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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Is the Matrix Completion of Reduced Density Matrices Unique?

Reduced density matrices are central to describing observables in many-body quantum systems. In electronic structure theory, the two-particle reduced density matrix (2-RDM) suffices to determine the energy and other key properties. Recent work has used matrix completion, leveraging the low-rank structure of RDMs and approximate theoretical models, to reconstruct the 2-RDM from partial data and thus reduce the computational cost. However, matrix completion is, in general, an under-determined problem. Revisiting Rosina’s theorem (Rosina, M. Queen’s Papers on Pure and Applied Mathematics , 1968, No. 11, 369), we here show that the matrix completion is unique under certain conditions, identifying the subset of 2-RDM elements that enables its exact reconstruction from incomplete information. Building on this, we introduce a hybrid quantum–stochastic algorithm that achieves exact matrix completion, demonstrated through applications to the Fermi–Hubbard model.

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

Scanning tunneling microscopy study of epitaxial Fe 3 GeTe 2 monolayers on Bi 2 Te 3

Abstract Introducing magnetism to the surface state of topological insulators, such as Bi 2 Te 3 , can lead to a variety of interesting phenomena. We use scanning tunneling microscopy (STM) to study a single quintuple layer (QL) of the van der Waals magnet Fe 3 GeTe 2 (FGT) that is grown on Bi 2 Te 3 via molecular beam epitaxy. STM topographic images show that the FGT grows as free-standing islands on Bi 2 Te 3 and outwards from Bi 2 Te 3 steps. Atomic resolution imaging shows triangular lattices of 390 ± 10 pm for FGT and 430 ± 10 pm for Bi 2 Te 3 , consistent with the respective bulk crystals. A moiré pattern is observed on FGT regions with a periodicity of 4.3 ± 0.4 nm that can be attributed solely to this lattice mismatch and thus indicates zero rotational misalignment. While most of the surface is covered by a single QL of the FGT, there are small double QL regions, as well as regions with distinct chemical terminations due to an incomplete QL. The most common partial QL surface termination is the FeGe layer, in which the top two atomic layers are missing. This termination has a distinctive electronic structure and a 3 x 3 R 30 ∘ reconstruction overlaid on the moiré pattern in STM images. Magnetic circular dichroism measurements confirm these thin FGT films are ferromagnetic withT C ∼190 K.

Materials Science↗
DOE OSTI2021

Automatic 3D image registration for nano-resolution chemical mapping using synchrotron spectro-tomography

Nano-resolution synchrotron X-ray spectro-tomography has been demonstrated as a powerful tool for probing the three-dimensional (3D) structural and chemical heterogeneity of a sample. By reconstructing a number of tomographic data sets recorded at different X-ray energy levels, the energy-dependent intensity variation in every given voxel fingerprints the corresponding local chemistry. The resolution and accuracy of this method, however, could be jeopardized by non-ideal experimental conditions, e.g. instability in the hardware system and/or in the sample itself. Herein is presented one such case, in which unanticipated sample deformation severely degrades the data quality. Here, to address this issue, an automatic 3D image registration method is implemented to evaluate and correct this effect. The method allows the redox heterogeneity in partially delithiated Li x Ta 0.3 Mn 0.4 O 2 battery cathode particles to be revealed with significantly improved fidelity.

3D image registration↗
DOE OSTI2021

Invasion and rapid adaptation of guppies ( Poecilia reticulata ) across the Hawaiian Archipelago

How much does natural selection, as opposed to genetic drift, admixture, and gene flow, contribute to the evolution of invasive species following introduction to a new environment? Here we assess how evolution can shape biological invasions by examining population genomic variation in non-native guppies (Poecilia reticulata) introduced to the Hawaiian Islands approximately a century ago. By examining 18 invasive populations from four Hawaiian islands and four populations from the native range in northern South America, we reconstructed the history of introductions and evaluated population structure as well as the extent of ongoing gene flow across watersheds and among islands. Patterns of differentiation indicate that guppies have developed significant population structure, with little natural or human-mediated gene flow having occurred among populations following introduction. Demographic modeling and admixture graph analyses together suggest that guppies were initially introduced to O‘ahu and Maui and then translocated to Hawai‘i and Kaua‘i. We detected evidence for only one introduction event from the native range, implying that any adaptive evolution in introduced populations likely utilized the genetic variation present in the founding population. Environmental association tests accounting for population structure identified loci exhibiting signatures of adaptive variation related to predators and landscape characteristics but not nutrient regimes. When paired with high estimates of effective population sizes and detectable population structure, the presence of environment-associated loci supports the role of natural selection in shaping contemporary evolution of Hawaiian guppy populations. Our findings indicate that local adaptation may engender invasion success, particularly in species with life histories that facilitate rapid evolution. Finally, evidence of low gene flow between populations suggests that removal could be an effective approach to control invasive guppies across the Hawaiian archipelago.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Scintillation light in SBND: simulation, reconstruction, and expected performance of the photon detection system

SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Tomo2Mesh: Fast Reconstruction and Visualization of Tomography Data in Mesh Format

Tomo2Mesh is an open-source project targeted towards real-time reconstruction, segmentation, and visualization of computed tomography (CT) data in mesh format. The CT reconstruction scheme is based on filtered back-projection of voxel subsets. The segmentation scheme uses a 3D convolutional neural network. To allow for fast, real-time reconstruction, voxel subsets are first identified by coarse reconstruction. The detail in specific regions of interest is improved through full reconstruction of voxel subsets in that region. Data structures are implemented to store and process voxel subsets. Finally, voxel data is labeled using connected components to detect disconnected regions such as voids whose morphological attributes (e.g., Feret diameter, principal axis orientation, local number density, size, etc.) can be measured also in real-time. Finally, a fast marching cubes implementation processes labeled voxel data into a triangular face mesh (vertices and faces) in .ply format for visualization in Paraview or other mesh visualization tools. The code provides a simple programming interface for detecting, classifying, and visualizing regions of interest based on morphology. For example, detected voids can be classified as round pores or extended cracks. Highly porous neighborhoods can be identified based on local number density. The mesh texture (or color) is assigned based these morphological attributes to allow smart visualization scenarios in real-time (e.g., show only long cracks). At the time of first release (July 2022), extraction of face mesh for visualization for raw CT data from a 2 megapixel camera would take between 1-5 minutes for most scenarios.

TEKAWADE, ANIKET↗
DOE OSTI2026

Machine Learning for Multipactor Susceptibility Prediction in Planar RF Gaps

Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics

Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluation of the Reconstruction Accuracy of the Ultrasound Model–Based Image Reconstruction (U-MBIR) Method for Concrete to Optimize Damage Detection

Reinforced concrete is a critical structural material used to construct nuclear power plants (NPPs). As such, its safety and performance must be thoroughly examined throughout the life cycle of the NPP infrastructure system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To support the development of these techniques, 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. In the work documented in this report, the U-MBIR methodology was applied to four sets of ultrasonic data collected from concrete specimens. The results demonstrate that the U-MBIR algorithm can successfully detect defects within the four concrete samples. The reconstruction images help identify the specimen thickness, regions of delamination, and location of rebar embedded within the concrete. The reconstruction images allow engineers and technicians to characterize the internal defects within concrete specimens and structural members. Ultimately, this knowledge can guide engineers in making informed decisions regarding the performance, safety, and reliability of structural materials (i.e., reinforced concrete) throughout the life cycle of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Assessing the interfacial corrosion mechanism of Inconel 617 in chloride molten salt corrosion using multi-modal advanced characterization techniques

The United States Department of Energy (DOE) has committed to expanding the domestic clean energy portfolio in response to the rising challenges of energy security in the wake of climate change. Accordingly, the construction of a series of Generation IV reactor technologies are being demonstrated, including sodium-cooled, small modular, and molten chloride fast reactors (MCFRs). To date, there are no fully qualified structural materials for constructing MCFRs. A number of commercial structural alloys have been considered for the construction of MCFRs, including alloys from the Inconel and Hastelloy series. Informed qualification of structural materials for the construction of MCFRs in the future can only be ensured by expanding the current fundamental knowledgebase of information pertaining to material performance under environmental stressors relevant to operation of the reactor, including corrosion susceptibility. The purpose of this investigation is to illustrate how a correlative multi-modal electron microscopy characterization approach, including the novel application of focused-ion beam 3D reconstruction capabilities, can elucidate the corrosion mechanism of a candidate structural material Inconel 617 for MCFR in NaCl-MgCl 2 eutectic salt at 700°C for 1,000 h. Evidence of intergranular corrosion, Ni and Fe dealloying, and Cr-O enrichment along the grain boundary, which most likely corresponds to Cr 2 O 3 , is a phenomenon that has been documented in other Ni-based superalloys exposed to chloride molten salt systems. Additional corrosion products, including the formation of insoluble MgAl 2 O 4 , within the porous network produced by the salt attack is a novel observation. In addition, Mo 3 Si 5 and τ 2 precipitates are detected in the alloy bulk and are dissolved by the salt. Furthermore, the lack of detection of design γ' precipitates in Inconel 617 after 1,000 h could indicate that the molten salt corrosion mechanism has indirectly induced a phase transformation of Al 2 TiNi (τ 2 ) and Ni 3 (Al,Ti) (γ’) phase. This investigation provides a comprehensive understanding of molten salt corrosion mechanisms in a complex material system such as a commercial structural alloy for applications in MCFRs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cryogenic electron tomography reveals novel structures in the apical complex of Plasmodium falciparum

Intracellular infectious agents, like the malaria parasite, Plasmodium falciparum, face the daunting challenge of how to invade a host cell. This problem may be even harder when the host cell in question is the enucleated red blood cell, which lacks the host machinery co-opted by many pathogens for internalization. Evolution has provided P. falciparum and related single-celled parasites within the phylum Apicomplexa with a collection of organelles at their apical end that mediate invasion. This apical complex includes at least two sets of secretory organelles, micronemes and rhoptries, and several structural features like apical rings and a putative pore through which proteins may be introduced into the host cell during invasion. We perform cryogenic electron tomography (cryo-ET) equipped with Volta Phase Plate on isolated and vitrified merozoites to visualize the apical machinery. Through tomographic reconstruction of cellular compartments, we see new details of known structures like the rhoptry tip interacting directly with a rosette resembling the recently described rhoptry secretory apparatus (RSA), or with an apical vesicle docked beneath the RSA. Subtomogram averaging reveals that the apical rings have a fixed number of repeating units, each of which is similar in overall size and shape to the units in the apical rings of tachyzoites of Toxoplasma gondii. Comparison of these polar rings in Plasmodium and Toxoplasma parasites also reveals them to have a structurally conserved assembly pattern. These results provide new insight into the essential and structurally conserved features of this remarkable machinery used by apicomplexan parasites to invade their respective host cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Does jackknife scale really matter for accurate large-scale structure covariances?

ABSTRACT The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings impact the precision of the covariance estimate on the correlation function multipoles and the error on the inferred baryon acoustic scale. We compare the measurement with the MultiDark Patchy mock galaxy catalogues, and we also validate it against a set of lognormal mocks with the same survey geometry. We build several jackknife configurations that vary in size and number of resamplings. We introduce the Hartlap factor in the covariance estimate that depends on the number of jackknife resamplings. We also find that it is useful to apply the tapering scheme to estimate the precision matrix from a limited number of resamplings. The results from CMASS and mock catalogues show that the error estimate of the baryon acoustic scale does not depend on the jackknife scale. For the shift parameter α, we find an average error of 1.6 per cent, 2.2 per cent and 1.2 per cent, respectively, from CMASS, Patchy, and lognormal jackknife covariances. Despite these uncertainties fluctuate significantly due to some structural limitations of the jackknife method, our α estimates are in reasonable agreement with published pre-reconstruction analyses. Jackknife methods will provide valuable and complementary covariance estimates for future large-scale structure surveys.

Favole, Ginevra (ORCID:000000028218563X)↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Joint iterative reconstruction and 3D rigid alignment for X-ray tomography

X-ray tomography is widely used for three-dimensional structure determination in many areas of science, from the millimeter to the nanometer scale. The resolution and quality of the 3D reconstruction is limited by the availability of alignment parameters that correct for the mechanical shifts of the sample or sample stage for the images that constitute a scan. In this paper we describe an algorithm for marker-free, fully automated and accurately aligned and reconstructed X-ray tomography data. Our approach solves the tomographic reconstruction jointly with projection data alignment based on a rigid-body deformation model. We demonstrate the robustness of our method on both synthetic phantom and experimental data and show that our method is highly efficient in recovering relatively large alignment errors without prior knowledge of a low resolution approximation of the 3D structure or a reasonable estimate of alignment parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ringing Artifact Reduction Method for Ultrasound Reconstruction Using Multi-Agent Consensus Equilibrium

Non-destructive characterization of multi-layered structures that can be accessed from only a single side is important for applications such as well-bore integrity inspection. Existing methods related to Synthetic Aperture Focusing Technique (SAFT) rapidly produce acceptable results but with significant artifacts. Recently, ultrasound model-based iterative reconstruction (UMBIR) approaches have shown significant improvements over SAFT. However, even these methods produce ringing artifacts due to the high fractional-bandwidth of the excitation signal.In this paper, we propose a ringing artifact reduction method for ultrasound image reconstruction that uses a multi-agent consensus equilibrium (RARE-MACE) framework. Our approach integrates a physics-based forward model that accounts for the propagation of a collimated ultrasonic beam in multi-layered media, a spatially varying image prior, and a denoiser designed to suppress the ringing artifacts that are characteristic of reconstructions from high-fractional bandwidth ultrasound sensor data. We test our method on simulated and experimental measurements and show substantial improvements in image quality compared to SAFT and UMBIR.

Alanazi, Abdulrahman M↗
DOE OSTI2024

Alkali cation-induced cathodic corrosion in Cu electrocatalysts

The reconstruction of Cu catalysts during electrochemical reduction of CO 2 is a widely known but poorly understood phenomenon. Herein, we examine the structural evolution of Cu nanocubes under CO 2 reduction reaction and its relevant reaction conditions using identical location transmission electron microscopy, cyclic voltammetry, in situ X-ray absorption fine structure spectroscopy and ab initio molecular dynamics simulation. Our results suggest that Cu catalysts reconstruct via a hitherto unexplored yet critical pathway - alkali cation-induced cathodic corrosion, when the electrode potential is more negative than an onset value (e.g., –0.4 V RHE when using 0.1 M KHCO 3 ). Having alkali cations in the electrolyte is critical for such a process. Consequently, Cu catalysts will inevitably undergo surface reconstructions during a typical process of CO 2 reduction reaction, resulting in dynamic catalyst morphologies. While having these reconstructions does not necessarily preclude stable electrocatalytic reactions, they will indeed prohibit long-term selectivity and activity enhancement by controlling the morphology of Cu pre-catalysts. Alternatively, by operating Cu catalysts at less negative potentials in the CO electrochemical reduction, we show that Cu nanocubes can provide a much more stable selectivity advantage over spherical Cu nanoparticles.

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

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗