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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Single Metal Atom Catalysts Prepared by Diluted Atomic Layer Deposition

The scalable and facile preparation of single-atom catalysts remains a critical challenge. Here, in this study, we introduce diluted atomic layer deposition (DALD), a unique approach for synthesizing supported metal catalysts with precisely tunable loadings. Unlike conventional metal deposition by ALD which uses pure metal precursors, DALD employs a diluted precursor mixture, combining organometallic precursors with the corresponding free ligand in controlled ratios. The method enables precise control over metal loadings, allowing the synthesis of structures ranging from nanoparticles to isolated single atoms, as exemplified by Ir, Rh, and Pt on high-surface-area γ-Al 2 O 3 . With its inherent simplicity and exceptional efficiency in metal precursor utilization, DALD represents a highly scalable strategy, unlocking opportunities for integrating single-atom catalysts into industrial processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Sensitivity of GPz Estimates of Photo-z Posterior PDFs to Realistically Complex Training Set Imperfections

The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires accurate and precise calculation of photometric redshifts; many diverse approaches to this problem are currently in the process of being developed, validated, and tested. In this work, we use the photometric redshift code GPz to examine two realistically complex training set imperfections scenarios for machine learning based photometric redshift calculation: (i) where the spectroscopic training set has a very different distribution in color–magnitude space to the test set, and (ii) where the effect of emission line confusion causes a fraction of the training spectroscopic sample to not have the true redshift. By evaluating the sensitivity of GPz to a range of increasingly severe imperfections, with a range of metrics (both of photo-z point estimates as well as posterior probability distribution functions, PDFs), we quantify the degree to which predictions get worse with higher degrees of degradation. In particular, we find that there is a substantial drop-off in photo-z quality when line-confusion goes above ~1%, and sample incompleteness below a redshift of 1.5, for an experimental setup using data from the Buzzard Flock synthetic sky catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Localization of infrasonic sources via Bayesian back projection

SUMMARY A Bayesian framework is investigated for event-specific localization of infrasonic sources using back projection ray tracing. Direction-of-arrival information from array-based detection analysis is used to initialize a back projection ray path originating from the detecting array location and quantifying propagation characteristics from hypothetical source locations. The Fisher statistic, computed from the array’s beam coherence, is mapped into uncertainty in the launch angles of the ray path. Auxiliary parameters previously introduced for solving the Transport equation to compute geometric spreading along ray paths are used to map uncertainty in the ray launch angles into spatial and temporal uncertainties in the ray path. An atmospheric ensemble approach is applied to account for atmospheric uncertainty, and the relation between uncertainties in the atmospheric state and confidence in estimated localization are evaluated using several ensembles with specified variances. The method is evaluated using a synthetic event in the western United States constructed via forward propagation simulations as well as a single-station, multi-arrival detection from a surface explosion in the western United States. Localization results using this event-specific approach are more accurate and exhibit improved precision than existing Bayesian localization methods that leverage generalized, pre-computed propagation statistics.

58 GEOSCIENCES↗
DOE OSTI2022

Generalized quasiharmonic approximation via space group irreducible derivatives

The quasiharmonic approximation (QHA) is the simplest nontrivial approximation for interacting phonons under constant pressure, bringing the effects of anharmonicity into temperature-dependent observables. Nonetheless, the QHA is often implemented with additional approximations due to the complexity of computing phonons under arbitrary strains, and the generalized QHA, which employs constant stress boundary conditions, has not been completely developed. In this work we formulate the generalized QHA, providing a practical algorithm for computing the strain state and other observables as a function of temperature and true stress. We circumvent the complexity of computing phonons under arbitrary strains by employing irreducible second-order displacement derivatives of the Born-Oppenheimer potential and their strain dependence, which are efficiently and precisely computed using the lone irreducible derivative approach. We formulate two complementary strain parametrizations: a discretized strain grid interpolation and a Taylor series expansion in symmetrized strain. We illustrate our approach by evaluating the temperature and pressure dependence of select elastic constants and the thermal expansion in thoria (ThO 2 ) using density functional theory with three exchange-correlation functionals. The QHA results are compared to our measurements of the elastic constant tensor using time-domain Brillouin scattering and inelastic neutron scattering. Our irreducible derivative approach simplifies the implementation of the generalized QHA, which will facilitate reproducible, data-driven applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Scaling approach to nuclear structure in high-energy heavy-ion collisions

In high-energy heavy-ion collisions, the initial condition of the produced quark-gluon plasma (QGP) and its evolution are sensitive to collective nuclear structure parameters describing the shape and radial profiles of the nuclei. Here, we find a general scaling relation between these parameters and many experimental observables such as elliptic flow, triangular flow, and particle multiplicity distribution. In particular, the ratios of observables between two isobar systems depend only on the differences of these parameters, but not on the details of the final state interactions, hence offering a new way to constrain the QGP initial condition. Using this scaling relation, we show how the structure parameters of $^{96}_{44}$Ru and $^{96}_{40}$Zr conspire to produce the rich centrality dependences of these ratios, as measured by the STAR Collaboration. Our scaling approach demonstrates that isobar collisions are a precision tool to probe the initial condition of heavy-ion collisions, as well as the collective nuclear structures, including the neutron skin, of the atomic nuclei across energy scales.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Lattice B -field correlators for heavy quarks

We analyze the color-magnetic (or “ B ”) field two-point function that encodes the finite-mass correction to the heavy-quark momentum-diffusion coefficient. The simulations are done on fine isotropic lattices in the quenched approximation at 1.5 T c , using a range of gradient flow times for noise suppression and operator renormalization. The continuum extrapolation is performed at fixed flow time followed by a second extrapolation to zero flow time. Perturbative calculations to next-to-leading order of this correlation function, matching gradient-flowed correlators to MS ¯ , are used to resolve nontrivial renormalization issues. We perform a spectral reconstruction based on perturbative model fits to estimate the coefficient κ B of the finite-mass correction to the heavy-quark momentum-diffusion coefficient. The approach we present here yields high-precision data for the correlator with all renormalization issues incorporated at next-to-leading order and is also applicable for actions with dynamical fermions. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem

In this paper, we present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2022

Transmission estimation at the quantum Cramér-Rao bound with macroscopic quantum light

The field of quantum metrology seeks to apply quantum techniques and/or resources to classical sensing approaches with the goal of enhancing the precision in the estimation of a parameter beyond what can be achieved with classical resources. Theoretically, the fundamental minimum uncertainty in the estimation of a parameter for a given probing state is bounded by the quantum Cramér-Rao bound. From a practical perspective, it is necessary to find physical measurements that can saturate this fundamental limit and to show experimentally that it is possible to perform measurements with the required precision to do so. Here we perform experiments that saturate the quantum Cramér-Rao bound for transmission estimation over a wide range of transmissions when probing the system under study with a continuous wave bright two-mode squeezed state. To properly take into account the imperfections in the generation of the quantum state, we extend our previous theoretical results to incorporate the measured properties of the generated quantum state. For our largest transmission level of 84%, we show a 62% reduction over the optimal classical protocol in the variance in transmission estimation when probing with a bright two-mode squeezed state with -8 dB of intensity-difference squeezing. Given that transmission estimation is an integral part of many sensing protocols, such as plasmonic sensing, spectroscopy, calibration of the quantum efficiency of detectors, etc., the results presented promise to have a significant impact on a number of applications in various fields of research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Modeling and Design of Micro Flux Monitors for Neutron Fluence Measurements

Measuring neutron flux is critical to understanding the characteristics of nuclear reactors and can provide information on the energy spectrum, total neutron fluence, and operational history of a facility. Such measurements are typically performed by irradiating high-purity materials in an operating reactor for a known length of time. By measuring and identifying activation products in the irradiated materials and referencing the energy-dependent cross section of each isotope, the neutron energy spectrum and total fluence can be determined. Historical techniques use a variety of small, individually pressed foils or wires for dosimeter devices, which can result in excessively high levels of radioactivity and complications in handling after irradiation. The present work describes a new neutron dosimetry concept, named the micro flux monitor, that uses semiconductor device fabrication processes to deposit thin films of flux monitoring materials in precise geometries onto a SiC wafer. This approach allows multiple dosimetry materials to be located on a single device, simplifying irradiation and subsequent analysis. The geometry, and therefore mass, of each material can be custom-tailored for a specific application—considering the expected neutron flux, duration of irradiation, and device retrieval time of a facility. Additionally, hundreds of identical dosimetry monitors can be produced simultaneously, reducing fabrication costs. Finally, the extremely thin metal dimensions of these devices could improve the fidelity of neutron fluence measurements by reducing neutron self-shielding and gamma self-attenuation effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Brochure on the 2024 ASCR Workshop on Analog Computing for Science

Analog computing fundamentally differs from digital by representing data with fully continuous physical quantities, such as voltages, probabilities, chemical concentrations, or light intensities, rather than encoding values in discrete binary states. While digital computing has historically excelled in precision, scalability, and noise resistance, newer analog approaches are gaining interest for their potential to dramatically improve energy efficiency and processing speed. Analog systems can inherently solve mathematical problems through their physical behavior and offer distinct advantages in scenarios where continuous operations are more effective than Boolean logic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Jipole: A Differentiable ipole-based Code for Radiative Transfer in Curved Spacetimes

Recent imaging of supermassive black holes by the Event Horizon Telescope has relied on exhaustive parameter-space searches, matching observations to large, precomputed libraries of theoretical models. As observational data become increasingly precise, the limitations of this computationally expensive approach grow more acute, creating a pressing need for more efficient methods. In this work, we present Jipole, an automatically differentiable (AD), ipole-based code for radiative transfer in curved spacetimes, designed to compute image gradients with respect to underlying model parameters. These gradients quantify how parameter changes—such as the black hole’s spin or the observer’s inclination—affect the image, enabling more efficient parameter estimation and reducing the number of required images. We validate Jipole against ipole in two analytical tests and then compare pixelwise intensity derivatives from AD with those from finite-difference methods. We then demonstrate the utility of these gradients by performing parameter recovery for an analytical model in three increasingly complex cases for the injected image: ideal, blurred, and blurred with added noise. In most cases, high-accuracy fits are obtained in only a few optimization steps, failing only in cases with extremely low signal-to-noise ratios. These results highlight the potential of AD-based methods to accelerate robust, high-fidelity model-data comparisons in current and future black hole imaging efforts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1971

Advancements in medicine from aerospace research

NASA has taken the lead in implementing the concept of technology utilization, and the Technology Utilization Program is the first vital step in the goal of a technological society to insure maximum benefit from the costs of technology. Experience has shown that the active approach to technology transfer is unique and is well received in the medical profession when appropriate problems are tackled. The problem solving approach is a useful one at the precise time when medicine is recognizing the need for new technology.

Wooten, F. T.↗
NASA NTRS1971

Elasto-plastic analysis of a mode I edge crack with application to a surface notch.

A finite element analysis has been undertaken of the mechanical state of a plate containing a crack subjected to mode I loading. Specifically, a localized, well-defined surface depression (dimple) previously observed on the back surface of plate specimens immediately behind the plane of the crack was examined. The approach taken is concerned with defining more precisely the dimpling behavior as a function of relative flaw depth and applied stress level, as well as the development of the plastic zone at the crack tip.

Francis, P. H.↗