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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Volatilization of oxides during oxidation of some superalloys at 1200 C

Volatilization of oxides during cyclic oxidation of commercial Nichrome, Inconel 750, Rene 41, Stellite 6B, and GE-1541 was studied at 1200 C in static air. Quantitative analysis of oxide vapor deposits revealed that oxides of tungsten, molybdenum, niobium, manganese, and chromium volatilized preferentially from the oxide scales. Aluminum and silicon were not detected in vapor deposits. For all the alloys except GE-1541 chromium was found to be the main metallic element in the oxide scales.

Zaplatynsky, I.↗
NASA NTRS2025

Laminae as Structural Biosignatures in NASA’s Life Detection Knowledge Base

Laminae are millimeter-scale features in rocks created by physiochemical processes that can be influenced by the presence and activities of communities of organisms that occur as biofilms and microbial mats. The structure and composition of laminae reflect the processes involved in their formation and can be preserved in the rock record over geologic time, however diagenetic and metamorphic alteration can lead to the loss of primary information and confusion over the interpretation of their origins. As potential records of ancient life, laminae can preserve evidence of microbial activity over billions of years of Earth’s history. On planetary bodies beyond Earth, laminae in sedimentary rocks are the most common sedimentary structure and represent significant features of interest that can record habitable conditions (e.g., the presence of liquid water) at the time of their formation. Here we review the significance of laminae as targets for astrobiological exploration. We discuss common mechanisms by which laminae form in natural environments on Earth, present arguments and evidence for laminae as biosignatures, and describe how such information is incorporated into the NASA Life Detection Knowledge Base.

Astrobiology↗
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗
DOE OSTI2026

Study on Application of Distributed Network of Sensors with List Mode for NMAC Literature Review

Nuclear material accounting and control (NMAC) for nuclear security detects, deters, and resolves questions related to unauthorized removal (i.e. theft) or misuse of nuclear material. NMAC also serves as a key insider threat mitigation measure and aids in recovery of nuclear material that is missing. Effective nuclear security depends on NMAC for timely and accurate information about nuclear material types, quantities, and locations. Bulk nuclear material processing facilities, however, present unique challenges for effective NMAC due to the presence of large quantities of material in-process and the accumulation of residual material holdup within process equipment. These holdup accumulations can obscure accurate physical inventory taking and complicate efforts to resolve NMAC irregularities at the facility level. Bulk material monitoring systems often rely on material balance calculations and indirect measurement techniques, which may mask protracted theft of smaller amounts of nuclear material. These monitoring limitations have generated increased interest in continuous monitoring technologies, including distributed non-destructive assay (NDA) sensor networks capable of providing real-time or near-real-time measurement of material movement and accumulation within bulk processing environments. Recent advancements in distributed networks of NDA radiation detectors and sensing technologies provide an opportunity to address these limitations. Although such distributed sensor networks have been implemented in select facilities for IAEA Safeguards applications, their potential for supporting NMAC functions specifically tailored to nuclear security objectives remains largely unexplored. Furthermore, emerging list-mode data acquisition technologies have reached high technology readiness levels, enabling time-correlated detection of nuclear events across multiple temporal scales. These capabilities provide enhanced opportunities for accurate holdup measurement, continuous process monitoring, and improved detection of material theft or misuse over time. The increasing global expansion of civil nuclear power and development of related bulk material processing facilities, including those supporting high-assay low-enriched uranium (HALEU) and other advanced reactor fuel fabrication, further increases the need for advanced measurement and monitoring strategies for NMAC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2017

Meteorological Inventory of Rain-On-Snow Events in the Canadian Arctic Archipelago and Satellite Detection Assessment Using Passive Microwave Data

The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.

Canadian Arctic Archipelago (CAA)↗
NASA NTRS1994

Coccolithophorid blooms in the global ocean

The global distribution pattern of coccolithophrid blooms was mapped in order to ascertain the prevalence of these blooms in the world's oceans and to estimate their worldwide production of CaCO3 and dimethyl sulfide (DMS). Mapping was accomplished by classifying pixels of 5-day global composites of coastal zone color scanner imagery into bloom and nonbloom classes using a supervised, multispectral classification scheme. Surface waters with the spectral signature of coccolithophorid blooms annually covered an average of 1.4 x 10(exp 6) sq km in the world oceans from 1979 to 1985, with the subpolar latitudes accounting for 71% of this surface area. Classified blooms were most extensive in the Subartic North Atlantic. Large expanses of the bloom signal were also detected in the North Pacific, on the Argentine shelf and slope, and in numerous lower latitude marginal seas and shelf regions. The greatest spatial extent of classified blooms in subpolar oceanic regions occurred in the months from summer to early autumn, while those in lower latitude marginal seas occurred in midwinter to early spring. Though the classification scheme was effcient in separating bloom and nonbloom classes during test simulations, and biogeographical literature generally confirms the resulting distribution pattern of blooms in the subpolar regions, the cause of the bloom signal is equivocal in some geographic areas, particularly on shelf regions at lower latitudes. Standing stock estimates suggest that the presumed Emiliania huxleyi blooms act as a significant source of calcite carbon and DMS sulfur on a regional scale. On a global scale, however, the satellite-detected coccolithophorid blooms are estimated to play only a minor role in the annual production of these two compounds and their flux from the surface mixed layer.

Brown, Christopher W.↗
NASA NTRS1999

Relativistic Electrons Observed at UARS and the Interpretation of their Storm-Associated Intensity Variations

The High Energy Particle Spectrometer (HEPS) instrument on the Upper Atmosphere Research Satellite (UARS) provides a database of electron intensities well resolved in energy and pitch-angle. Because of its 57 deg. orbital inclination, UARS encounters with magnetic shells L greater than 2 occur quite far off-equator (B/B (sub 0) greater than 9), corresponding to equatorial pitch angle alpha (sub 0) greater than 20 deg. Data acquired by HEPS (October 1991 through September 1994) span the declining phase of Solar Cycle 22. To reveal the storm-associated time dependence of relativistic electron intensities over the wide range of energies (50 keV to 5 MeV) covered by HEPS, we divide the daily average of the measured spectrum at a given L value (bin width = 0.25) by the corresponding 500-day average and plot the results with a color scale that spans only 2.5 decades. The data show that our off-equatorial electron intensities typically increase with time after the end of recovery phase (not during main phase or recovery phase) of each geomagnetic storm. The delay in off-equatorial energetic electron response and the subsequent lifetime of the corresponding electron flux enhancement seem to increase with particle energy above 300 keV. The trend below 300 keV seems to be opposite, such that the delay varies inversely with electron energy. Our working hypothesis for interpretation is that stormtime radial transport tends to increase the phase-space densities of trapped relativistic electrons but typically leads to a flux increases at specified energies only as the current (as indicated by Dst) decays. Flux enhancements in early recovery phase are greatest for equatorially mirroring electrons, and to pitch-angle anisotropies are initially large. Subsequent pitch-angle diffusion broadens the flux enhancement to particles that mirror off equator, thus gradually increasing low-altitude electron intensities (as detected by HEPS/UARS) on time scales equal to about 20% of corresponding lifetimes against diffusion into the loss cone. Alternative interpretations will also be examined.

Pesnell, W. D.↗
NASA NTRS2016

Stellar Echo Imaging of Exoplanets

All stars exhibit intensity fluctuations over several timescales, from nanoseconds to years. These intensity fluctuations echo off bodies and structures in the star system. We posit that it is possible to take advantage of these echoes to detect, and possibly image, Earth-scale exoplanets. Unlike direct imaging techniques, temporal measurements do not require fringe tracking, maintaining an optically-perfect baseline, or utilizing ultra-contrast coronagraphs. Unlike transit or radial velocity techniques, stellar echo detection is not constrained to any specific orbital inclination. Current results suggest that existing and emerging technology can already enable stellar echo techniques at flare stars, such as Proxima Centauri, including detection, spectroscopic interrogation, and possibly even continent-level imaging of exoplanets in a variety of orbits. Detection of Earth-like planets around Sun-like stars appears to be extremely challenging, but cannot be fully quantified without additional data on micro- and millisecond-scale intensity fluctuations of the Sun. We consider survey missions in the mold of Kepler and place preliminary constraints on the feasibility of producing 3D tomographic maps of other structures in star systems, such as accretion disks. In this report we discuss the theory, limitations, models, and future opportunities for stellar echo imaging.

stellar↗
DOE OSTI2026

Detection of Critical Surface Features in PTLs and GDLs for Improved Device Performance and Manufacturing Reliability

High points, or features that protrude above the surface of the material, on porous transport layers (PTLs) and gas diffusion layers (GDLs) can be critical features that may affect the manufacturing process and the performance of the device containing the feature. High points on PTLs and GDLs may stress the membrane of a polymer electrolyte membrane (PEM) during lamination and cell operation of a PEM electrolyzer or fuel cell. Additionally, high points on GDLs may impact the reliability of the manufacturing process. Thus, understanding these critical features and developing procedures to detect them are a key part of developing quality control techniques for PTLs and GDLs. This work evaluates the effectiveness of the Keyence VR6200 benchtop-scale structured light optical profilometer for detection of surface protrusions on PTLs and GDLs. Standard testing procedures for detecting and measuring high points were created for use on both material types. These procedures were evaluated using Gage Repeatability and Reproducibility (Gage R&R), where the repeatability, reproducibility, and effectiveness of the system to detect and measure high points were quantified. We have shown with high statistical power that the system is very effective in detection and measurement of high points, with Gage R&R contributions measured to be 2.2% and 3.6% for PTLs and GDLs, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-significance detection of correlation between the unresolved gamma-ray background and the large-scale cosmic structure

Our understanding of the γ-ray sky has improved dramatically in the past decade, however, the unresolved γ-ray background (UGRB) still has a potential wealth of information about the faintest γ-ray sources pervading the Universe. Statistical cross-correlations with tracers of cosmic structure can indirectly identify the populations that most characterize the γ-ray background. In this study, we analyze the angular correlation between the γ-ray background and the matter distribution in the Universe as traced by gravitational lensing, leveraging more than a decade of observations from the Fermi-Large Area Telescope (LAT) and 3 years of data from the Dark Energy Survey (DES). We detect a correlation at signal-to-noise ratio of 8.9. Most of the statistical significance comes from large scales, demonstrating, for the first time, that a substantial portion of the UGRB aligns with the mass clustering of the Universe as traced by weak lensing. Blazars provide a plausible explanation for this signal, especially if those contributing to the correlation reside in halos of large mass (∼ 10 14 M ⊙ ) and account for approximately 30–40% of the UGRB above 10 GeV. Additionally, we observe a preference for a curved γ-ray energy spectrum, with a log-parabolic shape being favored over a power-law. We also discuss the possibility of modifications to the blazar model and the inclusion of additional γ-ray sources, such as star-forming galaxies, misalinged active galactic nuclei, or particle dark matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Integrating N -glycan and CODEX imaging reveal cell-specific protein glycosylation in healthy human lung

Identifying cell-specific glycan structures in human lungs is critical for understanding the chemistry and mechanisms that guide cell–cell and cell–matrix interactions and determining nuanced functions of specific glycosylation. Our dual-modality omics platform, which uses matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to profile glycan chemistry at 50 μm × 50 μm scale, combined with co-detection by indexing (CODEX) to provide cell identification from the exact same tissue section, is a significant step in this direction. It enabled us to detect, differentiate, and reveal chemical properties of N-glycans in the various cell types of a human lung, suggesting the cell-specific function of distinct carbohydrate moieties. This innovative technological combination bridges the gap between the specific protein glycosylation and their cellular origin, paving the way for targeted studies in the lungs and many other human tissues where glycans mediate cell–cell recognition events.

Veličković, Dušan [Pacific Northwest National Labo↗
DOE OSTI2026

Combined tracer analysis for DESI 2024 BAO

This paper demonstrates how the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) and future baryon acoustic oscillations (BAO) analyses can optimally combine overlapping tracers (galaxies of distinct types) in the same redshift range. We make a unified catalog of Luminous Red Galaxies (LRGs) and Emission Line Galaxies (ELGs) in the redshift range 0.8 < z < 1.1 and investigate the impact on the BAO constraints. DESI DR1 contains ∼ 30% of the final DESI LRG sample and less than 25% of the final ELG sample, and the combination of LRGs and ELGs increases the number density and reduces the shot noise. We developed a pipeline to merge the overlapping tracers using galaxy bias as an approximately optimal weight and tested the pipeline on a suite of Abacus simulations, calibrated on the final version of the DESI Early Data Release. When applying our pipeline to the DESI DR1 catalog, we find an improvement in the BAO constraints of 11% for α iso and ∼ 7.0% for α AP consistent with our findings in mock catalogs. Our analysis was integrated into the DESI DR1 BAO analysis to produce theLRG+ELG result in the 0.8 < z < 1.1 redshift bin, which provided the most precise BAO measurement from DESI DR1 with a 0.86% constraint on the BAO distance scale and a 9.1σ detection of the isotropic BAO feature.

baryon acoustic oscillations↗
DOE OSTI2025

Measuring and unbiasing the BAO shift in the Ly α forest with AbacusSummit

ABSTRACT The Dark Energy Spectroscopic Instrument (DESI) places sub- per cent constraints on measurements of the Baryon Acoustic Oscillation (BAO) scaling parameters from the Ly $\alpha$ forest. However, no systematic error budget stemming from non-linearities in the three-dimensional clustering of the Ly $\alpha$ forest is included in the DESI-Ly $\alpha$ analysis. In this work, we measure the size of the shift of the BAO peak using large Ly $\alpha$ forest mocks produced on the N-body simulation suite AbacusSummit, which adopt the Fluctuating–Gunn–Peterson Approximation (FGPA). Specifically, we measure the Ly $\alpha$ autocorrelation and the Ly $\alpha$-quasar cross-correlation functions. To mitigate the noise, we adopt a linear control variates technique, reducing the error bars by a factor of up to $\sim \sqrt{50}$ on large scales. From the autocorrelation, we detect a small positive shift in radial direction of $\Delta \alpha _{\parallel }= 0.35~{{\ \rm per\ cent}}$ at the 3$\sigma$ level and virtually no shift in the transverse direction, $\alpha _\perp$. From the cross-correlation, we see a similar shift to $\Delta \alpha _\parallel$, albeit with larger error bars, and a small negative shift, $\Delta \alpha _{\perp }=\sim$0.25 per cent, at the 2$\sigma$ level. We also make a connection with the Ly $\alpha$ forest effective field theory (EFT) framework and find that the one-loop EFT power spectrum yields unbiased measurements of the BAO shift parameters in radial and transverse direction for Ly $\alpha$ auto- and the Ly $\alpha$-quasar cross-correlation measurements. When using the one-loop EFT framework, we find that we can recover the BAO parameters without a shift, which has important implications for future Ly $\alpha$ forest analyses based on EFT. This work paves the way for novel full-shape analyses of the currently observing DESI and future surveys such as the PFS, WEAVE-QSO, and 4MOST.

Hadzhiyska, Boryana↗
DOE OSTI2026

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2025

High-Throughput Characterization Tools/Algorithms To Outline Porosity Variability in AM Samples as a Function of Processing Conditions

This report documents the development and deployment of advanced algorithms and tools that enable high-throughput characterization for metal additive manufacturing (AM), with a particular focus on process parameter optimization and material/part qualification for nuclear applications. While the method ologies presented support diverse characterization techniques, the majority of the work is centered on AI-driven algorithms for X-ray computed tomography (XCT) to accelerate defect detection and materials analysis at scale.

36 MATERIALS SCIENCE↗
NASA NTRS1976

Selection of remote sensing techniques - Surface roughness information from 3 cm wavelength SLAR images

The value of SLAR (Side-Looking Airborne Radar) image data for detecting and measuring small-scale surface roughness is examined, using as an example the Cottonball Basin in Death Valley National Monument, California. The SLAR image was obtained by an X-band (3 cm wavelength) synthetic aperture radar system operated at an altitude of 10,732 m above sea level. The polarization of the transmitted and received signals was horizontal. Film density values were used to produce color hypsometric maps of small-scale surface irregularities. It is shown that semi-quantitative surface roughness information, from uniquely flat surfaces such as the Cottonball Basin, can be obtained by analysis of 3 cm wavelength SLAR images calibrated by limited field measurements. Quantitative roughness data could be obtained with proper consideration of modifying surface and radar system parameters.

Schaber, G. G.↗