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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Robust future intensification of winter precipitation over the United States

We investigate 21st-century hydroclimate changes over the United States (US) during winter and the sources of projection uncertainty under three emission scenarios (SSP2–4.5, SSP3–7.0, and SSP5–8.5) using CMIP6 models. Our study reveals a robust intensification of winter precipitation across the US, except in the Southern Great Plains, where changes are very small. By the end of the 21st century, winter precipitation is projected to increase by about 2–5% K -1 over most of the US. The frequency of very wet winters is also expected to increase, with 6–7 out of 30 winters exceeding the very wet threshold under the different scenarios. Our results suggest that the enhancement of future winter precipitation is modulated largely by coupled dynamic and thermodynamic responses, though partly offset by thermodynamic responses. Overall, our results highlight a high likelihood of increasing impacts from winter precipitation due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Accurately constituting robust interfaces for high-performance high-energy lithium metal batteries

High-energy lithium metal batteries (LMBs) have received ever-increasing interest. Among them, coupling lithium metal (Li) with nickel-rich material, LiNi x Mn y Co z O 2 (NMCs, x ≥ 0.6, x + y + z = 1), is promising because Li anodes enable an extremely high capacity (∼3860 mA h g −1 ) and the lowest redox potential (−3.04 V vs. standard hydrogen electrode), while NMCs can achieve a much higher capacity of ∼200 mA h g −1 and lower cost than those of LiCoO 2 . However, the resultant Li‖NMC cells have been hindered from commercialization due to a series of challenges related to the interface stability of both Li anodes and NMC cathodes. Specifically, Li anodes suffer from Li dendritic growth and the formation of solid electrolyte interphase (SEI), while NMC cathodes suffer from the formation of cathode electrolyte interphase (CEI) and other interface-related issues, including transition metal dissolution, oxygen release, cracking, and so on. To tackle these issues, recently, two sister techniques, atomic and molecular layer deposition (ALD and MLD), have emerged and exhibit tremendous capabilities to accurately constitute robust interfaces to achieve high-performance Li‖NMC LMBs. They can uniquely develop uniform and conformal films as surface coatings of LMBs in a precisely controllable mode at the atomic/molecular level, while proceeding with film deposition at low temperatures (e.g., ≤250 °C). In this Feature Article, we review the latest research progress in developing novel surface coatings via ALD and MLD for Li‖NMC LMBs and discuss outcomes for pursuing high performance.

25 ENERGY STORAGE↗
DOE OSTI2025

Layer-by-layer growth of graphene oxide multilayers using robust interlayer linking chemistry. 2 Zr–bissulfates

We report on the facile layer-by-layer growth of graphene oxide sulfate (GO-S) multilayer structures. The layers are bonded to a modified planar silica support using Zr 4+ ions. Optical ellipsometry shows step-by-step layer growth with layer thickness consistent with that expected from molecular mechanics calculations. The reaction to form Zr–bissulfate linkages is facile, with multistep layer deposition occurring within six to ten minutes per layer. The GO-S layers can be deposited directly on GO-S underlayer(s) or interlayer spacers can be incorporated to control the spacing with GO-S layers. X-ray photoelectron spectroscopy (XPS) data provides information on the density of the sulfonated groups present at the graphene oxide surface and on the Zr : S ratio. This is the first report we are aware of that demonstrates robust layer-by-layer growth of graphene oxide structures.

Graphene oxide↗
DOE OSTI2026

Robust photocatalytic hydrogen generation from CdSe nanoplatelets

Mercaptopropionic acid (MPA) capped-CdSe NPLs in-conjunction with a nickel-dihydrolipoic acid (Ni-DHLA) co-catalyst, yielded a robust output of photocatalytically generated hydrogen (H 2 ) at low concentrations of nanoplatelets. Furthermore, etching led to increased catalytic efficiency, suggesting that exposed catalytically active sites are primarily responsible for photocatalytic activity.

Phinney, Elizabeth O. [University of Rochester, NY↗
DOE OSTI2024

Robust giant anomalous Nernst effect in polycrystalline nodal web ferromagnets

The transverse thermoelectric effect based on the anomalous Nernst effect (ANE) has attracted attention, especially for thermoelectric and spintronic applications. Fe3X (X = Ga, Al) is known to exhibit a large ANE at room temperature owing to the topological electronic band structure so-called nodal web. Here, we systematically investigate ANE in the polycrystalline Fe3Ga1−xAlx and Fe3Al1−xSix. Despite significant chemical substitutions, a very robust feature of the large ANE −Syx∼ 5.7 μ V/K is found for Fe3Ga1−xAlx (0≲x≲0.6), exhibiting a striking x independent nodal web contribution. Since aluminum is more low-cost and abundant than gallium, our results indicate Fe3Ga1−xAlx (x∼0.6) should be suitable for large-scale thermoelectric applications.

Physics↗
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 OSTI2025

Robust quantification of the diamond nitrogen-vacancy center charge state via photoluminescence spectroscopy

Nitrogen vacancy (NV) centers in diamond are at the heart of many emerging quantum technologies, all of which require control over the NV charge state. Hence, methods for quantification of the relative photoluminescence intensities of the NV 0 and NV − charge states, i.e., a charge state ratio, are vital. Several approaches to quantify NV charge state ratios have been reported but are either limited to bulk-like NV diamond samples or yield qualitative results. We propose an NV charge state quantification protocol based on the determination of sample- and experimental setup-specific NV 0 and NV − reference spectra. The approach employs blue (400–470 nm) and green (480–570 nm) excitation to infer pure NV 0 and NV − spectra, which are then used to quantify NV charge state ratios in subsequent experiments via least squares fitting. We test our dual excitation protocol (DEP) for a bulk diamond NV sample and 20 and 100 nm nanodiamond particles and compare results with those obtained via other commonly used techniques such as zero-phonon line fitting and non-negative matrix factorization. We find that DEP can be employed across different samples and experimental setups and yields consistent and quantitative results for NV charge state ratios that are in agreement with our understanding of NV photophysics. By providing robust NV charge state quantification across sample types and measurement platforms, DEP will support the development of NV-based quantum technologies.

Color center laser spectroscopy↗
DOE OSTI2025

X-ray thermal diffuse scattering as a texture-robust temperature diagnostic for dynamically compressed solids

We present a model of x-ray thermal diffuse scattering (TDS) from a cubic polycrystal with an arbitrary crystallographic texture, based on the classic approach of Warren [B. E. Warren, Acta Crystallogr. 6, 803 (1953)]. We compare the predictions of our model with femtosecond x-ray diffraction patterns gathered from ambient and dynamically compressed rolled copper foils obtained at the High Energy Density instrument of the European X-Ray Free-Electron Laser facility and find that the texture-aware TDS model yields more accurate results than does the conventional powder model owed to Warren. Nevertheless, we further show: with sufficient angular detector coverage, the TDS signal is largely unchanged by sample orientation and in all cases strongly resembles the signal from a perfectly random powder; shot-to-shot fluctuations in the TDS signal resulting from grain-sampling statistics are at the percent level, in stark contrast to the fluctuations in the Bragg-peak intensities (which are over an order of magnitude greater); and TDS is largely unchanged even following texture evolution caused by compression-induced plastic deformation. We conclude that TDS is robust against texture variation, making it a flexible temperature diagnostic applicable just as well to off-the-shelf commercial foils as to ideal powders.

Crystal lattices↗
DOE OSTI2026

INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations

When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Comparative Evaluation of Spectral Methods for Robust Reactor Noise Estimation

Reactor noise analysis provides a noninvasive means to determine neutron kinetic parameters from stochastic fluctuations in detector signals. However, standard cross-power spectral density (CPSD) analyses can be sensitive to numerical processing choices, which may introduce processing-dependent systematic shifts in estimates of the prompt neutron decay constant (α) and limit reproducibility. This study uses a hybrid multitaper–Welch spectral estimator to analyze subcritical noise measurements from a fast-spectrum critical assembly. The decay constant α was extracted using three frequency-domain methods: the CPSD, the magnitude-squared coherence (MSC), and the generalized magnitude-squared coherence (GMSC). These coherence-based estimators normalize detector auto-spectral structure and are expected to reduce the sensitivity of fitted α values to processing parameters. A Sobol global sensitivity analysis identified which numerical inputs most strongly influence the fitted values of α. All estimators produced a linear dependence of α on inverse count rate, with delayed-critical extrapolations near 1.7 × 10 4 s −1 , in agreement within 8% of MCNP6.3 KOPTS benchmark calculations. Sensitivity results show that while the CPSD depends on both time-bin width and taper selection, the MSC and GMSC are dominated by time-bin width alone, indicating reduced parameter coupling and greater robustness to processing variability. These findings demonstrate the feasibility and practical value of coherence-based spectral estimators for extracting α from reactor noise and support their broader application to multi-detector and irregular datasets in subcritical system characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2026

Existence of a robust optimal control process for efficient measurements in a two-qubit system

The verification of quantum entanglement is essential for quality control in quantum communication. In this work we propose an efficient protocol to directly verify the two-qubit entanglement of a known target state through a single-expectation-value measurement. Our method provides exact entanglement quantification using the concurrence measure without performing quantum state tomography. We prove the existence of a unitary transformation that drives a known initial state of a two-qubit system to a designated final state, where the trace over a chosen observable directly yields the concurrence of the initial state. Furthermore, we implement an optimal control process of that transformation and demonstrate its effectiveness through numerical simulations. We also show that this process is robust to environmental noise. Our approach offers advantages in directly verifying entanglement with low circuit depth, making it suitable for industrial-scale quality control of entanglement generation. Our results presented here provide mathematical justification for our earlier computational experiments.

Rodriguez, Ricardo [McPherson Coll.] (ORCID:000000↗
DOE OSTI2025

Robustness of Vacancy-Bound Non-Abelian Anyons in the Kitaev Model in a Magnetic Field

Non-Abelian anyons in quantum spin liquids (QSLs) provide a promising route to fault-tolerant topological quantum computation. In the exactly solvable Kitaev honeycomb model, such anyons of the QSL state can be bound to nonmagnetic spin vacancies and endowed with non-Abelian statistics by an infinitesimal magnetic field. Here, we investigate how this approach for stabilizing non-Abelian anyons extends to a finite magnetic field represented by a proper Zeeman term. Through large-scale density-matrix renormalization group simulations, we compute the vacancy-anyon binding energy as a function of magnetic field for both the ferromagnetic and antiferromagnetic Kitaev models. Here, we find that anyon binding remains robust within the entire QSL phase for the ferromagnetic Kitaev model but breaks down already inside this phase for the antiferromagnetic Kitaev model. To compute a binding energy several orders of magnitude below the magnetic energy scale, we introduce both a refined definition and an extrapolation scheme based on carefully tailored perturbations.

Xiao, Bo [Oak Ridge National Laboratory (ORNL), Oa↗
DOE OSTI2024

Crosstalk-robust quantum control in multimode bosonic systems

High-coherence superconducting cavities offer a hardware-efficient platform for quantum information processing. To achieve universal operations of these bosonic modes, the requisite nonlinearity is realized by coupling them to a transmon ancilla. However, this configuration is susceptible to crosstalk errors in the dispersive regime, where the ancilla frequency is Stark shifted by the state of each coupled bosonic mode. This leads to a frequency mismatch of the ancilla drive, lowering the gate fidelities. To mitigate such coherent errors, we employ quantum optimal control to engineer ancilla pulses that are robust to the frequency shifts. These optimized pulses are subsequently integrated into a recently developed echoed conditional displacement protocol for executing single- and two-mode operations. Through numerical simulations, we examine two representative scenarios: the preparation of single-mode Fock states in the presence of spectator modes and the generation of two-mode entangled Bell-cat states. Our approach markedly suppresses crosstalk errors, outperforming conventional ancilla control methods by orders of magnitude. These results provide guidance for experimentally achieving high-fidelity multimode operations and pave the way for developing high-performance bosonic quantum information processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Robustness of the Galactic Center excess morphology against masking

The Galactic Center excess (GCE) remains an enduring mystery, with leading explanations being annihilating dark matter or an unresolved population of millisecond pulsars. Analyzing the morphology of the GCE provides critical clues to identify its exact origin. We investigate the robustness of the inferred GCE morphology against the effects of masking, an important step in the analysis where the gamma-ray emission from point sources and the galactic disk are excluded. Using different masks constructed from Fermi point source catalogs and a wavelet method, we find that the GCE morphology, particularly its ellipticity and cuspiness, is relatively independent of the choice of mask for energies above 2–3 GeV. The GCE morphology systematically favors an approximately spherical shape, as expected for dark matter annihilation. Compared to various stellar bulge profiles, a spherical dark matter annihilation profile better fits the data across different masks and galactic diffuse emission backgrounds, except for the stellar bulge profile which provides a similar fit to the data. Modeling the GCE with two components, one from dark matter annihilation and one tracing the Coleman bulge, we find this two-component model outperforms any single component or combinations of dark matter annihilation and other stellar bulge profiles. Uncertainty remains about the exact fraction contributed by each component across different background models and masks. Furthermore, when the Coleman bulge dominates, its corresponding spectrum lacks characteristics typically associated with millisecond pulsars, suggesting that it mostly models the emission from other sources instead of the GCE that is still present and spherically symmetric.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Contrastive learning for robust representations of neutrino data

In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025

Wilkinson, Alex (ORCID:0000000253404506)↗
DOE OSTI2024

Robust Quantum Control via Multipath Interference for Thousandfold Phase Amplification in a Resonant Atom Interferometer

We introduce a novel technique for enhancing the robustness of light-pulse atom interferometers against the pulse infidelities that typically limit their sensitivities. The technique uses quantum optimal control to favorably harness the multipath interference of the stray trajectories produced by imperfect atom-optics operations. We apply this method to a resonant atom interferometer and achieve thousandfold phase amplification, representing a 50-fold improvement over the performance observed without optimized control. Moreover, we find that spurious interference can arise from the interplay of spontaneous emission and many-pulse sequences and demonstrate optimization strategies to mitigate this effect. Given the ubiquity of spontaneous emission in quantum systems, these results may be valuable for improving the performance of a diverse array of quantum sensors. We anticipate our findings will significantly benefit the performance of matter-wave interferometers for a variety of applications, including dark matter, dark energy, and gravitational wave detection.

47 OTHER INSTRUMENTATION↗