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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Terahertz Dirac Hyperbolic Metamaterial

Hyperbolic metamaterials (HMMs) are engineered materials with a hyperbolic isofrequency surface, enabling a range of interesting phenomena and applications including negative refraction, enhanced sensing, and subdiffraction imaging, focusing, and waveguiding. Existing HMMs primarily work in the visible and infrared spectral range due to the inherent properties of their constituent materials. Here, we demonstrate a THz-range Dirac HMM using topological insulators as the building blocks. Here, we find that the structure houses up to three high-wavevector volume plasmon polariton (VPP) modes, consistent with transfer matrix modeling and effective medium theory calculations. The VPPs have mode indices greater than 100, significantly larger than observed for VPP modes in HMMs made from metals or doped semiconductors while maintaining comparable quality factors. We attribute these properties to the two-dimensional Dirac nature of the electrons occupying the topological insulator surface states. Because these are van der Waals materials, these structures can be grown at a wafer-scale on a variety of substrates, allowing them to be integrated with existing THz structures and enabling next-generation THz optical devices.

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

Demonstrating Advanced Sensors for In-Situ Monitoring Towards Qualification of Nuclear Relevant Components

The U.S. Department of Energy’s Office of Nuclear Energy Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing qualification of laser powder bed fusion (LPBF) components for nuclear applications. A major focus of this effort is the use of in situ process monitoring and machine learning–based tools to establish real-time quality assurance. The primary objective of this report is to identify and evaluate the most relevant in situ sensor systems for LPBF, and to document the deployment of these systems across platforms critical to the AMMT program. This work demonstrates how in situ monitoring can detect process anomalies, track geometry-dependent flaws, and identify limiting combinations of processing parameters—particularly those related to energy density and complex geometries (e.g., overhanging structures). To support this goal, a diverse suite of sensor modalities was evaluated across LPBF platforms, including visible and near-infrared (NIR) imaging, fringe projection profilometry, long-wavelength infrared (LWIR) thermography, and high-speed photodiode/pyrometry systems. These sensor streams were integrated with Peregrine, a machine-agnostic software platform that, among other capabilities, can generate real-time process anomaly classification. This report documents sensor deployments on multiple AMMT flagship platforms, including the Concept Laser M2 and Renishaw AM400/AM250 systems. Calibration builds with complex, flaw-prone geometries such as unsupported overhangs, stepped features, and thin walls, were used to evaluate how well Peregrine and its associated sensors could detect process anomalies and other instabilities under varied energy densities. It will be shown how Peregrine reliably identifies common process anomalies such as recoater streaking, superelevation, etc., and can be used in post-build analysis for anomaly spatial distributions throughout the build height to better understand the impact of geometry and processing parameter choice on the build. This work demonstrates measurable progress toward the vision that components can be born-qualified by establishing a real-time monitoring framework, identifying limiting process conditions, and laying the foundation for sensor fusion–enabled prediction pipelines that are scalable across platforms and applicable to nuclear-relevant components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2024

A Scalable & Non-Destructive Characterization Strategy to Study Semiconductor/Dielectric Interfaces and Predict Wafer-Level Device Performance

The defect density present at the dielectric-semiconductor interface in an MOS structure directly influences the channel carrier characteristics in semiconductor devices, especially in wide bandgap material systems used in power devices. While these trap defects are typically quantified through electrical characterization of MOS-capacitor test structures, this treatment offers very little insight into the physical nature of interface defects. Such shortcomings demand a physical characterization strategy to guide fabrication optimization. X-ray photoelectron spectroscopy (XPS) is suggested as a viable technique to determine chemical data for dielectric interfaces formed using atomic layer deposition (ALD) on GaN substrates. Previously, 1-D XPS characterization has confirmed the presence of a Ga x O y interlayer between ALD dielectrics and the GaN substrate. In this work, XPS data is serially collected to form 2-D images of an ALD-Al 2 O 3 /GaN interface as a proof-of-concept experiment for in-situ XPS quality monitoring during ALD processing. The information provided by this work reveals some of the challenges for incorporating XPS characterization as an in-situ strategy during fabrication of GaN-based devices. Separately, electrical mapping of a 2-D array of ALD-Al 2 O 3 /GaN MOS-capacitor devices provide a means to quantify the spatial variations in interface quality across a single wafer. Physical characterization techniques, such as time-of-flight secondary ion mass spectroscopy, provide additional chemical information about the Al 2 O 3 /Ga x O y /GaN structure that complement the electrical mapping results. This analysis shows that a higher Ga x O y content correlates with higher interface state defects for trap energies deep in the band gap.

42 ENGINEERING↗
DOE OSTI2026

Magneto-optical study of Nb thin films for superconducting qubits

Among the recognized sources of decoherence in superconducting qubits, the spatial inhomogeneity of the superconducting state and the possible presence of magnetic-flux vortices remain comparatively underexplored. Niobium is commonly used as a structural material in transmon qubits that host Josephson junctions, and excess dissipation anywhere in the transmon can become a bottleneck that limits overall quantum performance. The metal/substrate interfacial layer may simultaneously host pair-breaking loss channels (e.g., two-level systems, TLS) and control thermal transport, thereby affecting dissipation and temperature stability. Here, we use quantitative magneto-optical imaging of the magnetic-flux distribution to characterize the homogeneity of the superconducting state and the critical current density, $j_{c}$, in niobium films fabricated under different sputtering conditions. The imaging reveals distinct flux-penetration regimes, ranging from a nearly ideal Bean critical state to strongly nonuniform thermo-magnetic dendritic avalanches. By fitting the measured magnetic-induction profiles, we extract $j_{c}$ and correlate it with film physical properties and with measured qubit internal quality factors. Our results indicate that the Nb/Si interlayer can be a significant contributor to decoherence and should be considered an important factor that must be optimized.

Datta, Amlan [Ames Lab; Iowa State U.]↗
DOE OSTI2026

Magneto-optical study of Nb thin films for superconducting qubits

Abstract Among the recognized sources of decoherence in superconducting qubits, the spatial inhomogeneity of the superconducting state and the possible presence of magnetic-flux vortices remain comparatively underexplored. Niobium is commonly used as a structural material in transmon qubits that host Josephson junctions, and excess dissipation anywhere in the transmon can become a bottleneck that limits overall quantum performance. The metal/substrate interfacial layer may simultaneously host pair-breaking loss channels (e.g. two-level systems) and control thermal transport, thereby affecting dissipation and temperature stability. Here, we use quantitative magneto-optical imaging of the magnetic-flux distribution to characterize the homogeneity of the superconducting state and the critical current density, j c , in niobium films fabricated under different sputtering conditions. The imaging reveals distinct flux-penetration regimes, ranging from a nearly ideal Bean critical state to strongly nonuniform thermo-magnetic dendritic avalanches. By fitting the measured magnetic-induction profiles, we extract j c and try to correlate it with film physical properties and with measured qubit internal quality factors. Our results indicate that the Nb/Si interlayer can be a significant contributor to decoherence and should be considered an important factor that must be optimized.

Datta, Amlan [Ames National Laboratory; Iowa State↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2025

X-ray and Neutron Radiography for Quantitative Material Reconstructions

Radiography is a powerful tool to determine the interior structure of objects. X-ray radiography is widely used and provides high-resolution images, though X-rays have limited transmission through materials of high atomic number (Z) and density. In contrast, neutrons can penetrate many materials that are heavily attenuating to X-rays, such as metals, providing contrast in the inner layers of highly attenuating items. Past work has shown the value in using both X-ray and neutron radiography for estimating material thicknesses, though that work was limited to simulated data. Here, we demonstrate quantitative material reconstructions using experimental X-ray and neutron radiography data from lab-based systems, accurately modeling radiography system responses to within a few percent to enable quantitative measures of material thickness. We demonstrate the utility of neutron radiography and X-ray radiography for these quantitative reconstructions and introduce methods for using their complementarity to improve image quality and optimize experimental design.

Gilbert, Andrew J.↗
DOE OSTI2024

AGR-1 UCO Kernel Phase Analysis Imaging Archive

UCO kernels in tri-structural isotropic (TRISO) particles consist of a heterogeneous mixture of uranium oxide and uranium carbide. During the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program, mean kernel composition was specified based on bulk measurements of uranium, oxygen, and carbon content, as well as the resulting O/U, C/U, and O+C/U ratios. Further development of quality control characterization methods has resulted in a method for more direct measurement of phase fractions on a per-kernel basis using optical microscopy of polished kernel cross sections. This report provides benchmark values for this analysis method when applied to kernels from the AGR-1 campaign and to the raw images used.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Visible-to-THz near-field nanoscopy

Optical microscopy has a key role in research, development and quality control across a wide range of scientific, technological and medical fields. However, diffraction limits the spatial resolution of conventional optical instruments to about half the illumination wavelength. A technique that surpasses the diffraction limit in the wide spectral range between visible and terahertz frequencies is scattering-type scanning near-field optical microscopy (s-SNOM). The basis of s-SNOM is an atomic force microscope in which the tip is illuminated with light from the visible to the terahertz spectral range. By recording the elastically tip-scattered light while scanning the sample below the tip, s-SNOM yields near-field optical images with a remarkable resolution of 10 nm, simultaneously with the standard atomic force microscopic topography image. This resolution is independent of the illumination wavelength, rendering s-SNOM a versatile nanoimaging and nanospectroscopy technique for fundamental and applied studies of materials, structures and phenomena. This Review presents an overview of the fundamental principles governing the measurement and interpretation of near-field contrasts and discusses key applications of s-SNOM. We also showcase emerging developments that enable s-SNOM to operate under various environmental conditions, including cryogenic temperatures, electric and magnetic fields, electrical currents, strain and liquid environments. Furthermore, all these recent developments broaden the applicability of s-SNOMs for exploring fundamental solid-state and quantum phenomena, biological matter, catalytic reactions and more.

Hillenbrand, Rainer [CIC nanoGUNE BRTA and Departm↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2024

Infrared Optical Anisotropy in Quasi‐1D Hexagonal Chalcogenide BaTiSe 3

Polarimetric infrared (IR) detection bolsters IR thermography by leveraging the polarization of light. Optical anisotropy, i.e., birefringence and dichroism, can be leveraged to achieve polarimetric detection. Recently, giant optical anisotropy is discovered in quasi-1D narrow-bandgap hexagonal perovskite sulfides, A 1+x TiS 3 , specifically BaTiS 3 and Sr 9/8 TiS 3 . In these materials, the critical role of atomic-scale structure modulations in the unconventional electrical, optical, and thermal properties raises the broader question of the nature of other materials that belong to this family. To address this issue, for the first time, high-quality single crystals of a largely unexplored member of the A 1+x TiX 3 (X = S, Se) family, BaTiSe 3 are synthesized. Single-crystal X-ray diffraction determined the room-temperature structure with the P31c space group, which is a superstructure of the earlier reported P6 3 /mmc structure. The crystal structure of BaTiSe 3 features antiparallel c-axis displacements similar to but of lower symmetry than BaTiS 3 , verified by the polarization dependent Raman spectroscopy. Fourier transform infrared (FTIR) spectroscopy is used to characterize the optical anisotropy of BaTiSe 3 , whose refractive index along the ordinary (E ⊥ c) and extraordinary (E ‖ c) optical axes is quantitatively determined by combining ellipsometry studies with FTIR. With a giant birefringence Δn ∼ 0.9, BaTiSe 3 emerges as a new candidate for miniaturized birefringent optics for mid-wave infrared to long-wave infrared imaging.

optical anisotropy↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2025

Site-specific plan-view (S)TEM sample preparation from thin films using a dual-beam FIB-SEM

To fully evaluate the atomic structure, and associated properties of materials using transmission electron microscopy, examination of samples from three non-collinear orientations is needed. This is particularly challenging for thin films and nanoscale devices built on substrates due to limitations with plan-view sample preparation. In this work, a new method for preparation of high-quality, site-specific, plan-view TEM samples from thin-films grown on substrates, is presented and discussed. Here, it is based on using a dual-beam focused ion beam scanning electron microscope (FIB-SEM) system. To demonstrate the method, the samples were prepared from thin films of perovskite oxide BaSnO 3 grown on a SrTiO 3 substrate and metal oxide IrO 2 on a TiO 2 substrate, ranging from 20–80 nm in thicknesses using molecular beam epitaxy. While the method is optimized for the thin films, it can be extended to other site-specific plan-view samples and devices build on wafers. Aberration-corrected STEM was used to evaluate the quality of the samples and their applicability for atomic-resolution imaging and analysis.

BaSnO3↗
DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deep generative learning of magnetic frustration in artificial spin ice from magnetic force microscopy images

Increasingly large datasets of microscopic images with nanoscale resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accurately predict magnetic moments and directions within spin-ice structures. Variational Autoencoders (VAEs), an emergent unsupervised deep learning technique, are employed to generate high-quality synthetic magnetic force microscopy (MFM) images and extract latent feature representations, thereby reducing experimental and segmentation errors. The second stage of proposed methodology enables precise identification and prediction of frustrated vertices and nanomagnetic segments, effectively correlating structural and functional aspects of microscopic images. This facilitates the design of optimized spin-ice configurations with controlled frustration patterns, enabling potential on-demand synthesis.

36 MATERIALS SCIENCE↗