% w/w water content) and gel phase (38–41% w/w water content). The tensile strength of polymer-polymer adhesion was measured after variable development time and compressive force. Regardless of pretest parameters, the adhesive strength of two glass phase films was negligible. In contrast, adhesion testing of two gel phase films resulted in significant tensile adhesion strength (p < 0.01). Adhesion was also observed between glass phase and gel phase films—likely reflecting the diffusion of water from the gel phase to the glass phase films. In studies of the interaction between two gel phase films, the polymer-polymer adhesive strength increased linearly with increasing compressive force (range 10–80 N) (R 2 = 0.956). In contrast, adhesive strength increased logarithmically with time (range 10–10,000 s) (R 2 = 0.913); most of the adhesive strength was observed within minutes of contact. Fracture mechanics demonstrated that the adhesion of two gel phase films resulted in a conjoined film with distinctive physical properties including increased extensibility, decreased stiffness and a 30% increase in the work of cohesion relative to native polymers (p < 0.01). Scanning electron microscopy of the conjoined films demonstrated cross-grain adhesion at the interface between the adhesive homopolymers. These structural and functional data suggest that blended pectin films have emergent physical properties resulting from the cross-grain intermingling of interfacial pectin chains.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing

The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Utah FORGE: Well 16A(78)-32 Core Analysis Results

This dataset features petrographic, X-Ray Diffraction (XRD), and Scanning Electron Microscopy Analyses of core samples from well 16A(78)-32, which serves as the injection well for the Utah FORGE Enhanced Geothermal System (EGS) project. In addition to an overview of the analysis, the following items have been included here: images of 21 entire thin sections in plane-polarized and cross-polarized light from 16 depths, scanning electron microscope back scatter electron photomosaic maps of 21 thin sections from 16 depths, bulk XRD data from 16 depths, clay-sized XRD patterns from 16 depths, an optical petrography report, and scanning electron microscopy and energy dispersive spectroscopy analyses of eight selected samples.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Micropillar Compression of Additively Manufactured 316L Stainless Steels after 2 MeV Proton Irradiation: A Comparison Study between Planar and Cross-Sectional Micropillars

A micropillar compression study with two different techniques was performed on proton-irradiated additively manufactured (AM) 316L stainless steels. The sample was irradiated at 360 °C using 2 MeV protons to 1.8 average displacement per atom (dpa) in the near-surface region. A comparison study with mechanical test and microstructure characterization was made between planar and cross-sectional pillars prepared from the irradiated surface. While a 2 MeV proton irradiation creates a relatively flat damage zone up to 12 µm, the dpa gradient by a factor of 2 leads to significant dpa uncertainty along the pillar height direction for the conventional planar technique. Cross-sectional pillars can significantly reduce such dpa uncertainty. From one single sample, three cross-sectional pillars were able to show dpa-dependent hardening. Furthermore, post-compression transmission electron microscopy allows the determination of the deformation mechanism of individual micropillars. Cross-sectional micropillar compression can be used to study radiation-induced mechanical property changes with better resolution and less data fluctuation.

transmission electron microscopy↗
DOE OSTI2024

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

Abstract The Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench .

Levine, L. E. (ORCID:0000000334484229)↗
DOE OSTI2023

Systematic mapping of the conformational landscape and dynamism of soluble fibrinogen

Background: Fibrinogen is a soluble, multisubunit, and multidomain dimeric protein, which, upon its proteolytic cleavage by thrombin, is converted to insoluble fibrin, initiating polymerization that substantially contributes to clot growth. Fibrinogen contains numerous, transiently accessible “cryptic” epitopes for hemostatic and immunologic proteins, suggesting that fibrinogen exhibits conformational flexibility, which may play functional roles in its temporal and spatial interactions. Hitherto, there have been limited integrative approaches characterizing the solution structure and internal flexibility of fibrinogen. Methods: Here, utilizing a multipronged, biophysical approach involving 2 solution-based techniques, temperature-dependent hydrogen-deuterium exchange mass spectrometry and small angle X-ray scattering, corroborated by negative stain electron microscopy, we present a holistic, conformationally dynamic model of human fibrinogen in solution. Results: Our data reveal 4 major and distinct conformations of fibrinogen accommodated by a high degree of internal protein flexibility along its central scaffold. We propose that the fibrinogen structure in the solution consists of a complex, conformational landscape with multiple local minima. This is further supported by the location of numerous point mutations that are linked to dysfibrinogenemia and posttranslational modifications, residing near the identified fibrinogen flexions. Conclusion: Further, this work provides a molecular basis for the structural “dynamism” of fibrinogen that is expected to influence the broad swath of its functionally diverse macromolecular interactions and fine-tune the structural and mechanical properties of blood clots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tunable structural, morphological and optical properties of undoped, Mn, Ni and Ag-doped CuInS 2 thin films prepared by AACVD

The preparation of high-quality copper indium sulphide, CuInS 2 photo-absorber material with tunable properties for efficient, low-cost, thin film solar cells using scalable methods remains a challenge. In order to address this, high quality off-stoichiometric undoped and Mn, Ni and Ag-doped CuInS 2 (CIS) thin films have been deposited onto glass substrates by aerosol-assisted chemical vapour deposition (AACVD) using metal diethyldithiocarbamate precursors at temperatures of 350, 400 and 450 °C. Data from powder X-ray diffraction (p-XRD), Raman spectroscopy and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) suggest a correlation of morphology and composition of tetragonal phase (chalcopyrite and copper-gold-type) sulphur-deficient microcrystalline CIS thin films. For undoped thin films, near-infrared optical absorption and emission with tunable band gap between 1.32 and 1.42 eV are likely associated with donor-acceptor pairs involving deep or shallow electronic states such as sulphur vacancies (V S •• ), indium-copper anti-sites (In Cu •• ) and indium interstitials (In i ••• ) as donors and copper vacancies (V Cu ') as acceptors. Whilst Mn-doped thin films exhibit minimal tunable properties, Ni and Ag-doped films exhibit tunable morphology, composition and near-infrared absorption for small dopant content. Here, though further studies are required to explore the defect chemistry, these results show the utility of AACVD in tuning structural, morphological and optical properties of undoped, Mn, Ni and Ag-doped CIS thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Alternating-bias assisted annealing of amorphous oxide tunnel junctions

Superconducting quantum bits (qubits) rely on ultra-thin, amorphous oxide tunneling barriers that can have significant inhomogeneities and defects as grown. This can result in relatively large uncertainties and deleterious effects in the circuits, limiting the scalability. Finding a robust solution to the junction reproducibility problem has been a long-standing goal in the field. Here, we demonstrate a transformational technique for controllably tuning the electrical properties of aluminum-oxide tunnel junctions. This is accomplished using a low-voltage, alternating-bias applied individually to the tunnel junctions, with which resistance tuning by more than 70% can be achieved. The data indicates an improvement of coherence and reduction of two-level system defects. Transmission electron microscopy shows that the treated junctions are predominantly amorphous, albeit with a more uniform distribution of alumina coordination across the barrier. This technique is expected to be useful for other devices based on ionic amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Robust design of semi-automated clustering models for 4D-STEM datasets

Materials discovery and design require characterizing material structures at the nanometer and sub-nanometer scale. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) resolves the crystal structure of materials, but many 4D-STEM data analysis pipelines are not suited for the identification of anomalous and unexpected structures. This work introduces improvements to the iterative Non-Negative Matrix Factorization (NMF) method by implementing consensus clustering for ensemble learning. We evaluate the performance of models during parameter tuning and find that consensus clustering improves performance in all cases and is able to recover specific grains missed by the best performing model in the ensemble. The methods introduced in this work can be applied broadly to materials characterization datasets to aid in the design of new materials.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2023

Spatially resolved structural order in low-temperature liquid electrolyte

Determining the degree and the spatial extent of structural order in liquids is a grand challenge. Here, we are able to resolve the structural order in a model organic electrolyte of 1 M lithium hexafluorophosphate (LiP 6 ) dissolved in 1:1 (v/v) ethylene carbonate:diethylcarbonate by developing an integrated method of liquid-phase transmission electron microscopy (TEM), cryo-TEM operated at -30°C, four-dimensional scanning TEM, and data analysis based on deep learning. This study reveals the presence of short-range order (SRO) in the high–salt concentration domains of the liquid electrolyte from liquid phase separation at the low temperature. Molecular dynamics simulations suggest the SRO originates from the Li + - (PF 6 - ) n (n > 2) local structural order induced by high LiP 6 salt concentration.

42 ENGINEERING↗
DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗
DOE OSTI2024

Probing ferroelectric domain structures and their switching dynamics in SrBi2Ta2O9 by in-situ electric biasing in transmission electron microscopy

Abstract Lead-free SrBi 2 Ta 2 O 9 (SBT) has been a promising ferroelectric material for various applications such as electronics and data storage due to its outstanding ferroelectric properties including high fatigue endurance and low leakage current. However, the atomic-scale domain structure and switching dynamics of ferroelectric SBT remain elusive. This study reveals that spontaneous polarization arises from canted bismuth-cation displacements, forming 90° and Ising-type 180° domain walls. Interestingly, topological pairs of ferroelectric vortex and antivortex connect ferroelectric boundaries where three domain walls converge. In situ electrical biasing transmission electron microscopy (TEM) reveals the dominance of 180° switching over 90°, where oxygen octahedral connectivity is protected by ferroelastic energy in the 90° domain wall. Consequently, all 180° domain walls and (anti)vortices are removed, leaving only 90° domain walls in the electrically poled states. Chemical deterioration along domain walls highlights vulnerability of SBT to ferroelectric fatigue. This study provides insight into crucial aspects for practical applications of SBT.

36 MATERIALS SCIENCE↗