Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “3D image analysis”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating Cryo–TEM Reconstruction Accuracy of Self–Assembled Polymer Nanostructures

Cryogenic transmission electron microscopy (cryo–TEM) combined with single particle analysis (SPA) is an emerging imaging approach for soft materials. However, the accuracy of SPA–reconstructed nanostructures, particularly those formed by synthetic polymers, remains uncertain due to potential packing heterogeneity of the nanostructures. In this study, the combination of molecular dynamics (MD) simulations and image simulations is utilized to validate the accuracy of cryo–TEM 3D reconstructions of self–assembled polypeptoid fibril nanostructures. Using CryoSPARC software, image simulations, 2D classifications, ab initio reconstructions, and homogenous refinements are performed. By comparing the results with atomic models, the recovery of molecular details is assessed, heterogeneous structures are identified, and the influence of extraction location on the reconstructions is evaluated. In conclusion, these findings confirm the fidelity of single particle analysis in accurately resolving complex structural characteristics and heterogeneous structures, exhibiting its potential as a valuable tool for detailed structural analysis of synthetic polymers and soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The 3D clustering of Lyman Alpha Emitters measured with DESI

We present a clustering analysis of Lyman-$α$ emitters (LAEs) using spectroscopic observations from the Dark Energy Spectroscopic Instrument (DESI) of candidates selected from the Blanco/DECam Intermediate-Band Imaging Survey (IBIS). We measure the two-point correlation function and the power spectrum, including cross-correlations with DESI quasars. Using both analytical and halo occupation distribution (HOD) simulation-based modeling, we find a linear bias of $b \sim 2.31$--$2.62$ for LAEs over the redshift range $2.26 < z < 3.41$. The analytical modeling also provides constraints on the strength of radiative transfer effects, while the HOD analysis characterizes the LAE-halo connection across multiple models. Finally, we quantify the magnitude of non-perturbative clustering effects such as Fingers of God in the LAE population, providing essential input for the accurate modeling of LAE-based cosmological analyses in forthcoming high-redshift surveys such as DESI-II.

Ebina, H. [UC, Berkeley; LBL, Berkeley] (ORCID:000↗
DOE OSTI2023

MATBOX, an Open-Source Microstructure Analysis Toolbox for Meshing, Generation, Segmentation, and Characterization of 3D Heterogenous Volumes

Battery performance is strongly correlated with electrode microstructural properties. To account for its impact, lithium-ion battery (LIB) models either abstract the microstructural heterogeneity of composite electrodes using effective macroscopic properties (macro- or meso- scale models) or directly solve the system of equations on the microstructure geometry or mesh (microstructure-scale models). Therefore, to be adequate, both families of models require information from the microstructure geometry, which can be provided by the numerical tool presented in this work. MATBOX is a MATLAB open-source application [1] developed by NREL for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization and correlation, visualization, and microstructure meshing. MATBOX was originally developed for the analysis of LIB electrode microstructures; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphical-User Interface, requires no coding by the user, and is well documented. This presentation will illustrate various MATBOX features for the characterization of a LIB electrode, including a fully automated Representative Volume Element (RVE) analysis, the numerical generation of complex 'virtual' microstructure, including dual-layer electrodes and carbon-binder additive phase, and the meshing of a complex NMC/graphite full cell microstructure suitable for 3D finite-element modeling. Other modules (segmentation, visualization, and correlation) will be briefly presented. Thanks to its modular, open-source approach, MATBOX can easily incorporate third-party algorithms to eventually build a standard in the field that will benefit the whole scientific community. Effective diffusion coefficient [2], additive phase numerical generation [3], and meshing [4] third-party algorithms have been already integrated in the toolbox with more to come.

DIRECT ENERGY CONVERSION,MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sputter-Deposited Mo Thin Films: Multimodal Characterization of Structure, Surface Morphology, Density, Residual Stress, Electrical Resistivity, and Mechanical Response

Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process–structure–property relationships and for designing materials with targeted structures and/or properties. For this data descriptor article, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature coatings, photovoltaics, and microelectronics. In this dataset we explored a process space consisting of 27 unique combinations of sputter power and Ar deposition pressure. Here, the phase, structure, surface morphology, and composition of the Mo thin films were characterized by x-ray diffraction, scanning electron microscopy, atomic force microscopy, and Rutherford backscattering spectrometry. Physical properties—namely, thickness, film stress and sheet resistance—were also measured to provide additional film characteristics and behaviors. Additionally, nanoindentation was utilized to obtain mechanical load-displacement data. The entire dataset consists of 2072 measurements including scalar values (e.g., film stress values), 2D linescans (e.g., x-ray diffractograms), and 3D imagery (e.g., atomic force microscopy images). An additional 1889 quantities, including film hardness, modulus, electrical resistivity, density, and surface roughness, were derived from the experimental datasets using traditional methods. Minimal analysis and discussion of the results are provided in this data descriptor article to limit the authors’ preconceived interpretations of the data. Overall, the data modalities are consistent with previous reports of refractory metal thin films, ensuring that a high-quality dataset was generated. The entirety of this data is committed to a public repository in the Materials Data Facility.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth&rsquo;s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth&rsquo;s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO 2 , ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy&rsquo;s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm 2 for the ARMP and 0.26 g/cm 2 for the AERONET datasets, with bias of 0.072 g/cm 2 and 0.096 g/cm 2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Utah FORGE: Seismic DAS and Geophone Borehole Data Processing and 3D Imaging of Vp/Vs Ratio in the 2024 Stimulated Reservoir

This dataset includes a final report and a 3D velocity model derived from seismic DAS and geophone borehole data collected during the April 2024 stimulation of the reservoir at Utah FORGE. The report details the processing of over 50,000 P- and S-wave travel times used in a tomographic inversion to estimate the Vp/Vs ratio, revealing anomalies adjacent to wells 16A and 16B that may be associated with injected fracturing fluids. Additionally, Wadati analysis of more than 27,000 pairs of differential P- and S-wave travel times supports this interpretation with a high Vp/Vs estimate of 1.86, averaged over the dimensions of the earthquake cluster. The accompanying data file provides a 3D model of P- and S-wave velocities and Vp/Vs ratios, structured with spatial coordinates and velocity values.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
DOE OSTI2025

Using multimodal X-ray computed tomography to advance 3D petrography: A non-destructive investigation of olivine inside a carbonaceous chondrite

Rocks form in three dimensions through time and studying them provides information from inside dynamic systems we cannot otherwise observe. Yet how we typically access the interior of the rocks themselves to gain that information may limit our understanding and influence how we reconstruct the processes that formed them. Here, we demonstrate combined non-destructive 3D X-ray imaging techniques that produce quantitative densitometric and crystallographic maps of entire individual grains inside a rock. Olivine grains throughout a sample of the carbonaceous chondrite Northwest Africa (NWA) 11346 were each characterized by size, shape, composition, zoning intensity, and crystallographic orientation. The addition of 3D crystallographic mapping to calibrated 3D densitometric analysis—used to calculate chemical composition—demonstrates a fully non-destructive petrographic method and provides unique insight. For instance, in our case, using crystallographic data to delineate individual grains and then measuring the 3D size, shape, and composition of each distinguishes variably reset relict grains from those later crystallized after a melting event. Intersection in a 2D slice could not have led to this interpretation because the integration of three-dimensional size, rounding, composition, location, and crystallographic orientation measured from each grain forms the key patterns. Multimodal laboratory X-ray imaging has strong potential to advance 3D petrography.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Flash x-ray radiography analysis of detonation wave propagation in additive-manufactured high explosives

Recent research has demonstrated that additive manufacturing (AM) can be used to produce directionally sensitive high explosives (HEs), but detonation wave propagation in AM HEs with variable internal structure has not been studied. In this work, samples were printed using a 73 wt. % octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine-based HE ink. Flash x-ray radiography imaging was used to observe density changes in two imaging planes of each sample during detonation, and high-speed imaging was used to calculate the detonation velocity at the HE surface. The detonation front initially appears to fail in internal channel regions of the HE, but late reactions occurred in two samples, which increased the material bulk density by 2.8%, possibly due to shock convergence phenomena. The calculated detonation wave pressure similarly increased, but the accuracy of the results is uncertain because of the deviation of the low bulk density printed samples from the cast charge principal isentrope. Here, this work demonstrates that structure can be effectively utilized to guide detonation wave propagation through HE charges.

3D printing↗
DOE OSTI2025

Operando X-ray imaging reveals size-dependent evolution of cobalt oxide thermochemical material during thermal redox cycles

Multivalent metal oxides are promising thermochemical materials (TCMs) for energy storage and conversion owing to their high energy density, air compatibility, and high-temperature stability. Co 3 O 4 serves as a model system for examining particle-size- and structure-dependent redox behavior. While particle size and porosity are known to affect performance, their interplay and the kinetics of pore formation during cycling remain unclear. Here we show the chemical and 3D morphological evolution of Co 3 O 4 micro- and nanoparticles during redox cycles at 800–900 °C using thermal analysis, in-situ synchrotron transmission X-ray microscopy (TXM), and scanning electron microscopy. Thermal analysis shows that nanoparticles re-oxidize more rapidly than microparticles at 800 °C. In-situ nanotomography and chemical imaging reveals that nanoparticles undergo redox conversion without forming internal pores, whereas microparticles develop isolated porosity during reduction. These pores persist through re-oxidation, correlating to a lower conversion rate in subsequent cycles. Our results demonstrate distinct degradation kinetics in Co 3 O 4 micro- and nanoparticles, underscoring the critical role of particle size and porosity in redox performance and informing strategies to enhance the long-term efficiency of metal oxide TCMs.

25 ENERGY STORAGE↗
DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

X-ray Nanocomputed Tomography in Zernike Phase Contrast for Studying 3D Morphology of Li–O 2 Battery Electrode

In this work, we investigated the lithium peroxide (Li 2 O 2 ) and pore size distribution in lithium-O 2 battery electrodes at different states of charge using transmission X-ray microscopy coupled with Zernike phase contrast to carry out nano-Computed Tomography. We report that such technique enables, at the nanoscale, to distinguish light elements such as carbon and Li 2 O 2 in Li-O 2 battery cathode electrodes. We verified by wave-propagation simulation that this approach efficiently improves the contrast of images in comparison with the pure absorption. The Li 2 O 2 distribution and thickness, interphases, and pore network are visualized and quantified, which give a valuable insight into our cathode architecture. From this 3D analysis, we highlight modifications of the air-cathode morphology and the Li 2 O 2 spatial organization as well as their potential implication in terms of carbon surface passivation and pore-clogging. After the full recharge process, this technique can also reveal the spatial distribution of the residual Li 2 O 2 and other byproducts.

25 ENERGY STORAGE↗
DOE OSTI2021

Theory and application of the vector pair correlation function for real-space crystallographic analysis of order/disorder correlations from STEM images

Deviations of local structure and chemistry from the average crystalline unit cell are increasingly recognized to have a significant influence on the properties of many technologically important materials. Here, we present the vector pair correlation function (vPCF) as a new real-space crystallographic analysis method, which can be applied to atomic-resolution scanning transmission electron microscopy (STEM) images to quantify and analyze structural order/disorder correlations. Our STEM-based vPCFs have several advantages over radial PCFs and/or 3D pair distribution functions from x-ray total scattering: vPCFs explicitly retain crystallographic orientation information, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any material that can be imaged with STEM. To show the utility of our approach, we measure partial vPCFs in Ba 5 SmSn 3 Nb 7 O 30 (BSSN), a tetragonal tungsten bronze (TTB) structured complex oxide. Many TTBs are known to be classical or relaxor ferroelectrics, and these properties have been correlated with the presence of superlattice ordering. BSSN, specifically, exhibits relaxor behavior and an incommensurate structural modulation. From the vPCF data, we observe that, of the cation sites, only the Ba (A2) sublattice is structurally modulated. We then infer the local modulation vector and reveal a marked anisotropy in its correlation length. Finally, short-range correlated polar displacements on the B2 cation sites are observed. This work introduces the vPCF as a powerful real-space crystallography technique, which enables direct, robust quantification of short-to-long range order on a sublattice-specific basis and is applicable to a wide range of complex material types.

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

Validation of 3D cryoEM single-particle reconstruction correctness and handedness with Ewald's sphere correction

The correct description of quantum scattering places the observed scattering contributions on the Ewald's sphere and its Friedel mate copy. In electron microscopy, due to the large radius of the Ewald's sphere, these scattering contributions are typically merged during data analysis. We present an approach that separates and factorizes those contributions into real and imaginary components of the image. When an inverted solution is calculated, the map derived from the real component of the image generates an inverted solution, while the map derived from the imaginary component of the image generates an inverted and sign-flipped solution. Therefore, the sign of correlation between reconstructions derived from the real and imaginary components provides the automatic determination of handedness and additional validation for the quality of 3D reconstructions. The factorization and its implementation are robust enough to be routinely used in single-particle reconstructions, even at resolutions below the limit where the curvature of the Ewald's sphere affects the overall signal-to-noise ratio.

Chemistry↗
DOE OSTI2021

SINGLE: Atomic-resolution structure identification of nanocrystals by graphene liquid cell EM

Analysis of the three-dimensional (3D) structures of nanocrystals with solution-phase transmission electron microscopy is beginning to reveal their unique physiochemical properties. We developed a “one-particle Brownian 3D reconstruction method” based on imaging of ensembles of colloidal nanocrystals using graphene liquid cell electron microscopy. Projection images of differently rotated nanocrystals are acquired using a direct electron detector with high temporal (<2.5 ms) resolution and analyzed to obtain an ensemble of 3D reconstructions. Here, we introduce computational methods required for successful atomic-resolution 3D reconstruction: (i) tracking of the individual particles throughout the time series, (ii) subtraction of the interfering background of the graphene liquid cell, (iii) identification and rejection of low-quality images, and (iv) tailored strategies for 2D/3D alignment and averaging that differ from those used in biological cryo–electron microscopy. Our developments are made available through the open-source software package SINGLE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗