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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 OSTI2023

The UCLA Cosmochemistry Database

Abstract The UCLA Cosmochemistry Database was initiated as part of a data-rescue and -storage project aimed at archiving a variety of cosmochemical data acquired at University of California, Los Angeles (UCLA). The data collection includes elemental compositions of extraterrestrial materials analyzed by UCLA cosmochemists over the last five decades. The analytical techniques include atomic absorption spectrometry (AAS) and neutron activation analysis (NAA) at UCLA. The data collection is stored on the Astromaterials Data System (Astromat). We provide both interactive tables and downloadable datasheets for users to access all data. The UCLA Cosmochemistry Database archives cosmochemical data that are essential tools for increasing our understanding of the nature and origin of extraterrestrial materials. Future studies can reference the data collection in the examination, analysis, and classification of newly acquired extraterrestrial samples.

Science & Technology - Other Topics↗
DOE OSTI2026

Nanohybrid of Cu2O-Ti3C2Tx as a Silver-Free MXene Sorbent for Iodine Gas Capture from Nuclear Waste

The capture of volatile radioiodine from nuclear fuel reprocessing off-gas streams remains a critical challenge due to the high volatility, long half-life of 129I, and biological uptake of iodide from the environment. Although silver-based sorbents provide strong iodine chemisorption, their high cost and regulatory classification as mixed radioactive-hazardous waste motivate the development of alternative materials. Here, we report a silver-free Cu2O-Ti3C2Tx MXene hybrid for iodine gas capture at 150 °C. Structural and compositional analyses confirm the formation of Cu2O nanoparticles on Ti3C2Tx nanosheets and their subsequent conversion to thermodynamically stable CuI upon static iodine gas exposure, achieving an iodine mass loading of up to 1115 mg/g. These results demonstrate the potential of Cu2O-Ti3C2Tx MXene as a copper-based alternative to silver sorbents for elevated-temperature iodine gas capture.

iodine gas capture↗
NASA NTRS2026

Manufacturing Process Development of a Carbon Fiber Reinforced Polymer Composite Shaft for Electric Motors

Electric aircraft applications require electric motors with increased specific power and efficiency. Composite structural components in motors are a potential solution for reducing motor mass, reducing magnetic losses, and limiting undesired conduction paths for fault, electromagnetic interference, or common-mode currents. In this report, manufacturing trials for a high-speed carbon fiber reinforced polymer composite motor shaft are presented. Four prototype shafts were produced using a hybrid biaxial/triaxial fabric that was circumferentially wrapped onto an additively manufactured high-temperature washout mandrel. An additional traditional overbraid approach was also evaluated and shows promise for high-rate, high-performance parts using automated manufacturing. This paper discusses the shaft design, manufacturing methods explored, material selection, the manufacturing trials, and the lessons learned. The results of this manufacturing investigation show feasibility for manufacturing composite shafts for electric motors.

Electric moto shaft↗
DOE OSTI2025

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

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

Chemistry↗
NASA NTRS2005

Chapter 19A – Radar Cross Section

The material in this Section will provide the novice Flight Test Engineer (FTE) with a brief overview of the radar cross section (RCS) concept. Brief details of radar range descriptions, operations, target calibrations, and data reduction are included. It is noted that these range operations concentrate on the "static" test range rather than the much more sophisticated "dynamic" test range. The dynamic test range is what the FTE will probably work with. Radar reflectivity measurement has developed over the last two decades from a relatively simple endeavor involving the measurement of target RCS amplitude statistics to involving wide band, coherent systems that can measure high resolution images of targets as well as, in many cases, the polarization and phasing properties. This rapid growth in RCS technology has occurred because of the increased use of radar in today's commercial and military systems. In general, the goal for commercial systems is to enhance radar reflectivity, whereas the military goal is to reduce radar reflectivity. Also, classification and identification are important for military purposes because in adverse weather radar may be the only system that may be capable of separating enemy targets from friendly ones. This Section will discuss the fundamentals of RCS. Since the flight test techniques associated with RCS measurements are very similar to those of antenna pattern measurements, both will be presented at the same time in Section 19B Antenna Radiation Pattern Measurements.

Robert W Borek↗
DOE OSTI2024

Recognizing Blazars Using Radio Morphology from the VLA Sky Survey

Abstract Blazars are radio-loud active galactic nuclei whose jets have a very small angle to our line of sight. Observationally, the radio emissions are mostly compact or compact-core with a one-sided jet. With 2.″5 resolution at 3 GHz, the Very Large Array Sky Survey (VLASS) enables us to resolve the structure of some blazar candidates in the sky north of decl. −40°. We introduce an algorithm to classify radio sources as either blazar-like or non-blazar-like based on their morphology in the VLASS images. We apply our algorithm to three existing catalogs, including one of the known blazars (Roma-BzCAT) and two blazar candidates identified by Wide-field Infrared Survey Explorer colors and radio emission (WIBRaLS, KDEBLLACS). We show that in all three catalogs, there are objects with morphologies inconsistent with being blazars. Considering all the catalogs, more than 12% of the candidates are unlikely to be blazars, based on this analysis. Notably, we show that 3% of the Roma-BzCATconfirmedblazars could be a misclassification based on their VLASS morphology. The resulting table with all sources and their radio morphological classification is available online.

Astronomy & Astrophysics↗
NASA NTRS2009

Modular Battery Charge Controller

A new approach to masterless, distributed, digital-charge control for batteries requiring charge control has been developed and implemented. This approach is required in battery chemistries that need cell-level charge control for safety and is characterized by the use of one controller per cell, resulting in redundant sensors for critical components, such as voltage, temperature, and current. The charge controllers in a given battery interact in a masterless fashion for the purpose of cell balancing, charge control, and state-of-charge estimation. This makes the battery system invariably fault-tolerant. The solution to the single-fault failure, due to the use of a single charge controller (CC), was solved by implementing one CC per cell and linking them via an isolated communication bus [e.g., controller area network (CAN)] in a masterless fashion so that the failure of one or more CCs will not impact the remaining functional CCs. Each micro-controller-based CC digitizes the cell voltage (V(sub cell)), two cell temperatures, and the voltage across the switch (V); the latter variable is used in conjunction with V(sub cell) to estimate the bypass current for a given bypass resistor. Furthermore, CC1 digitizes the battery current (I1) and battery voltage (V(sub batt) and CC5 digitizes a second battery current (I2). As a result, redundant readings are taken for temperature, battery current, and battery voltage through the summation of the individual cell voltages given that each CC knows the voltage of the other cells. For the purpose of cell balancing, each CC periodically and independently transmits its cell voltage and stores the received cell voltage of the other cells in an array. The position in the array depends on the identifier (ID) of the transmitting CC. After eight cell voltage receptions, the array is checked to see if one or more cells did not transmit. If one or more transmissions are missing, the missing cell(s) is (are) eliminated from cell-balancing calculations. The cell-balancing algorithm is based on the error between the cell s voltage and the other cells and is categorized into four zones of operation. The algorithm is executed every second and, if cell balancing is activated, the error variable is set to a negative low value. The largest error between the cell and the other cells is found and the zone of operation determined. If the error is zero or negative, then the cell is at the lowest voltage and no balancing action is needed. If the error is less than a predetermined negative value, a Cell Bad Flag is set. If the error is positive, then cell balancing is needed, but a hysteretic zone is added to prevent the bypass circuit from triggering repeatedly near zero error. This approach keeps the cells within a predetermined voltage range.

Button, Robert↗
DOE OSTI2024

Phase stability in the Hf-N and Zr-N systems

Hf and Zr nitrides are promising compounds for many technologically important areas, including high-temperature structural applications, quantum computing, and solar and optical applications. Here, this article reports on a comprehensive first-principles statistical mechanics study of phase stability in the Hf-N and Zr-N binary systems. A high solubility of nitrogen in the hcp forms of Hf and Zr is predicted. The rocksalt forms of HfN and ZrN can also tolerate a high degree of off-stoichiometry through the introduction of nitrogen and metal vacancies. The Hf-N binary favors a family of stacking faulted parent crystal structures at intermediate nitrogen concentrations that host a unique form of short-range order among nitrogen interstitials and vacancies. These phases can accommodate some degree of configurational entropy and remain ordered to temperatures as high as 1200 K.

Monte Carlo methods↗
DOE OSTI2024

System for controller area network payload decoding

A system for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This system can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗
DOE OSTI2026

Unveiling field-transverse spin anisotropy in the spin liquid candidate α-RuCl3 via spin Hall magnetoresistance

α-RuCl3 has emerged as a possible candidate for a quantum spin liquid (QSL) that promises exotic quasiparticles relevant for fault-tolerant quantum computation. Here, we report spin-sensitive transport measurements using a proximal spin Hall metal, platinum (Pt), to probe magnetic moments in the insulator α-RuCl3. We observe spin Hall magnetoresistance (SMR), where both the transverse and longitudinal resistivities exhibit angular oscillations between the in-plane magnetic field and the current, driven by the interplay between the spin Hall effect in Pt and local magnetic moments in α-RuCl3. These oscillations occur from 1.5 to 18 T, covering the zigzag antiferromagnetic, putative QSL, and supposedly partially field-polarized phases. The phase of the SMR oscillations suggests that the local moments, whether static or fluctuating, develop spin anisotropy with a quantization axis in-plane and largely transverse to the magnetic field across all fields from 1.5 to 18 T. Temperature dependence indicates that the spin anisotropy operates at an energy scale similar to that of the reported QSL signatures in α-RuCl3.

Idzuchi, Hiroshi [Tohoku University, Japan]↗
DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Anisotropic multi- Q order in Co x TaS 2

The cobalt-intercalated transition metal dichalcogenide Co x TaS 2 hosts a rich landscape of magnetic phases that depend sensitively on x. While the stoichiometric compound with x = 1/3 exhibits a single magnetic transition, samples with x≤0.325 display two transitions with an anomalous Hall effect (AHE) emerging in the lower temperature phase. Here, we resolve the spin structure in each phase by employing a suite of magneto-optical probes that include the discovery of anomalous magneto-birefringence: a spontaneous time-reversal sensitive rotation of the principal optic axes. A symmetry-based analysis identifies the AHE-active phase as an anisotropic (2+1)Q state, in which magnetic modulation at one wavevector (Q) differs in symmetry from that at the remaining two. The (2+1)Q state naturally exhibits scalar spin chirality as a mechanism for the AHE and expands the classification of multi-Q magnetic phases.

Kruppe, Jonathon [University of California, Berkel↗
NASA NTRS2002

Hubble Space Telescope On-orbit NiH2 Battery Performance

This paper summarizes the Hubble Space Telescope (HST) nickel-hydrogen (NiH2) battery performance from launch to the present time. Over the life of HST vehicle configuration, charge system degradation and failures together with thermal design limitations have had a significant effect on the capacity of the HST batteries. Changes made to the charge system configuration in order to protect against power system failures and to maintain battery thermal stability resulted in undercharging of the batteries. This undercharging resulted in decreased usable battery capacity as well as battery cell voltage/capacity divergence. This cell divergence was made evident during on-orbit battery capacity measurements by a relatively shallow slope of the discharge curve following the discharge knee. Early efforts to improve the battery performance have been successful. On-orbit capacity measurement data indicates increases in the usable battery capacity of all six batteries as well as improvements in the battery cell voltage/capacity divergence. Additional measures have been implemented to improve battery performance, however, failures within the HST Power Control Unit (PCU) have prevented verification of battery status. As this PCU fault prevents the execution of on-orbit capacity testing, the HST Project has based the battery capacity on trends, which utilizes previous on-orbit battery capacity test data, for science mission and servicing mission planning. The Servicing Mission 38 (SM-3B) in March 2002 replaced the faulty PCU. Following the servicing mission, on-orbit capacity test resumed. A summary of battery performance is reviewed since launch in this paper.

Rao, Gopalakrishna M.↗
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↗
NASA NTRS2026

Long-Term Evaluation of Landsat 9 Oli Data Product Uniformity at Focal Plane Module-Boundaries Using Earth Scenes

The uniformity performance characteristic in remote sensing data products is a metric that helps evaluate how an image accurately represents radiometric responses to geophysical variability at the Earth’s surface or within the atmosphere. These uniformity attributes are used in Earth remote sensing science applications such as investigating water quality, land-cover/land-use classifications, and crop health assessments. To ensure these applications perform effectively, it is crucial to maintain and regularly assess the uniformity of calibrated remote sensing data products. In the Landsat missions, data from the onboard radiometric reference Solar diffuser device are used as the standard approach to derive the calibration parameters, ensuring the maintenance of both absolute radiance and radiometric uniformity through quarterly updates. This publication presents an alternative approach that leverages Earth scene data to assess the on-orbit uniformity performance of the Landsat 9 (L9) mission. The analysis characterizes uniformity at a specific set of spatial positions (the focal-plane boundary zones) that are intrinsic to the Operational Land Imager (OLI) focal plane design. By employing these Earth data statistics, the article further explores the temporal trends in the radiometric uniformity results. The results shown in this article demonstrate that for pushbroom imaging design systems with staggered sensor chip focal plane architectures, the co-registered pixels, i.e., overlapping measurements of the same ground target viewed by different detectors, offer a self-contained mechanism for assessing product uniformity. Remarkably, this alternative approach achieves a level of radiometric quality comparable to that delivered by a stable, well-characterized onboard Solar diffuser calibration device. The findings demonstrate the potential and capability of such Earth scene statistics in maintaining radiometric uniformity in calibrated data products to better than 1% (1-sigma) throughout the mission’s operational lifetime.

trend↗
DOE OSTI2026

Evaluation of high-temperature properties of binary, eutectic Ni-X alloys (X = B, Ca, Ce, La, Y, Zr)

We investigated the high-temperature behavior of binary eutectic nickel-base alloys with low-solubility elements, towards developing alloys with new combinations of structural and functional (e.g. thermal or corrosion) properties. Based on thermodynamic screening, we cast binary near-eutectic Ni-B, Ni-Ca, Ni-Ce, Ni-La, Ni-Y, and Ni-Zr alloys and characterized their microstructure and high-temperature properties. All alloys (except Ni-Ca) contain a fine eutectic microstructure, containing submicron alternating lamellae of γ-Ni and continuous intermetallic phases. The eutectic microstructure did not significantly coarsen at 700°C (except for Ni-B), but measurably coarsened in all alloys at 900°C, most quickly in Ni-B, followed by Ni-Ce, Ni-La, Ni-Y, and Ni-Zr. Coarsening in these alloys occurs by fault migration; diffusion within the intermetallic, and its interfacial energy, likely influence coarsening kinetics. At room-temperature, all alloys possess negligible ductility due to the continuous, brittle intermetallic. At 700 and 900°C, the Ni-Zr alloy showed the best combination of strength and ductility. Oxidation experiments at 900°C on Ni-Zr and Ni-Ce revealed heavy internal oxidation of the continuous intermetallic phase. Overall, the Ni-Zr alloy showed good high-temperature coarsening resistance and strength, though ductility and oxidation resistance require improvement for use in structural applications. Possible routes for improving alloy properties via additive manufacturing are discussed.

Ekaputra, Clement [ORNL] (ORCID:0000000307166479)↗
DOE OSTI2026

Maximizing dynamic range and performance of anatase TiO 2 ECRAM through structure and programming

Here, in this study, we investigate the structure-dependent modulation characteristics of all-solid-state three-terminal electrochemical random-access memory (ECRAM) based on an anatase Li x TiO 2 channel. By directly comparing “asymmetric” and “symmetric” ECRAM device architectures, we reveal significant insight into the impact of a non-zero gate-drain open-circuit voltage and its influence on voltage vs. current-controlled gating. We also explore the impact of potentiation/depression write parameters on the symmetry, linearity, and dynamic range of the device response. Together, initial results from optimizing structure and programming approaches yielded unprecedented G max /G min ratios of >1,000 for ECRAM and hundreds of tunable memory states with excellent linearity and symmetry. Simulations based on these ECRAM devices further illustrate the promise of this analog memory technology, achieving near 2% classification error in the MNIST digit recognition benchmark for a range of training parameters compared to a theoretical best of 1.66% and outperforming other device models extracted from the literature.

AIHWKit↗