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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

SILICON CARBIDE THERMOMETRY USING RAMAN SPECTROSCOPY ON IRRADIATED TRISO PARTICLES

Silicon carbide (SiC) passive thermometry has emerged as a promising post-irradiation examination (PIE) technique for estimating irradiation temperature near the end of irradiation. While dilatometry techniques have been traditionally used to analyze prismatic samples after irradiation, Raman spectroscopy has recently been shown to provide comparable results by analyzing Raman-active phonon modes. In this work, Raman spectroscopy has been applied to the SiC layer of cross-sectioned irradiation tristructural isotropic (TRISO) particles from the AGR-5/6/7 experiment to evaluate the feasibility of particle-scale passive thermometry. Two-dimensional Raman mapping was used to measure the position of the SiC longitudinal optical (LO) phonon, which was then converted to an apparent irradiation temperature using a previously established empirical correlation. Particles from AGR-5/6/7 Compacts 2-2-1 and 5-1-3 were selected as their calculated time-averaged, volume-averaged (TAVA) temperatures (828°C and 706°C, respectively) fall within the range of the sensitivity of the experimental approach. The use of SiC thermometry was anticipated to confirm or highlight potential deviations from calculated end-of-life TAVA temperature across compacts. Four particles from Compact 2-2-1 and two particles from Compact 5-1-3 were selected based on 110mAg inventory (either some measurable activity or below the minimum detection limit) which is commonly used as an indicator of in-pile temperature variation of particles within the same compact. Across every particle selected it was determined that the average LO peak position was located around 968 cm-1 to 969 cm-1. Using the previously determined empirical correlation, this corresponds to an irradiation temperature around 950°C to 966°C, which does not align with the reported TAVA temperature values. This discrepancy in apparent irradiation temperatures likely reflects a combination of uncertainties in the calculated particle temperatures and differences in irradiation history between the present specimens and those used to establish the empirical Raman calibration such as neutron flux (damage rate) and SiC microstructure (as fabricated and irradiated).

Vawdrey, Josh [ORNL]↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
DOE OSTI2026

Quality and Loss Factor Characterization of YBCO Superconducting Transmission Lines for Axion Dark Matter Detection

Axion haloscope experiments aim to detect the conversion of axions into microwave photons under a strong magnetic field through resonant cavity techniques. The conversion produces an extremely small signal, requiring a cryogenic environment to reduce thermal noise, and low-loss superconducting transmission lines for minimal attenuation in the readout chain. Yttrium barium copper oxide (YBCO) cables are a promising candidate. Unlike commonly used low-loss cryogenic cables such as niobium (Nb) and niobium titanium (NbTi), which have low critical fields, YBCO is a high-temperature superconductor that can remain stable in strong magnetic fields. We characterized the quality and loss of five stripline resonators from the Brookhaven Technology Group: three YBCO, a copper reference, and a silver reference. Experimental methodology included collecting scattering parameter data on a vector network analyzer for the samples at 77 K along with room temperature baseline measurements, and employing both Lorentzian-fit and circle-fitting techniques to confirm the coupling regime and corroborate results. We achieved quality factors on the order of $10^3$ with corresponding loss factors on the order of $10^{-1}$ dB/m. Trends were comparable with literature data on Nb and NbTi at 4 K, which did have lower loss under these conditions, but suggested similar or perhaps better loss for YBCO when accounting for the temperature difference. We also developed a cryogenic probe for future measurements in a 14 T applied field at 30 mK. This will serve to directly evaluate YBCO's durability in magnetic fields, and we expect significantly improved performance at millikelvin temperatures.

Marinos, Z. [UCLA]↗
DOE OSTI2026

Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is #BQP-complete in the worst case. In this work, we introduce QFAMES (quantum filtering and analysis of multiplicities in eigenvalue spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing quantum phases in the transverse-field Ising model and estimating the ground-state degeneracy of a topologically ordered phase in the two-dimensional toric code model. We also generalize QFAMES to the setting of mixed initial states. Our approach offers rigorous theoretical guarantees and significant advantages over existing subspace-based quantum spectral analysis methods, particularly in terms of the sample complexity and the ability to resolve degeneracies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Improved high-gradient performance for medium-velocity superconducting half-wave resonators: Surface preparation and trapped flux mitigation

A development effort to improve the performance of superconducting radio-frequency half-wave resonators (SRF HWRs) is underway at the Facility for Rare Isotope Beams (FRIB), where 220 such resonators are in operation. Our goal was to achieve an intrinsic quality factor (𝑄 0 ) of ≥ 2 × 10 10 at an accelerating gradient (𝐸 acc ) of 12 MV/m. FRIB production resonators were prepared with buffered chemical polishing. First trials of electropolishing (EP) and post-EP low-temperature baking of FRIB HWRs allowed us to reach higher gradient (15 MV/m, limited by quench) with a higher quality factor at high gradient, but 𝑄 0 was still below our goal. Trapped magnetic flux during the Dewar test was found to be a source of 𝑄 0 reduction. Three strategies were used to reduce the trapped flux: (i) adding a local magnetic shield (LMGS) to supplement the “global” magnetic shield around the Dewar for reduction of the ambient magnetic field; (ii) performing a “uniform cooldown” (UC) to reduce the thermoelectric currents; and (iii) using a compensation coil to further reduce the ambient field with active field cancellation (AFC). The LMGS improved the 𝑄 0 , but not enough to reach our goal. With UC and AFC, we exceeded our goal, reaching 𝑄 0 = 2.8 ×10 10 at 𝐸 acc = 12 MV/m.

Cryogenics & vacuum technology↗
DOE OSTI2025

Robust and tunable oxide nanoscrolls for solar-driven H 2 generation and storage

Hydrogen gas is a promising alternative to fossil fuels due to its high energy output and environmentally safe byproducts. Various morphologies of photocatalytic materials have been explored for high-efficiency H 2 production, for instance, quasi-1D nanoscroll structures that provide a larger surface-to-volume ratio. Recently, we predicted layer-by-layer formation of stable oxide nanoscrolls directly from dichalcogenide precursors, eliminating the need for costly formation of two-dimensional oxides for a roll-up synthesis of nanoscrolls. Here, in this study, we evaluate the suitability of those oxide nanoscroll materials—MoO 3 , WO 3 , PdO 2 , HfO 2 , and GeO 2 —for solar-driven photocatalytic H 2 production and storage. Using excited state theory coupled with Bethe–Salpeter equation simulations, we discern their electronic and optical properties as a function of interlayer scroll spacing and find them to be highly conducive for solar-driven photocatalysis. Additionally, using ab initio molecular dynamics simulations, we show that they are also suitable for H 2 storage as the nanoscrolls exhibit an effective trapping of hydrogen, even in the presence of defects and vacancies in the oxides. This work thus demonstrates the discovery of robust and tunable oxide nanoscrolls as materials for advancing solar-driven hydrogen technologies.

Chemical compounds↗
DOE OSTI2026

Peptoid-Based Nanosheets Exhibiting Broad Antiviral Activity Against Enveloped RNA Viruses

Enveloped RNA viruses, such as Influenza A (H1N1) and Sindbis virus, pose persistent global health threats due to their high mutation rates, efficient transmission, and frequent drug resistance. By mimicking host cell membrane receptors, multivalent virus inhibitors can block viral attachment, making them promising broad-spectrum antiviral agents. However, most of existing antivirals are often limited by strain specificity, short-lived efficacy, and toxicity. Here, we introduce a broad-spectrum antiviral platform based on highly tunable and biocompatible two-dimensional nanomembranes (2DNMs) self-assembled from amphiphilic peptoids, operating via a non-genomic, mutation-insensitive mechanism. By varying peptoid sequence, we design and synthesize over twenty different 2DNMs with various surface charge and high density of viral-attachment ligands (VALs). The self-assembled architecture of these stable 2DNMs provides cooperative noncovalent multivalent binding to virus particles that result in effective inhibition of viral infection. Screening of variants identified three leads that potently suppressed Influenza A (H1N1) and Sindbis virus infection across median tissue culture infectious dose (TCID50), plaque, RT–qPCR, and immunofluorescence assays, while maintaining >90% cell viability. These nanosheets significantly reduced infectious titers, viral RNA replication, and intracellular viral protein expression, indicating inhibition at early stages of viral entry and propagation. The sequence programmability, chemical robustness, and mutation-insensitive antiviral activity distinguish 2DNMs from traditional antivirals and positions them as a versatile materials platform for antiviral coatings, protective barriers, and prophylactic biomedical applications.

Influenza A virus↗
DOE OSTI2026

Computational discovery of a tetragonal MBene phase: diamond-shaped boron lattices, viable MAB precursors, and selective CO 2 reduction

MBenes are a class of two-dimensional transition-metal borides derived from layered MAB phases. Here, we use first-principles calculations to predict a new tetragonal MB (1 : 1) MBene phase featuring a diamond-shaped B–B lattice and systematically assess its phase stability, synthetic accessibility, and catalytic performance towards the CO 2 reduction reaction (CO 2 RR). Four tetragonal configurations are benchmarked against known hexagonal and orthorhombic phases, and a multi-tier screening identifies six robust members (CrB, FeB, MoB, WB, IrB, and PtB). Exploration of the I4/mmm MAB space reveals 11 viable precursors, including the experimentally synthesized Ir 2 ZnB 2 , which validates our screening approach. CO 2 RR free-energy diagrams show that CrB favors CH 3 OH while FeB, MoB, WB, IrB, and PtB preferentially yield HCOOH. The corresponding limiting potentials are competitive with, and in some cases superior to, those reported for state-of-the-art orthorhombic and hexagonal MBenes. These findings expand the MBene landscape by establishing a tetragonal phase with structural, synthetic, and catalytic promise.

Lu, Linguo [University of Puerto Rico, San Juan, P↗
DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗
DOE OSTI2026

Fast Active-Set Thresholding Method for Nonnegative Least Squares

Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.

Cobb, Benjamin [Georgia Institute of Technology]↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2026

Integrated Simulation of Weld Residual Stress Evolution and Crack Propagation Using XFEM

Nuclear power plant components operate in environments that promote multiple degradation mecha- nisms, several of which involve crack initiation and growth. An ongoing effort in the U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is developing a general capability within the Multiphysics Object Oriented Simulation Environment (MOOSE) framework for simulating three-dimensional crack growth under a range of driving conditions, including fatigue, stress corrosion cracking (SCC), brittle fracture, and stress-relaxation cracking. This report demonstrates an end-to-end workflow that uses this capability to model weld-residual-stress-driven SCC in the J-groove weld of a pressurized-water reactor control rod drive mechanism penetration in the vessel head. The workflow consists of a thermomechanical welding simulation with temperature-dependent plasticity, followed by cooldown to ambient conditions, and a restart of the simulation using the MOOSE extended finite element method (XFEM) module to propagate a three-dimensional crack through the residual stress field. New welding capabilities were developed to properly initialize newly activated elements in the weld region, and robustness improvements were made to the mesh-based algorithm for defining cutting planes in the 3D XFEM algorithm, allowing it to handle complex crack fronts and stress fields. Together these advances allowed the simulated SCC crack to grow from an initial elliptical flaw in the weld, across the weld, through the tube wall, and almost to the triple point (where the weld, tube, and reactor pressure vessel head intersect) over roughly 36 years of simulated service. These results demonstrate a workflow that can be extended to fully three-dimensional welding simulations and more complex crack interaction problems.

42 - ENGINEERING↗
DOE OSTI2026

Dynamics of Radiation Damage Buildup in Ultrathin Hexagonal Boron Nitride Films under Ion Bombardment

Two-dimensional hexagonal boron nitride (hBN) is attractive for several emerging applications. Ion bombardment can be used to modify the hBN properties. However, the understanding of radiation damage buildup in hBN remains limited. Here, we investigate the effects of the dose rate and ion mass on radiation damage buildup by studying 40 nm-thick hBN films bombarded at room temperature with 500 keV 4 He, 15 N, 40 Ar, and 129 Xe ions and comparing with results for ion bombardment of polycrystalline hBN ceramics. Raman spectroscopy is used to quantify damage buildup, and transmission electron microscopy is used for microstructural analysis. Experiments are complemented by molecular dynamics simulations of the formation and evolution of point defects. Lighter ions are found to be more efficient at disordering hBN than heavier ions. This observation points to a critical role of intracascade defect processes. In contrast, a negligible dose rate effect observed suggests limited intercascade defect dynamic annealing processes for these irradiation conditions. These findings provide a fundamental basis for hBN defect engineering.

2D materials↗
DOE OSTI2024

Exciton Transport in Perovskite Materials

Halide perovskites have emerged as promising materials for a wide variety of optoelectronic applications, including solar cells, light‐emitting devices, photodetectors, and quantum information applications. In addition to their desirable optical and electronic properties, halide perovskites provide tremendous synthetic flexibility through variation of not only their chemical composition but also their structure and morphology. At the heart of their use in optoelectronic technologies is the interaction of light with electronic excitations in the form of excitons. This review discusses the properties and behavior of excitons in halide perovskite materials, with a particular emphasis on low‐dimensional perovskites and the effects of nanoscale morphology on excitonic behavior. The basic theory of excitonic energy migration in semiconductor nanomaterials is introduced, and novel observations in halide perovskite nanomaterials that have evolved our current understanding are explored. Lastly, many important questions that remain unanswered are presented and exciting emerging directions in low‐dimensional perovskite exciton physics are discussed.

2D materials↗
DOE OSTI2024

Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks

Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

Science & Technology - Other Topics↗
DOE OSTI2026

A Novel Low-Profile High-Efficiency Three-Phase Matrix Transformer

High step-down isolated DC-DC conversion from an 800 V DC bus to low-voltage, high-current outputs is required in automotive auxiliary converters and data center power supplies. In such applications, conventional transformer-based converters require large turns ratios, which increase winding resistance, leakage inductance, and magnetic height. This paper proposes a novel low-profile three-phase matrix transformer that realizes a large effective voltage ratio through flux division among multiple secondary legs, without increasing the physical turns count of each winding. As a result, the proposed structure reduces copper usage and transformer height while preserving the voltage conversion capability of a conventional three-phase transformer. Finite element analysis shows that the proposed design reduces magnetic height by 27%, ferrite volume by 34%, and copper volume by 28%. Circuit-level simulations of an 800 V/12 V,3 kW CLLLC dual-active-bridge converter further show that the lower winding resistance reduces total system loss by 91% and increases DC-DC efficiency from 82.6% to 97.6% at 3 kW output.

Inoue, Shuntaro [ORNL] (ORCID:0000000262637627)↗
DOE OSTI2024

The effect of gamma ray irradiation on few layered MoSe2: A material for nuclear and space applications

In recent years, emerging two-dimensional (2D) materials, such as molybdenum diselenide (MoSe2), have been at the center of attention for many researchers. This is due to their unique and fascinating physicochemical properties that make them attractive in space and defense applications that include shielding harsh irradiation environments. In this study, we examined the effects of gamma (γ) rays at various doses on the structural, chemical, and optical properties of MoSe2 layers. After the samples were exposed to intense gamma radiation (from a 60Co source) with various exposure times to vary the total accumulated dosage (up to 100 kGy), Raman and photoluminescence spectroscopies were used to study and probe radiation-induced changes to the samples. When compared to pristine materials, very few changes in optical properties were typically observed, indicating good robustness with little sensitivity, even at relatively high doses of gamma radiation. The imaging using scanning electron microscopy revealed a number of nano-hillocks that were connected to substrate alterations. X-ray photoelectron spectroscopies revealed that Mo’s binding energies remained the same, but Se’s binding energies blueshifted. We associated this shift with the decrease in Se vacancies that occurred after irradiation as a result of Mo atoms creating adatoms next to Se atoms. When compared to pristine materials, very few changes in optical, chemical, and structural properties were typically observed. These findings highlight the inherent resilience of MoSe2 in hostile radioactive conditions, which spurs additional research into their optical, electrical, and structural characteristics as well as exploration for potential space, energy, and defense applications.

Materials Science↗
DOE OSTI2026

Emergence of low-energy spin waves in superconducting electron-doped cuprates

In order to fully utilize the technological potential of unconventional superconductors, an enhanced understanding of the superconducting mechanism is necessary. In the best performing superconductors, the cuprates, superconductivity is intimately linked with magnetism, although the details of this coupling remain elusive. Here, we address this gap by studying the electron-doped cuprate Nd 1.85 Ce 0.15 CuO 4−δ that has an antiferromagnetic ground state when synthesized and only becomes superconducting after a reductive annealing process. Using neutron spectroscopy, we show that the as-grown crystal exhibits a large spin pseudogap in the magnetic fluctuation spectrum. Annealing removes defects introduced by the commonly employed synthesis method and significantly reduces the spin pseudogap. While the spin pseudogap in the annealed sample likely arises from superconductivity, in the as-grown sample it results from the absence of long-wavelength spin waves. These results reveal a direct connection between defects, magnetism, and superconductivity, offering new insight into the mechanisms underlying high-temperature superconductivity and guiding the design of improved superconducting materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗