Search NASA⌕ Search

SEARCH · Search NASA

Results for “structure reconstruction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantitative uranium elemental reconstruction using spectral x-ray radiography

X-ray radiography offers the ability to noninvasively inspect the internal structure and material composition of objects. Dual-energy radiography allows material discrimination but is limited in quantitative material measurements due to the broad nature of the interrogating x-ray spectrum. Spectral radiography detectors provide an observation of not just the number of x rays that are transmitted through a sample but also the spectrum. Here, experimental validations of a method to use spectral x-ray radiography to accurately quantify the uranium mass in a powder are presented. An accurate system response model was developed, which includes a model of the incident x-ray source spectrum and a pixel-wise detector response that describes how the transmitted x rays were observed. Further, a calibration to account for the effect of partial x-ray attenuation was developed. As a result, the method is demonstrated to be capable of estimating the uranium mass in a variety of uranium oxide powders to a bias of –0.01±0.62%.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Real-time sparse-sampled Ptychographic imaging through deep neural networks

Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample. A ptychographic reconstruction is achieved by means of solving a complex inverse problem that imposes constraints both on the acquisition and on the analysis of the data, which typically precludes real-time imaging due to computational cost involved in solving this inverse problem. In this work we propose PtychoNN, a novel approach to solve the ptychography reconstruction problem based on deep convolutional neural networks. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. The presented results demonstrate how PtychoNN can effectively be used on experimental data, being able to generate high quality reconstructions of a sample up to hundreds of times faster than state-of-the-art ptychography reconstruction solutions once trained. By surpassing the typical constraints of iterative model-based methods, we can significantly relax the data acquisition sampling conditions and produce equally satisfactory reconstructions. Besides drastically accelerating acquisition and analysis, this capability can enable new imaging scenarios that were not possible before, in cases of dose sensitive, dynamic and extremely voluminous samples.

Cherukara, Mathew J↗
DOE OSTI2020

K-space algorithmic reconstruction (KAREN): a robust statistical methodology to separate Bragg and diffuse scattering

Diffuse scattering occurring in the Bragg diffraction pattern of a long-range-ordered structure represents local deviation from the governing regular lattice. However, interpreting the real-space structure from the diffraction pattern presents a significant challenge because of the dramatic difference in intensity between the Bragg and diffuse components of the total scattering function. In contrast to the sharp Bragg diffraction, the diffuse signal has generally been considered to be a weak expansive or continuous background signal. In this paper, using 1D and 2D models, it is demonstrated that diffuse scattering in fact consists of a complex array of high-frequency features that must not be averaged into a low-frequency background signal. To evaluate the actual diffuse scattering effectively, an algorithm has been developed that uses robust statistics and traditional signal processing techniques to identify Bragg peaks as signal outliers which can be removed from the overall scattering data and then replaced by statistically valid fill values. This method, described as a 'K-space algorithmic reconstruction' (KAREN), can identify Bragg reflections independent of prior knowledge of a system's unit cell. KAREN does not alter any data other than that in the immediate vicinity of the Bragg reflections, and reconstructs the diffuse component surrounding the Bragg peaks without introducing discontinuities which induce Fourier ripples or artifacts from underfilling `punched' voids. The KAREN algorithm for reconstructing diffuse scattering provides demonstrably better resolution than can be obtained from previously described punch-and-fill methods. The superior structural resolution obtained using the KAREN method is demonstrated by evaluating the complex ordered diffuse scattering observed from the neutron diffraction of a single plastic crystal of CBr 4 using pair distribution function analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Complete Performance Comparison Between the Optimized Image Construction Algorithm (U-MBIR) and the Existing Reconstruction Algorithm for Detecting Defects and Damage in Concrete

Reinforced concrete (RC) is a composite material subjected to mechanical, thermal, and chemical loads throughout its service life. Because of these external stressors and the susceptibility of RC structural members to shrinkage and microcracking, the material degrades throughout its life cycle. This deterioration can lead to a decrease in member capacity and, ultimately, poses a threat to the structural integrity. Thus, it is crucial that the damage caused by aging and degradation be monitored and assessed at regular intervals throughout the material’s service life. Since coring of the material is typically not feasible for in-service structural systems, non-destructive evaluation (NDE) methodologies are used to assess remaining structural capacity. NDE methods enable surface and subsurface examination without damaging or degrading the medium. Moreover, RC is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To this end, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. More specifically, this report presents a comparison between reconstruction images produced via a widely employed ultrasonic NDE technique—the synthetic aperture focusing technique (SAFT)—and the ORNL-developed U-MBIR algorithm. These NDE methodologies are demonstrated using ultrasonic data collected from four concrete specimens. Overall, the U-MBIR algorithm eliminates artifacts and noise that are typically present within the SAFT reconstructions, and it shows defects and anomalies more clearly than the SAFT images. In conclusion, this algorithm is suitable for identifying concrete defects, although more improvements and optimization could be done to better define internal defects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM)

Abstract Motivation Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. Results Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. Availability and implementation Software and data available at https://github.com/lp1611/mol_count_dstorm. Supplementary information Supplementary data are available at Bioinformatics online.

Patel, Lekha↗
DOE OSTI2020

Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom

The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. In this work, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

(CrMnCoNiTi) 3 O 4 high-entropy spinel oxide as a high-performance electrode for supercapacitors

In this study, spinel-structured (CrMnCoNiTi)₃O₄ high-entropy oxides (HEOs) have been successfully synthesized using the solution combustion method, and their performance as supercapacitor electrode materials was investigated. These HEOs exhibit excellent performance, stemming from the unique high-entropy effect and lattice distortion. This structure effectively buffers volumetric strain during cycling, conferring the material with extremely high structural stability and specific capacity. The material demonstrates a high specific capacity of 491.5 C∙g⁻¹ at a current density of 0.5 A∙g⁻¹. Furthermore, it shows extraordinary cycling stability: after 10,000 charge/discharge cycles at 5 A∙g⁻¹, the capacity retention rate is 99%, and the Coulombic efficiency consistently remains close to 100%. Ex-situ structural characterizations further reveal that this excellent cycling durability originates from a cooperative multination valence reconstruction mechanism within an entropy-stabilized spinel framework. Additionally, a symmetric supercapacitor assembled using this material and 1 M KOH electrolyte successfully extended the working voltage to 1.2 V. This research highlights the great potential of high-entropy oxides in synergizing high energy density and ultra-long cycling life, laying a solid foundation for the development of next-generation high-performance supercapacitor electrode materials.

High entropy oxides↗
DOE OSTI2024

Important Role of Ion Flux Regulated by Separators in Lithium Metal Batteries

Abstract Polyolefin separators are the most common separators used in rechargeable lithium (Li)‐ion batteries. However, the influence of different polyolefin separators on the performance of Li metal batteries (LMBs) has not been well studied. By performing particle injection simulations on the reconstructed three‐dimensional pores of different polyethylene separators, it is revealed that the pore structure of the separator has a significant impact on the ion flux distribution, the Li deposition behavior, and consequently, the cycle life of LMBs. It is also discovered that the homogeneity factor of Li‐ion toward Li metal electrode is positively correlated to the longevity and reproducibility of LMBs. This work not only emphasizes the importance of the pore structure of polyolefin separators but also provides an economic and effective method to screen favorable separators for LMBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Bulky Cation-Modified Interfaces for Thermally Stable Lead Halide Perovskite Solar Cells

Charged conjugated organic molecules offer promising prospects for reducing nonradiative recombination at interfaces in perovskite solar cells, while protecting the active layer from moisture. However, several studies have shown that the heat induced diffusion of these cations leads to irreversible solar cell degradation. Passivation molecules for perovskite can reconstruct the film surface into lower-dimensional phases when exposed to thermal stress, impeding charge extraction and affecting the photoconversion efficiency (PCE) of devices. In this work, we study how molecular interactions between passivation molecules and 3D CsFAPbI 3 perovskite impact stability and charge extraction at the perovskite/hole transport layer interfaces. Two model π- conjugated molecules are studied: 2-([2,2′-bithiophen]-5-yl)ethan-1-aminium iodide (2TI) and 2-(3‴,4′-dimethyl-[2,2′:5′,2″:5″,2‴- quaterthiophen]-5-yl)ethan-1-ammonium iodide (4TmI). We demonstrate that the speed of surface layer reconstruction under thermal stress can be controlled by the cation size and correlate these structural changes with the solar cell performance and stability. Devices treated with 2TI and 4TmI achieve PCEs over 21% and maintain their performance under thermal stress. Our findings demonstrate that thermal stability in PSCs can be achieved via the design engineering of passivation agents, offering a blueprint for developing next-generation passivation molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding Reactivities of Ni-rich Li[NixMnyCo1-x-y]O2 Single-Crystal Cathode Materials

While Ni-rich Li[NixMnyCo1-x-y]O2 (NMC, x ? 0.8) compounds are considered the most promising cathode materials for high-energy lithium-ion batteries (LIBs), a significant challenge is the higher reactivities caused by the increased Ni content, especially under high-voltage operation conditions. In the present study, we synthesize three single-crystal NMC (SC-NMC) samples with the same particle size and morphology: LiNi0.8Mn0.1Co0.1O2 (NMC811), LiNi0.80Mn0.15Co0.05O2 (NMC80155) and LiNi0.85Mn0.10Co0.05O2 (NMC85105). By systematically varying the composition while controlling other properties, the role of each transition metal during air exposure, thermal treatment and long-term cycling is clearly demonstrated. We reveal that while higher Ni content leads to an overall increased reactivities, the presence of Mn provides a stabilizing effect on thermal, structural and chemical properties. In the absence of cycling-induced particle cracking, surface reconstruction is shown to be the dominating contributor to cathode capacity fade. Our study provides key insights needed for the development of better-performing Ni-rich NMC cathode materials.

Kim, Minkyung↗
DOE OSTI2023

Thermal equation of state of ice-VII revisited by single-crystal X-ray diffraction

Abstract Ice-VII is a high-pressure polymorph of H2O ice and an important mineral widely present in many planetary environments, such as in the interiors of large icy planetary bodies, within some cold subducted slabs, and in diamonds of deep origin as mineral inclusions. However, its stability at high pressures and high temperatures and thermoelastic properties are still under debate. In this study, we synthesized ice-VII single crystals in externally heated diamond-anvil cells and conducted single-crystal X-ray diffraction experiments up to 78 GPa and 1000 K to revisit the high-pressure and high-temperature phase stability and thermoelastic properties of ice-VII. No obvious unit-cell volume discontinuity or strain anomaly of the high-pressure ice was observed up to the highest achieved pressures and temperatures. The volume-pressure-temperature data were fitted to a high-temperature Birch-Murnaghan equation of state formalism, yielding bulk modulus KT0 = 21.0(4) GPa, its first pressure derivative KT0′ = 4.45(6), dK/dT = –0.009(4) GPa/K, and thermal expansion relation αT = 15(5) × 10–5 + 15(8) × 10–8 × (T – 300) K–1. The determined phase stability and thermoelastic properties of ice-VII can be used to model the inner structure of icy cosmic bodies. Combined with the thermoelastic properties of diamonds, we can reconstruct the isomeke P-T paths of ice-VII inclusions in diamond from depth, offering clues on the water-rich regions in Earth’s deep mantle and the formation environments of those diamonds.

Geochemistry & Geophysics↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2020

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Monomer and dimer structures of cytochrome bo 3 ubiquinol oxidase from Escherichia coli

Abstract The Escherichia coli cytochrome bo 3 ubiquinol oxidase is a four‐subunit heme‐copper oxidase that serves as a proton pump in the E. coli aerobic respiratory chain. Despite many mechanistic studies, it is unclear whether this ubiquinol oxidase functions as a monomer, or as a dimer in a manner similar to its eukaryotic counterparts—the mitochondrial electron transport complexes. In this study, we determined the monomeric and dimeric structures of the E. coli cytochrome bo 3 ubiquinol oxidase reconstituted in amphipol by cryogenic electron microscopy single particle reconstruction (cryo‐EM SPR) to a resolution of 3.15 and 3.46 Å, respectively. We have discovered that the protein can form a dimer with C2 symmetry, with the dimerization interface maintained by interactions between the subunit II of one monomer and the subunit IV of the other monomer. Moreover, the dimerization does not induce significant structural changes in the monomers, except the movement of a loop in subunit IV (residues 67–74).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Growth and structural studies of In/Au(111) alloys and InO x /Au(111) inverse oxide/metal model catalysts

Indium oxide has received attention as an exciting candidate for catalyzing the CO 2 hydrogenation to methanol due to its high selectivity (>80%). Compared to the extent of research on the activity of indium oxide-based powder catalysts, very little is known about the phenomena associated with the formation of surface alloys involving indium or the growth mechanism for indium oxide nanoparticles. In this report, scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS) were employed to elucidate the growth mode, structure, and chemical state of In/Au(111) alloys and InO x /Au(111) inverse model catalysts. Here, our study reveals distinct morphological differences between In/Au(111) and InO x /Au(111), and the InO x structure also depends strongly on the preparation conditions. In/Au surface alloy systems with extremely low coverage (0.02 ML) form islands preferentially on the elbow sites of reconstructed Au(111) herringbone, regardless of hcp and fcc stacking. At higher coverage (0.1 ML), the In islands expand over the herringbone in the <110> direction and create two dimensional domain structures over the entire surfaces. Moreover, this 2D domain structure is disturbed by temperature with high dispersion of indium atoms observed during the annealing process. Oxidation of the In/Au(111) surface alloys with O 2 at 550 K produces InO x /Au(111) systems which contain various sizes of InO x aggregates (from 0.7 to 10 nm). On the other hand, InO x /Au(111) surfaces prepared by vapor deposition of In at 550 K in an O 2 background exhibit highly dispersed and uniformly small InO x particles (~1 nm). Both InO x systems were confirmed to be partially oxidized by XPS.

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

Coregistered positron emission particle tracking (PEPT) and X-ray computed tomography (CT) for engineering flow measurements

Increasingly, fully 3D experimental measurements of flow in complex engineering geometries are required to validate computational fluid dynamics models that support and inform reactor design and licensing. One barrier to such measurements is the complexity of typical reactor components and subsequent lack of optical access in these systems. To overcome this, the deployment of coregistered positron emission particle tracking (PEPT) and X-ray computed tomography (CT) is explored for flow measurement in reactor thermal hydraulic components and model (scaled) systems. Through this methodology, fully 3D flow information (via PEPT) and detailed internal geometry (via CT) are captured in opaque systems such as pipes, rod bundles, packed beds, etc. The reconstructed flow field and geometry can then be overlain to reveal detailed flow features around internal structures within a given test section. This is enabled through the use of a combined preclinical PET/CT scanner with overlapping PET and CT fields of view. Such measurements are useful for characterizing flow inside such intricate nuclear thermal hydraulic components as core geometries and heat exchangers, among others, and providing valuable 3D validation data for CFD models. In this work, basic tests of this 3D flow/geometry mapping are presented, and the implications of such measurements are discussed. Further, preliminary measurements are made with both point sources and flow in a simple pipe flow geometry to evaluate the capabilities of this technique. PEPT and CT features are coregistered with up to 0.1 mm precision, and pipe flow mean velocity and Reynolds stresses are reconstructed with similar accuracy to previous PEPT demonstrations. The utility of PEPT/CT is shown herein, and suggestions for future measurements are made.

3D flow measurement↗