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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Direct Imaging of Asymmetric Interfaces and Electrostatic Potentials inside a Hafnia–Zirconia Ferroelectric Nanocapacitor

In hafnia-based thin-film ferroelectric devices, chemical phenomena during growth and processing, such as oxygen vacancy formation and interfacial reactions, appear to strongly affect device performance. However, the correlation between the structure, chemistry, and electrical potentials at the nanoscale in these devices is not fully known, making it difficult to understand their influence on device properties. Here, we directly image the composition and electrostatic potential with nanometer resolution in the cross section of a nanocrystalline W/Hf 0.5 Zr 0.5 O 2−δ (HZO)/W ferroelectric capacitor using multimodal electron microscopy. This reveals a 1.4 nm wide tungsten suboxide interfacial layer formed at the bottom interface during fabrication, which introduces a potential dip and leads to asymmetric switching fields. Additionally, we compare the measured potentials to DFT calculations and find it is nearly 3 V lower than expected in the HZO, which appears to be caused by oxygen vacancies and a resulting negative built-in potential. In conclusion, these chemical and electrostatic details are important to characterize and tune to achieve high-performance ferroelectric devices.

Defects in solids↗
DOE OSTI2024

Distinguishing the XUV-induced Coulomb explosion dynamics of iodobenzene using covariance analysis

The primary and secondary fragmentation dynamics of iodobenzene following its ionization at 120 eV were determined using three-dimensional velocity map imaging and covariance analysis. Site-selective iodine 4d ionization was used to populate a range of excited polycationic parent states, which primarily broke apart at the carbon-iodine bond to produce I+ with phenyl or phenyl-like cations (C n H x + or C n H, with n = 1 – 6 and x = 1 – 5). The molecular products were produced with varying degrees of internal excitation and dehydrogenation, leading to stable and unstable outcomes. This further allowed the secondary dynamics of C 6 H x 2+ intermediates to be distinguished using native-frame covariance analysis, which isolated these processes in their own centre-of-mass reference frames. Furthermore, the mass resolution of the imaging mass spectrometer used for these measurements enabled the primary and secondary reaction channels to be specified at the level of individual hydrogen atoms, demonstrating the ability of covariance analysis to comprehensively measure the competing fragmentation channels of aryl cations, including those involving intermediate steps.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Characterization of latent image of electron beam resist via critical-dimension resonant soft X-ray scattering

Metrology plays a crucial role in semiconductor manufacturing by providing accurate and precise measurement and characterization of critical parameters. With the development of high-resolution extreme ultraviolet lithography (EUVL) processes, critical dimensions are shrinking to sub-10 nm. Resist materials encounter the challenge of providing heightened sensitivity and a handle on exacerbating stochastic variations. A comprehensive understanding of the chemical profile of the latent image is pivotal for mitigating stochastic effects and optimizing pattern quality. However, the subtle differences in chemistry between the exposed and unexposed regions of the resists make it extremely challenging to characterize the latent images with sub-nanometer precision. Here, we develop the metrology with critical-dimension resonant soft X-ray scattering (CD-RSoXS) to probe the chemical profiles of latent images stored in resist after exposure. The combination of absorption spectroscopy and enhanced scattering contrast makes it possible to characterize the subtle structural and chemical variations in the latent image. Moreover, the results of the measurements are compared with the simulations with a finite element method–based Maxwell solver to extract a detailed profile of the latent and developed images. We demonstrate that the CD-RSoXS technique can provide valuable insights into the high spatial resolution and local chemical sensitivity simultaneously, which is crucial to understanding the resolution limits and stochastic effects in EUVL processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018

The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes?This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES).The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

2018 National Ecological Observatory Network Campa↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

Computational fluid dynamics↗
DOE OSTI2024

Visualization of post-detonation fireball flowfields and comparison to CFD modeling

Visualization of flow structures within post-detonation fireballs has been performed for benchmark validation of numerical simulations. Custom pressed PETN explosives with a 12-mm diameter hemispherical form factor were used to produce a spherically symmetric post-detonation flow with low soot yield. Hydroxyl-radical planar laser induce fluorescence (OH-PLIF) was employed to visualize the structure ranging from approximately 10 μs to 35 μs after shock breakout from the explosive pellet. Fireball simulations were performed using the HyBurn Computational Fluid Dynamics (CFD) package. Experimental OH-PLIF results were compared to synthetic OH-PLIF from post-processing of CFD simulations. From the comparison of experimental and synthetic OH-PLIF images, CFD is shown to replicate much of the flow structure observed in the experiments, revealing potential differences in turbulent length scales and OH kinetics. Furthermore, results provide significant advancement in experimental resolution of these harsh turbulent combustion environments and validate physical models thereof.

Detonator↗
DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗
DOE OSTI2025

Pose Estimation for the Inspecta Project

In this addendum report we take the lessons learned from our initial generic object pose estimation research described in (Smartt, et al., 2025) and apply it to metal cup seals, which are attached to items after an international nuclear safeguards inspection (i.e., metal cup seals may be attached to containers of spent nuclear fuel after an inspector has measured and verified the material). We have also refined our software pipeline and training techniques since the final Inspecta report and will describe that process here. The objective of this task is to allow a robot to acquire images of the engraved seal characters to confirm that the correct seal is attached to the correct container. However, the seal may be attached in any orientation and therefore the robot manipulator arm with a camera needs to align with the normal face of the seal.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Projector Selection for SOFAST Testing

In Concentrating Solar Power (CSP) there is a need for mirrors that are optically accurate, as they are used to focus light onto a receiver at high concentration. To test the accuracy of the mirrors’ optical slopes, we employ a technique called deflectometry. Deflectometry measures mirror slope directly. A projector displays an image onto a screen or wall, and a mirror is positioned to reflect the image to a camera. The camera sees the mirror’s reflection of the screen and sends imaging information to software which analyzes the reflected images from the mirror to determine the local slopes and curvature of the mirror. For this, projectors that can provide a crisp and clear image are imperative. Choosing the right projector(s) can be a long and tedious process; this paper will cover the process we used to select projectors for the SOFAST systems in the Sandia Concentrating Solar Optics Laboratory (CSOL).

14 SOLAR ENERGY↗
DOE OSTI2026

Optimized manufacturing process for multilayer two-dimensional focusing mirrors in laboratory X-ray applications

Recent advances in laboratory X-ray applications require high-performance optical components that achieve exceptional imaging resolution and beam uniformity within compact experimental setups. Montel mirrors have become a preferred solution due to their unique dual-reflection focusing mechanism and a space-efficient design. Here, in this study, we present an effective manufacturing process for producing Montel mirrors tailored to focus laboratory X-ray beams. The mirrors were fabricated from single-crystal silicon substrates, chosen for their high mechanical stability and compatibility with precision polishing techniques. Our approach begins with the integration of a deterministic chemo-mechanical polishing (CMP)-based pre-shaping step followed by ion beam figuring (IBF), significantly improving manufacturing efficiency. Subsequently, our custom-developed advanced metrology and IBF techniques were employed for fabricating an off-axis, elliptical cylinder Montel mirror system with a 6-mrad total slope, with stringent optical specifications. While post-IBF processes, including multilayer coating, dicing, and gluing, introduced minor surface errors, yet their impact on performance remained negligible. The Montel mirrors manufactured with the optimized process exhibited significantly improved beam uniformity and a reduced focal spot size. These findings validate our approach as a viable solution for high-precision Montel mirror fabrication and facilitate further advancements in laboratory X-ray applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure

Swings in pH can be achieved by electrically polarizing a bipolar membrane (BPM) to drive water dissociation at the BPM junction for electrochemical conversion and separation processes. BPM junction design is critical to tailor performance for specific applications; however, characterization techniques capable of resolving the nanometer scale physical structure of the junction are limited. We present sample preparation, imaging, and analysis workflows that are adaptable to a variety of BPM junction architectures. Atomic force microscopy produces BPM junction images with nanometer scale lateral resolution for samples with and without a graphene oxide water dissociation catalyst in the junction. Subsequent image segmentation and analysis quantify line edge roughness and catalyst layer thickness as descriptors of junction structure. Comparison of pre- and post-electrodialysis junctions suggests electric field-induced alignment of catalyst particles during electrodialysis. This characterization workflow can inform manufacturing protocols, computational modeling, and failure mode analysis for next-generation BPMs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Radiation Imaging with Event Camera

Neuromorphic or event-based imaging is a new, commercially available sensor technology inspired by how the human eye works. Instead of measuring frames at a fixed rate, the camera measures changes in pixel intensity asynchronously. This difference in readout architecture results in a high dynamic range and low latency. Event-based cameras have been used in a variety of applications, including object tracking, navigation, and lidar technologies. However, event-based cameras have not been adequately researched for their ability to image high-energy particles. This report explores the use of an event camera for imaging alpha, beta, and X-rays particles, when coupled with scintillator screens to convert high-energy particles into visible light. Methods to process event data were developed and are presented here, along with the results. The event camera can measure alpha and beta particles with comparable performance to that of a conventional camera. Event cameras can also image higher-activity sources and offer the possibility of discriminating particle interaction types on the basis of timing differences, which typical cameras cannot do. Additionally, event cameras can image objects with an X-ray source when the source strength dynamically changes but does not create a high-contrast image during static X-ray measurement.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach

Protocol optimization is critical in Computed Tomography (CT) for achieving desired diagnostic image quality while minimizing radiation dose. Due to the inter-effect of influencing CT parameters, traditional optimization methods rely on the testing of exhaustive combinations of these parameters. This poses a notable limitation due to the impracticality of exhaustive parameter testing. This study introduces a novel methodology leveraging Virtual Imaging Trials (VITs) and reinforcement learning to more efficiently optimize CT protocols. Computational phantoms with liver lesions were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction Toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization process was done using a Proximal Policy Optimization (PPO) agent which was trained to maximize the Detectability Index (d’) of the liver lesion for each reconstructed image. Results showed that our reinforcement learning approach found the absolute maximum d’ across the test cases while requiring 79.7% fewer steps compared to an exhaustive search, demonstrating both accuracy and computational efficiency, offering a efficient and robust framework for CT protocol optimization. The flexibility of the proposed technique allows for use of varying image quality metrics as the objective metric to maximize for. Our findings highlight the advantages of combining VIT and reinforcement learning for CT protocol management.

Fenwick, David [Duke University Medical Center]↗
DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗
DOE OSTI2024

Enhancing NDE Reliability for Grade 91 Steel Welds: Ultrasonic Imaging and Microstructural Correlations

Ensuring the integrity of Grade 91 (9Cr-1Mo-V) steel welds is vital for the safe and reliable operation of fossil fuel–fired and nuclear power plants. Here, this study applies an imaging technique for the ultrasonic characterization of two Grade 91 steel welds created with cold metal transfer and flux-cored arc welding processes. Ultrasonic immersion testing in the through-transmission configuration was employed to generate shear waves, which helped identify the weld metal, heat-affected zone, and base metal regions. These weld microstructures were also correlated to their ultrasonic images using metallography, ultrasonic amplitude, hardness measurements, and grain size. The findings from this study can assist practitioners in developing new nondestructive evaluation technologies, improving the inspection reliability of creep strength–enhanced ferritic steel welds by potentially identifying weld microstructure regions susceptible to creep-type failures.

Grade 91 steel↗
DOE OSTI2025

Synthesis‐Related Nanoscale Defects in Mo‐Based Janus Monolayers Revealed by Cross‐Correlated AFM and TERS Imaging

2D Janus transition metal dichalcogenides (TMDs) are promising candidates for various applications including non-linear optics, energy harvesting, and catalysis. These materials are usually synthesized via chemical conversion of pristine TMDs. Nanometer-scale characterization of the obtained Janus materials’ morphology and local composition is crucial for both the synthesis optimization and the future device applications. In this work, we present the results of cross-correlated atomic force microscopy (AFM) and tip-enhanced Raman spectroscopy (TERS) study of Janus monolayers synthesized by the hydrogen plasma-assisted chemical conversion of MoSe 2 and MoS 2 . We demonstrate that the choice of both the growth substrate and the starting TMD influences the residual strain, thereby shaping the nanoscale morphology of the resulting Janus material. Furthermore, by employing TERS imaging, we show the presence of nanoscale islands (≈20 nm across) of MoSe 2 - ${\mathrm{Mo}}_{{\mathrm{Se}}}^{\mathrm{S}}$ (MoS 2 -${\mathrm{Mo}}_{\mathrm{S}}^{{\mathrm{Se}}}$) vertical heterostructures originating from the bilayer nanoislands in the precursor monolayer crystals. The understanding of the origins of nanoscale defects in Janus TMDs revealed in this study can help with further optimization of the Janus conversion process towards uniform and wrinkle-/crack-free Janus materials. Moreover, this work shows that cross-correlated AFM and TERS imaging is a powerful and accessible method for studying nanoscale composition and defects in Janus TMD monolayers.

2D materials↗