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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tetranuclear Polypyridylruthenium(II) Complexes as Selective Nucleic Acid Stains for Flow Cytometric Analysis of Monocytic and Epithelial Lung Carcinoma Large Extracellular Vesicles

Selective staining of extracellular vesicles (EVs) is a major challenge for diagnostic and therapeutic applications. Herein, the EV labeling properties of a new class of tetranuclear polypyridylruthenium(II) complexes, Rubb7-TNL and Rubb7-TL, as phosphorescent stains are described. These new stains have many advantages over standard stains to detect and characterize EVs, including: high specificity for EV staining versus cell staining; high phosphorescence yields; photostability; and a lack of leaching from EVs until incorporation with target cells. As an example of their utility, large EVs released from control (basal) or lipopolysaccharide (LPS)-stimulated THP-1 monocytic leukemia cells were studied as a model of immune system EVs released during bacterial infection. Key findings from EV staining combined with flow cytometry were as follows: (i) LPS-stimulated THP-1 cells generated significantly larger and more numerous large EVs, as compared with those from unstimulated cells; (ii) EVs retained native EV physical properties after staining; and (iii) the new stains selectively differentiated intact large EVs from artificial liposomes, which are models of cell membrane fragments or other lipid-containing debris, as well as distinguished two distinct subpopulations of monocytic EVs within the same experiment, as a result of biochemical differences between unstimulated and LPS-stimulated monocytes. Comparatively, the staining patterns of A549 epithelial lung carcinoma-derived EVs closely resembled those of THP-1 cell line-derived EVs, which highlighted similarities in their selective staining despite their distinct cellular origins. This is consistent with the hypothesis that these new phosphorescent stains target RNA within the EVs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Measurement and Modeling of Polarized Atmosphere at the South Pole with SPT-3G

We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The measured spatial scaling, frequency scaling, and elevation dependence of the polarized emission are explained by this model. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 yr of Austral winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. We present a mitigation strategy that involves both downweighting significantly contaminated observations and subtracting a polarized atmospheric signal from the 150 GHz band maps. In observations with the SPT-3G instrument, the polarized atmospheric signal is a well-understood and subdominant contribution to the measured noise after implementing the mitigation strategies described here.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Neutron detection efficiency measurements of organic glass scintillator arrays

We characterize the neutron detection efficiency of pixelated organic glass scintillator (OGS) arrays manufactured in two ways: an additive method in which the OGS is cast in place in a pixelated mold, and a subtractive method in which the scintillator is first cast as a large block before being cut in bars and bonded together to form the array. The cast-in-place array utilizes a mixture of “bis(9,9-dimethyl-9H-fluoren-2-yl)diphenylsilane” (P2), “tri([1,1'-biphenyl]-4-yl)(phenyl)silane” (PB3), and “4-bis(2-methylstyryl)benzene” (MSB), and 50% by weight of polystyrene is added to the mixture for subtractive manufactured array. We compare the performance of the OGS arrays to that of an EJ-299-33M plastic scintillator array that was manufactured using a subtractive process similar to that of the second OGS array.

Neutron detectors (cold, thermal, fast neutrons)↗
DOE OSTI2026

DELVE Milky Way Satellite Galaxy Census. I. Satellite Population and Survey Selection Function in DES, DELVE, and Pan-STARRS

The properties of Milky Way satellite galaxies have important implications for galaxy formation, reionization, and the fundamental physics of dark matter. However, the population of Milky Way satellites includes the faintest known galaxies, and current observations are incomplete. To understand the impact of observational selection effects on the known satellite population, we perform rigorous, quantitative estimates of the Milky Way satellite galaxy detection efficiency in three wide-field survey datasets: the Dark Energy Survey Year 6, the DECam Local Volume Exploration Data Release 3, and the Pan-STARRS1 Data Release 1. Together, these surveys cover ∼13,600 deg 2 to g ∼ 24.0 and ∼27,700 deg 2 to g ∼ 22.5, spanning ∼91% of the high-Galactic-latitude sky (∣b∣ ≥ 15°). We apply multiple detection algorithms over the combined footprint and recover 49 known satellites above a strict census detection threshold. To characterize the sensitivity of our census, we run our detection algorithms on a large set of simulated galaxies injected into the survey data, which allows us to develop models that predict the detectability of satellites as a function of their properties. We then fit an empirical model to our data and infer the luminosity function, radial distribution, and size–luminosity relation of Milky Way satellite galaxies. Our empirical model predicts a total of $265^{+79}_{-47}$ satellite galaxies with −20 ≤ M V ≤ 0, half-light radii of 15 ≤ r 1/2 , (pc) ≤ 3000, and galactocentric distances of 10 ≤ D GC (kpc) ≤ 300. We also identify a mild anisotropy in the angular distribution of the observed galaxies, at a significance of ∼2σ, which can be attributed to the clustering of satellites associated with the LMC.

Tan, Chin Yi [Univ. of Chicago, IL (United States)↗
DOE OSTI2024

Systems and methods for resolving a number of incident RF-range photons

A photon-number-resolving detector comprises a detection element, an ohmmeter, and a hardware logic component. The detection element can be formed from a Weyl or Dirac semimetal. Electrons of the detection element are characterized by a surface state that exhibits a Dirac cone and a bulk superconducting state that exhibits a bandgap. When photons having energies less than the bandgap of the bulk superconducting state impinges on the detection element, the photons can be absorbed by electrons of the detection element that are characterized by the surface state. The ohmmeter outputs resistance data indicative of an electrical resistance of the detection element while the photons impinge on the detection element. The hardware logic component can determine, based upon the resistance data, a number of the photons that are absorbed by the surface state electrons of the detection element.

Soh, Daniel Beom Soo↗
DOE OSTI2025

Characterization of Infrasonic Signatures of Earth-Grazing Fireballs as Analogues to Hypersonic Vehicles (Final Report)

Accurate detection, discrimination, and characterization of high-altitude hypersonic events using infrasonic monitoring are critical to planetary defense and global strategic surveillance. This report synthesizes recent advances achieved through rigorous analysis of infrasonic signatures from natural meteoroids, emphasizing shallow entry-angle meteoroids as essentially proxies for artificial hypersonic systems. Meteoroids naturally encompass diverse velocities, trajectories, altitudes, and fragmentation behaviors, enabling systematic validation of empirical period–yield relationships, waveform morphology classifiers, and trajectory-induced back-azimuth deviation models. Integration of adaptive array-processing enhancements within Cardinal software further extends infrasonic detection sensitivity and signal classification reliability. Collectively these advances, based solely on infrasonic signatures or limited optical data, offer robust methodologies for distinguishing natural from artificial hypersonic sources, significantly reducing event geolocation uncertainties and refining source-function determination. The outcomes detailed herein lay foundational groundwork for improved global hypersonic event-surveillance frameworks, supporting improved security preparedness and informing strategic monitoring and defense policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Singlet-doublet dark matter revisited

The singlet-doublet model is an economical model of weakly interacting dark matter. We revisit it in light of improved dark matter direct detection limits. We characterize the now well-defined regions of remaining parameter space with suppressed direct detection cross sections and discuss features of the spectrum accessible at the Large Hadron Collider. We discuss when and how parameters in these special regions might be realized as the result of renormalization group evolution when starting with generic ultraviolet initial conditions.

Particle dark matter↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Nuclear Quadrupole Resonance for Substance Detection

This review paper provides a comprehensive overview of recent advances in nuclear quadrupole resonance (NQR) spectroscopy for substance detection, highlighting its principles, methodologies, and applications. The paper elucidates the fundamental physics underlying NQR spectroscopy, emphasizing the interaction between nuclear quadrupole moments and electric field gradients. It explores the various experimental techniques and instrumentation developments that have enabled the sensitive detection and precise characterization of substances containing quadrupolar nuclei. A significant portion of the survey is dedicated to discussing the diverse applications of NQR spectroscopy, including the detection of explosives, drug pharmaceuticals, and material authentication. Furthermore, the survey examines the challenges and limitations associated with NQR spectroscopy, including issues related to signal-to-noise ratio (SNR), temperature dependency, and substance restrictions. Strategies to overcome these challenges are discussed, offering insights into the future directions of NQR spectroscopy research that includes artificial intelligence (AI), internet of things (IoT) integration, incorporating a cloud database for NQR parameter storage, and multi-modal analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Characterization of Scintillating Crystal for High Energy Radiation Detection and Applications

Cesium Hafnium Chloride (Cs2Hfl6) is a promising metal halide scintillating detector due to its excellent characteristics such as non-hygroscopicity, high light yield without doping. Scintillators plays an important role in nuclear security applications as detectors at border monitoring stations. We grew and characterized CHC crystals to be fabricated for gamma detection. Photoluminescence was performed using the Duetta Fluorescence and Absorbance Spectrometer. The energy response to Cs-137 and Co-60 reference sources was recorded for a representative sodium iodide (NaI) scintillating detector.

Reeder, Angel↗
DOE OSTI2025

STILGAR End-of-Project Report

The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.

58 GEOSCIENCES↗
DOE OSTI2025

Comparison of measurement techniques and sorption of radium-226 in low and high salinity aqueous samples

Human activities have the potential to redistribute radium (Ra) in the marine environment in a manner that may necessitate monitoring or management of subsequent human or environmental exposures. There is therefore a need to identify accurate and accessible techniques for Ra measurement in high salinity samples and to describe the distribution of Ra in estuarine and marine environments, but most efforts in these areas have focused on low salinity matrices. In addition, rapid and reliable measurements are crucial for time-sensitive samples such as short-lived isotopes or emergency situations. The objective of this study is to describe the limits of detection, cost, and relative ease for measurement of Ra in both low and high salinity aqueous samples via three analytical methods: liquid scintillation counting (LSC), high purity germanium (HPGe) gamma spectrometry, and inductively coupled plasma mass spectrometry (ICP-MS). To contextualize these measurements for real-world scenarios, the partitioning of 226 Ra to substrates relevant to the marine environment was also characterized. Although HPGe detection with solid phase extraction had the lowest limit of detection for low salinity samples (0.27 Bq L −1 ), poor 226 Ra recovery for high salinity samples and high materials costs make this method prohibitive for many users. Limits of detection for high salinity samples were lower for LSC (1.28 Bq L −1 ) than for ICP-MS without dilution (11.4 Bq L −1 ), but significant and unexpected degradation of the high salinity LSC standards was observed after six months. Furthermore, our preferred measurement method for high salinity Ra samples is ICP-MS with sample dilution as necessary to reduce matrix effects.

07 ISOTOPE AND RADIATION SOURCES↗
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 OSTI2025

Pyrolyzed Parylene-N for in Vivo Electrochemical Detection of Neurotransmitters

Carbon electrodes are typically used for in vivo dopamine detection, and new types of electrodes and customized fabrication methods will facilitate new applications. Parylene is an insulator that can be deposited in a thin layer on a substrate and then pyrolyzed to carbon to enable its use as an electrode. However, pyrolyzed parylene has not been used for the real-time detection of neurochemicals by fast-scan cyclic voltammetry. In this work, we deposited thin layers of parylene-N (PN) on metal wires and then pyrolyzed them to carbon with high temperatures in a rapid thermal processor (RTP). Different masses of PN, 1, 6, and 12 g, were deposited to vary the thickness. RTP-PN (6 g) produced a 194 nm layer carbon thickness and had optimal electrochemical stability. Pyrolyzed parylene-N modified electrodes (PPNMEs) were characterized for electrochemical detection of dopamine, serotonin, and adenosine. Background-normalized currents at PPNMEs were about 2 times larger than those of carbon-fiber microelectrodes (CFMEs). Rich defect sites and oxygen functional groups promoted the neurochemical adsorption of cationic neurotransmitters. PPNMEs resisted fouling from serotonin polymer formation. PPNMEs were used in vivo to detect stimulated dopamine release and monitor spontaneous adenosine release. Pyrolyzed parylene is a sensitive and fouling-resistant thin-film carbon electrode that could be used in the future for making customized electrodes and devices.

25 ENERGY STORAGE↗
DOE OSTI2025

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2025

Multiscale astrobiology with the Vera C. Rubin Observatory Legacy Survey of Space and Time

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) presents an unprecedented opportunity to advance the search for astrobiologically relevant data across diverse astronomical environments. Through its extensive imaging capabilities, LSST will enhance our inventory of Solar System objects and assess their potential astrobiological conditions. Beyond the Solar System, LSST will survey billions of stars, contributing to the detection of exoplanets, characterizing planetary atmospheres at a zeroth-order level, identifying transient phenomena, and exploring conditions within habitable zones. In this paper, we highlight LSST’s unique contribution to astrobiology, complementing other missions and expanding the search for life beyond Earth in the coming decades.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Atomically Dispersed Ni‐N‐C Catalysts for Electrochemical CO 2 Reduction

Abstract The atomic dispersion of nickel in Ni‐N‐C catalysts is key for the selective generation of carbon monoxide through the electrochemical carbon dioxide reduction reaction (CO 2 RR). Herein, the study reports a highly selective, atomically dispersed Ni 1.0% ‐N‐C catalyst with reduced Ni loading compared to previous reports. Extensive materials characterization fails to detect Ni crystalline phases, reveals the highest concentration of atomically dispersed Ni metal, and confirms the presence of the proposed Ni‐N x active site at this reduced loading. The catalyst shows excellent activity and selectivity toward CO generation, with a faradaic efficiency for CO generation (FE CO ) of 97% and partial current density for CO (j co ) of ‐9.0 mA cm −2 at ‐0.9 V in an electrochemical H‐type cell. CO 2 RR activity and selectivity are also studied by rotating disk electrode (RDE) measurements where transport limitations can be suppressed. It is expected that the utility of these Ni‐N‐C catalysts will lie with tandem CO 2 RR reaction schemes to multi‐carbon (C 2+ ) products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗