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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Stable Co-valorization of Carbon Dioxide and Methane via Dynamic Reconstruction of a Metal Oxide Solid Solution Catalyst

Dry reforming of methane (DRM) is a process that converts two greenhouse gases (methane and carbon dioxide) into syngas, a mixture of H 2 and CO, that can lead to a variety of value-added chemicals. Owing to its endothermic nature, high reaction temperatures up to 800 °C are typically required and the grand challenge lies in developing robust catalysts without sintering and coking-induced deactivation during the long-term on-stream operation. Towards this aim, herein, a robust complex oxide-supported NiCu alloy catalyst was generated in situ during DRM. By leveraging the configurational stability of a solid oxide solution precursor, tightly anchored NiCu bimetallic nanoparticles were in situ exsoluted and acted as the active sites in DRM. The as-afforded catalyst exhibited stable performance for DRM due to the ability to repel coke off the surface as the reaction proceeds. Kinetic experiments along with top surface characterization detail the reconstruction behavior of the solid oxide solution under DRM reaction conditions. In conclusion, the fundamental insights from this work provide guidance on generating resistant and flexible catalysts via in situ active sites formation from easily synthesized metal oxide solid solutions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Discrete Max-Focusing

The Quantum Focusing Conjecture (QFC) lies at the foundation of holography and semiclassical gravity. The QFC implies the Bousso bound and the Quantum Null Energy Condition (QNEC). The QFC also ensures the consistency of the quantum extremal surface prescription and bulk reconstruction in AdS/CFT. However, the central object in the QFC — the expansion of lightrays — is not defined at points where geodesics enter or leave a null congruence. Moreover, the expansion admits three inequivalent quantum extensions in terms of the conditional max, min, and von Neumann entropies.

AdS-CFT Correspondence↗
DOE OSTI2026

3D reconstruction and neural rendering for adversarial machine learning

While evasion attacks on computer vision systems have been widely studied, creating attacks that remain effective under significant changes in viewpoint continues to be challenging. Traditional approaches often rely on affine transformations of images, but these approaches degrade at larger perspective shifts and often produce unrealistic or ineffective perturbations. Recent methods use differentiable renderers to improve viewpoint robustness, but they typically depend on manually constructed 3D models. We introduce a semi-automated pipeline that generates physically printable and perspective-invariant adversarial patches using only a small set of 2D images. Our method integrates 3D reconstruction, neural rendering, adversarial patch optimization, and an object detection victim model into a unified workflow. We use 2D Gaussian Splatting for high fidelity mesh reconstruction and FlexPara for surface parameterization that produces texture maps suitable for patch editing. Together, these components form a fully differentiable pipeline in PyTorch3D that links texture modification to model outputs, enabling efficient optimization of patches that remain effective across many viewpoints. The complete process, from image capture to patch printing and physical evaluation, can be completed within a few hours. We demonstrate the effectiveness of the resulting patches through attacks on the YOLOv8 object detection model and discuss remaining challenges and opportunities for improving robustness and scalability.

Singhvi, Vivaan [ORNL] (ORCID:0009000586288221)↗
DOE OSTI2024

Origin of anomalous magnetotransport in kagome superconductors 𝐴⁢V 3 ⁢Sb 5 (𝐴 = K,Rb,Cs)

Multiple anomalous features in electronic spectra of metals with a kagome lattice structure—van Hove singularities, Dirac points, and flat bands—imply that materials containing this structural motif may lie at a nexus of topological and correlated electron physics. Due to the prospects of such exceptional electronic behavior, the recent discovery of superconductivity coexisting with charge-density wave (CDW) order in the layered kagome metals 𝐴⁢V 3 ⁢Sb 5 (𝐴 = K,Rb,Cs) has attracted considerable attention. Notably, these archetypal kagome metals express unconventional magnetotransport behavior, including an unexpected linear-in-𝐻 diagonal resistivity at low fields, and an even more peculiar, nonmonotonic sign-changing behavior of the Hall resistivity, which has been speculated to arise from a chiral CDW. We argue here that this unusual magnetotransport derives not from such unconventional phenomena, but rather from the unique fermiology of the 𝐴⁢V 3 ⁢Sb 5 materials. Specifically, it is caused by a large, concave hexagonal Fermi surface sheet formed in the close proximity to the van Hove singularities, which is backfolded into a small hexagonal sheet and two large triangular sheets in the CDW state. We introduce and analyze a model of the electronic structure of these Fermi surface sheets that allows for a full analytical treatment within Boltzmann kinetic theory and that enables semi-quantitative fits of our transport data. Specifically, we find that the anomalous magnetotransport behavior is caused by the confluence of strong reduction of the Fermi velocity near the van Hove singularities located near the vertices of the hexagonal sheet and sharp corners in Fermi surface generated by the CDW reconstruction. In conclusion, our analytical approach not only explains the anomalous magnetotransport in the kagome superconductors but also can be extended to a variety of metallic systems hosting singular features in their Fermi surfaces.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

A low-latitude, high-elevation 10Be production-rate calibration from the Rwenzori Mountains, Uganda

Cosmogenic nuclide surface-exposure dating has become a key tool in geomorphology. Robust application of the technique relies on determination of local nuclide production rates using independently dated calibration sites. However, there are few existing calibration sites in certain regions, particularly in low-latitude, high-elevation regions where cosmogenic nuclide production is theoretically more sensitive to changes in Earth's magnetic field and atmospheric conditions. We present a new cosmogenic beryllium-10 (10Be) production-rate calibration from the equatorial Rwenzori Mountains in Uganda. The calibration is based on radiocarbon dating of basal lake sediments (∼21.3 kyr BP) from Lake Mahoma (2900 m asl) and measured 10Be concentrations from boulders on the crest of a moraine which dams the lake. Our results indicate a time-integrated 10Be production rate of 15.83 ± 0.81 at/g/yr at Lake Mahoma. We then use these calibration data and two public online calculators to calculate ages for moraines in a separate Rwenzori valley for which there exists a minimum-limiting radiocarbon age constraint (∼12 kyr BP) on surficial glacial sediments. This enables us to assess the performance of different parameters used to scale production rates spatially and temporally (e.g., scaling frameworks, geomagnetic field reconstructions, atmospheric models) for surface-exposure age calculation in the region. Our results highlight the sensitivity of low-latitude cosmogenic nuclide surface-exposure ages to discrete calculation parameters, such as scaling framework or reconstructions of Earth's magnetic field, and the utility of determining production rates at sites where limiting age data exists for other nearby landforms.

Anderson, N [Dartmouth College]↗
DOE OSTI2025

Evidence of Au(111) topological states in a kagome analogue lattice and their robustness beyond ultra-low temperatures and defect-free conditions

The experimental realization of kagome lattices that exhibit the predicted coexistence of topological states with high electron kinetics and non-dispersing quantum states remains challenging. Additionally, the robustness of these states against structural perturbations has rarely been explored. Here, we report on the formation of an analogue kagome structure via the electrostatic self-assembly of 4,7-dibromobenzo[c]-1,2,5-thiadiazole (2Br-BTD) molecules on Au(111). Local spectroscopic measurements, supported by theoretical calculations, reveal that the weak molecular coupling reshapes the topological-induced Shockley surface state of Au(111) by imposing a (7 × 7) periodicity, resulting in new band crossings. The molecular overlayer favours the opening of electron gaps at these positions manifested as sharp peaks in dI/dV spectra and electron localization in either the hexagonal or triangular sublattices of the kagome structure. To explore the robustness of these topological states, we monitored their stability under varied conditions, including different temperatures, the unaltered herringbone reconstruction of the Au(111) surface and local structural relaxation of the molecular assembly. These results demonstrate the degree of topological protection of these states, which holds potential for fundamental and applied research.

Austin, Dave [University of Central Florida, Orlan↗
DOE OSTI2024

Three-dimensional hard X-ray ptychographic reflectometry imaging on extended mesoscopic surface structures

Many nanodevices and quantum devices, with their sizes often spanning from millimeters down to sub-nanometer, have intricate low-dimensional, non-uniform, or hierarchical structures on surfaces and interfaces. Since their functionalities are dependent on these structures, high-resolution surface-sensitive characterization becomes imperative to gain a comprehensive understanding of the function–structure relationship. We thus developed hard x-ray ptychographic reflectometry imaging, a new technique that merges the high-resolution two-dimensional imaging capabilities of hard x-ray ptychography for extended objects, with the high-resolution depth profiling capabilities of x-ray reflectivity for layered structures. The synergy of these two methods fully leverages both amplitude and phase information from ptychography reconstruction to not only reveal surface topography and localized structures, such as shapes and electron densities, but also yields statistical details, such as interfacial roughness that is not readily accessible through coherent imaging solely. The hard x-ray ptychographic reflectometry imaging is well-suited for three-dimensional imaging of mesoscopic samples, particularly those comprising planar or layered nanostructures on opaque supports, and could also offer a high-resolution surface metrology and defect analysis on semiconductor devices, such as integrated nanocircuits and lithographic photomasks for microchip fabrications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Neural network reconstruction of the DIII-D tokamak plasma boundary using a reduced set of diagnostics

This study investigates the feasibility of reconstructing the last closed flux surface in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of $0.04$ m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to $0.03$ m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in fusion power plants due to diagnostic constraints imposed by the presence of blankets and shielding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. Here we describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

How Did Westward Volcaniclastic Deposits Accumulate in the Deep Sea Following the January 2022 Eruption of Hunga Volcano?

Most volcanic eruptions on Earth take place below the ocean surface and remain largely unobserved. Reconstruction of past submerged eruptions has thus primarily been based on the study of seafloor deposits. Rarely before the 15 January 2022 eruption of Hunga volcano (Kingdom of Tonga) have we been able to categorically link deep-sea deposits to a specific volcanic source. This eruption was the largest in the modern satellite era, producing a 58-km-tall plume, a 20-m high tsunami, and a pressure wave that propagated around the world. The eruption induced the fastest submarine density currents ever measured, which destroyed submarine telecommunication cables and traveled at least 85 km to the west to the neighboring Lau Basin. Here we report findings from a series of remotely operated vehicle dives conducted 4 months after the eruption along the Eastern Lau Spreading Center-Valu Fa Ridge. Hunga-sourced volcaniclastic deposits 7–150 cm in thickness were found at nine sites, and collected. Study of the internal structure, grain size, componentry, glass chemistry, and microfossil assemblages of the cores show that these deposits are the distal portions of at least two ~100-km-runout submarine density currents. We identify distinct physical characteristics of entrained microfossils that demonstrate the dynamics and pathways of the density currents. Microfossil evidence suggests that even the distal parts of the currents were erosive, remobilizing microfossil-concentrated sediments across the Lau Basin. Remobilization by volcaniclastic submarine density currents may thus play a greater role in carbon transport into deep sea basins than previously thought.

58 GEOSCIENCES↗
DOE OSTI2024

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ESMs Latent Space Exploration for Uncertainty Quantification and Spatiotemporal Downscaling

This final report for DOE Award DE-SC0023044 presents advances in two key areas of climate modeling: (1) representative climate model selection and (2) Earth System Model (ESM) downscaling using hybrid AI methods. The first section introduces a reordered, three-stage workflow to select representative GCM runs that more effectively balance historical skill with ensemble spread, validated across Texas, Bihar, and New York. The second section introduces two novel super-resolution frameworks, ViSIR and ViFOR, that integrate Vision Transformers with sinusoidal and Fourier-based implicit neural representations. These models achieve state-of-the-art reconstruction accuracy for ESM variables including surface temperature and heat fluxes. The report includes detailed methodology, benchmarks, and results, demonstrating significant gains in uncertainty quantification, spatial fidelity, and scalability for climate-impact studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2025

The Reliability of Hybrid Functionals for Accurate Fundamental and Optical Gap Prediction of Bulk Solids and Surfaces

Hybrid functionals have been considered insufficiently reliable for the prediction of band gaps in solids and surfaces. We revisit this issue with a new generation of optimally tuned range-separated hybrid functionals, focusing on the reconstructed Si(111)-(2×1) and Ge(111)-(2×1) surfaces. We show that certain hybrid functionals can accurately predict the surface-state and bulk fundamental and optical gaps, as well as projected band structures of these surfaces, by combining ground-state and time-dependent density functional theory.

Sagredo, Francisca↗
DOE OSTI2024

Geometric surprises in the Python's lunch conjecture

A bulge surface, on a time reflection-symmetric Cauchy slice of a holographic spacetime, is a non-minimal extremal surface that occurs between two locally minimal surfaces homologous to a given boundary region. According to the python’s lunch conjecture of Brown et al., the bulge’s area controls the complexity of bulk reconstruction, in the sense of the amount of post-selection that needs to be overcome for the reconstruction of the entanglement wedge beyond the outermost extremal surface. We study the geometry of bulges in a variety of classical spacetimes, and discover a number of surprising features that distinguish them from more familiar extremal surfaces such as Ryu-Takayanagi surfaces: they spontaneously break spatial isometries, both continuous and discrete; they are sensitive to the choice of boundary infrared regulator; they can self-intersect; and they probe entanglement shadows, orbifold singularities, and compact spaces such as the sphere in AdS _p× S^q p × S q . These features imply, according to the python’s lunch conjecture, novel qualitative differences between complexity and entanglement in the holographic context. We also find, surprisingly, that extended black brane interiors have a non-extensive complexity; similarly, for multi-boundary wormhole states, the complexity pleateaus after a certain number of boundaries have been included.

Physics↗
DOE OSTI2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Effect of Annealing on Direct Recycled NMC Cathodes

A substantial number of electric vehicle batteries are poised to reach end-of-life conditions in the next decade. Direct recycling has advantages over typical recycling processes because it preserves the chemical structure of the material. One key step of direct recycling materials like nickel manganese cobalt oxide (NMC) cathodes is relithiation, which includes replacing the depleted lithium inventory in the cathode and annealing the material to fix crystallographic degradation. This work uses multiple characterization techniques (synchrotron X-ray diffraction, Ni X-ray absorption near edge structure, scanning transmission electron microscopy) to understand the lithiation mechanism of degraded and chemically relithiated NMC 622. Despite the necessary reconstruction after relithiation being limited to the surface of the degraded NMC 622, a high annealing temperature of 720 °C is still necessary to restore the NMC 622 structure back to pristine condition after relithiation. This work also finds that treating degraded NMC 622 with an annealing step is sufficient to restore key electrochemical and structural properties, other essential features of pristine NMC 622 material such as particle porosity and morphology are largely unaffected. Understanding the effect of this annealing step has important implications on defining the degree of success of any given direct recycling strategy.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Emergent surface resonance from charge density wave symmetry breaking in TiSe 2

Surface confined electronic states provide a fertile ground for discovering emergent phenomena that have no counterpart in the bulk, offering new routes to manipulate correlations, symmetry breaking, and dimensionality at the atomic scale. Here, in this study, we show that charge density wave (CDW) symmetry breaking can yield surface states in 1⁢T−TiSe 2 . Micro–angle-resolved photoemission spectroscopy (µ-ARPES) resolves a sharp, two-dimensional surface resonant state (SRS) that emerges within the CDW reconstructed low energy spectrum. The SRS exhibits notable temperature dependence and its spectral weight collapses around ∼ 160 K, while CDW transition temperature 𝑇 CDW is commonly reported as ≈ 202 K. Slab DFT + 𝑈 calculations reproduce a surface localized resonance when CDW folding brings valence and conduction states into near degeneracy, suggesting a correlation tuned, surface selective origin. These results point to a form of correlation-tuned surface resonance in a layered CDW compound and suggest a framework for engineering low-dimensional quantum states in van der Waals materials via symmetry breaking and electronic structure tuning.

36 MATERIALS SCIENCE↗