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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

TOI-532b: The Habitable-zone Planet Finder confirms a Large Super Neptune in the Neptune Desert orbiting a metal-rich M-dwarf host

We confirm the planetary nature of TOI-532b, using a combination of precise near-infrared radial velocities with the Habitable-zone Planet Finder, Transiting Exoplanet Survey Satellite (TESS) light curves, ground-based photometric follow up, and high-contrast imaging. TOI-532 is a faint (J ~ 11.5) metal-rich M dwarf with T{sub eff} = 3957 ± 69 K and [Fe/H] = 0.38 ± 0.04; it hosts a transiting gaseous planet with a period of ~2.3 days. Joint fitting of the radial velocities with the TESS and ground-based transits reveal a planet with radius of 5.82 ± 0.19 R {sub ⊕}, and a mass of 61.5{sub -9.3}{sup +9.7} M {sub ⊕}. TOI-532b is the largest and most massive super Neptune detected around an M dwarf with both mass and radius measurements, and it bridges the gap between the Neptune-sized planets and the heavier Jovian planets known to orbit M dwarfs. It also follows the previously noted trend between gas giants and host-star metallicity for M-dwarf planets. In addition, it is situated at the edge of the Neptune desert in the Radius–Insolation plane, helping place constraints on the mechanisms responsible for sculpting this region of planetary parameter space.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗
DOE OSTI2025

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗
DOE OSTI2022

Upward propagation of gigantic jets revealed by 3D radio and optical mapping

Occasionally, lightning will exit the top of a thunderstorm and connect to the lower edge of space, forming a gigantic jet. Here, we report on observations of a negative gigantic jet that transferred an extraordinary amount of charge between the troposphere and ionosphere (~300 C). It occurred in unusual circumstances, emerging from an area of weak convection. As the discharge ascended from the cloud top, tens of very high frequency (VHF) radio sources were detected from 22 to 45 km altitude, while simultaneous optical emissions (777.4 nm OI emitted from lightning leaders) remained near cloud top (15 to 20 km altitude). This implies that the high-altitude VHF sources were produced by streamers and the streamer discharge activity can extend all the way from near cloud top to the ionosphere. The simultaneous three-dimensional radio and optical data indicate that VHF lightning networks detect emissions from streamer corona rather than the leader channel, which has broad implications to lightning physics beyond that of gigantic jets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GraphAlign: Graph-Enabled Machine Learning for Seismic Event Filtering

This report summarizes results from a 2 year effort to improve the current automated seismic event processing system by leveraging machine learning models that can operated over the inherent graph data structure of a seismic sensor network. Specifically, the GraphAlign project seeks to utilize prior information on which stations are more likely to detect signals originating from particular geographic regions to inform event filtering. To date, the GraphAlign team has developed a Graphical Neural Network (GNN) model to filter out false events generated by the Global Associator (GA) algorithm. The algorithm operates directly on waveform data that has been associated to an event by building a variable sized graph of station waveforms nodes with edge relations to an event location node. This builds off of previous work where random forest models were used to do the same task using hand crafted features. The GNN model performance was analyzed using an 8 week IMS/IDC dataset, and it was demonstrated that the GNN outperforms the random forest baseline. We provide additional error analysis of which events the GNN model performs well and poorly against concluded by future directions for improvements.

58 GEOSCIENCES↗
DOE OSTI2025

Surface Trap Dynamics and Gamma‐Ray Detection Enhancement in Passivated FAPbBr 3 Perovskite Crystals

Perovskite‐based direct radiation detectors offer a compelling platform for room‐temperature gamma spectroscopy due to their high sensitivity, tunable optoelectronic properties, and compatibility with solution processing. While prior studies on formamidinium lead tribromide (FAPbBr 3 ) have shown that surface passivation mitigates deep trap state impact on the charge collection efficiency (CCE), the influence of surface‐based shallow traps on time‐resolved detector response remains largely unexplored. In this article, a digital signal processing (DSP) method is applied to analyze waveform rising edge dynamics in FAPbBr 3 single‐crystal detectors, identifying prompt and delayed charge collection components. Using the second derivative of the collected charge signal, the onset of shallow trap reemission is isolated, occurring 1.1 µs after carrier generation. Surface passivation suppressed deep trap states, shifting carrier occupancy toward shallower traps and extending the temporal window of delayed charge collection. This delayed contribution correlates with measurable changes in the temporal and amplitude distributions of detector events. By leveraging time‐domain filtering informed by these dynamics, a selective enhancement in signal quality and gamma peak‐to‐Compton ratio (PCR) is achieved, while preserving over 90% of photoelectric events. An improvement in the PCR to 1.80, from the original 1.09, is enabled by focusing on high collection efficiency events corresponding to reemitted carriers in the 1.1 to 3 µs range after the generation event. These results demonstrate a pathway for leveraging shallow trap dynamics to enhance spectral fidelity in time‐resolved perovskite detectors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Flatfielding of hybrid pixel detectors in tender x-ray scattering

The ability of the soft matter interfaces beamline at National Synchrotron Light Source II to access x-ray energy in the tender x-ray regime, i.e., from 2.1 to 5 keV, enables new resonant x-ray scattering studies at the sulfur K-edge and others. We present a new approach to correct data acquired in the tender x-ray regime with a Pilatus3 detector in order to improve the data quality and to correct the various artifacts inherent to hybrid pixel detectors, such as variations in modules’ efficiency or noisy detector module junctions. Further, this new flatfielding significantly enhances the data quality and enables detection of weak scattering signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Characterization of Low-energy Ionization Signals in Silicon Detectors for the Nab Experiment

The Nab (Neutron a b) experiment is designed to measure the beta-antineutrino angular correlation in free neutron 𝛽 decay with an ultimate precision goal of 0.1%, providing input for tests of Cabibbo-Kobayashi-Maskawa matrix unitarity. This measurement is performed via detection of electrons and protons in delayed coincidence using custom large-area segmented silicon detectors. We present the characterization of one such detector system to establish the proton energy and timing response, using a dedicated proton accelerator. The detected proton peak was studied for 25, 30, and 35 keV incident protons on a set of detector segments and multiple cooling cycles over a one-year period. Ionization losses were consistent with models of the detector dead layer with thicknesses less than 100 nm. The detected proton peak was stable within the uncertainty from energy calibration (0.25 keV). The rise times of detector pulses from 109Cd and 113Sn conversion electron sources were used to extract the impurity density profile and establish a precise model for the detector timing response. The observed impurity density profile varied from (2±2)×109/cm3 at the center to (26±2)×109/cm3 at the edge. This impurity density profile was then used to characterize systematic effects in proton time-of-flight measurements due to detector pulse-shape effects; the resultant proton timing systematic uncertainties were below 0.3 ns, which is sufficient for the Nab experiment.

Taylor, RJ [North Carolina State University]↗
DOE OSTI2020

Evolution of nontrivial Fermi surface features in the band structures of the homologous members Pb 5 Bi 6 Se 14 and Pb 5 Bi 12 Se 23

High-quality single crystals of (PbSe) 5 (Bi 2 Se 3 ) 3m were grown and analyzed by nuclear magnetic resonance (NMR) spectroscopy. We report on 77 Se and 207 Pb NMR shifts and nuclear spin-lattice relaxation measurements in the naturally formed heterostructure homology (PbSe) 5 (Bi 2 Se 3 ) 3m with m = 1 (Pb 5 Bi 6 Se 14 ) and m = 2 (Pb 5 Bi 12 Se 23 ). A distinct site-specific contribution has been detected for both nuclei as a function of temperature, which reveals an electronic changeover from a semiconducting (Pb 5 Bi 6 Se 14 ) to a semimetalliclike (Pb 5 Bi 12 Se 23 ) system with nontrivial band structure features near the Fermi level. The temperature dependences of the relaxation rates are dominated by significant changes in the topology of energy dispersions accompanied with band edges and crossings in the region of the Fermi surface. These results, which interrogate nuclear spin interactions from selected atomic sites, clearly expose the effects of the added Bi 2 Se 3 layer on the crystal and electronic structure of (Pb 5 Bi 12 Se 23 ). These findings provide direct microscopic insight into the unconventional and dual nature of the electronic structure of these homologous thermoelectric and topologically nontrivial compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Redox Mechanism in Na-Ion Battery Cathodes Probed by Advanced Soft X-Ray Spectroscopy

A Na-ion battery (NIB) device is a promising solution for mid-/large-scale energy storage, with the advantages of material abundance, low cost, and environmental benignity. To improve the NIB capacity and retainability, extensive efforts have been put into the developments of NIB electrode materials. The redox activities of the transition metal (TM)-based NIB electrodes are critical in defining the capacity and stability. Here, we provide a comprehensive review on recent studies of the redox mechanisms of NIB cathodes through synchrotron-based soft X-ray absorption spectroscopy (sXAS) and mapping of resonant inelastic X-ray scattering (mRIXS). These soft X-ray techniques are direct and effective tools to fingerprint the TM-3d and O-p states with both bulk and surface sensitivities. Particularly, 3d TM L-edge sXAS has been used to quantify the cationic redox contributions to the electrochemical property; however, it suffers from lineshape distortion for the bulk sensitive signals in some scenarios. With the new dimension of information along the emitted photon energy, mRIXS can address the distortion issue of in TM-L sXAS; moreover, it also breaks through the limitation of conventional sXAS on detecting unconventional TM and O states, e.g., Mn(I) in NIB anode and oxidized oxygen in NIB cathodes. The mRIXS fingerprint of the oxidized oxygen state enables the detection of the reversibility of the oxygen redox reaction through the evolution of feature intensity upon electrochemical cycling and thus clarifies various misunderstandings in our conventional wisdom. We conclude that, with mRIXS established as a powerful tool, its potential and power will continue to be explored for characterizing novel chemical states in NIB electrodes.

25 ENERGY STORAGE↗
DOE OSTI2024

``Beads on a String" Star Formation Tied to one of the most Powerful AGN Outbursts Observed in a Cool Core Galaxy Cluster

With two central galaxies engaged in a major merger and a remarkable chain of 19 young stellar superclusters wound around them in projection, the galaxy cluster SDSS J1531+3414 ($z=0.335$) offers an excellent laboratory to study the interplay between mergers, AGN feedback, and star formation. New Chandra X-ray imaging reveals rapidly cooling hot ($T\sim 10^6$ K) intracluster gas, with two "wings" forming a concave density discontinuity near the edge of the cool core. LOFAR $144$ MHz observations uncover diffuse radio emission strikingly aligned with the "wings," suggesting that the "wings" are actually the opening to a giant X-ray supercavity. The steep radio emission is likely an ancient relic of one of the most energetic AGN outbursts observed, with $4pV > 10^{61}$ erg. To the north of the supercavity, GMOS detects warm ($T\sim 10^4$ K) ionized gas that enshrouds the stellar superclusters but is redshifted up to $+ 800$ km s$^{-1}$ with respect to the southern central galaxy. ALMA detects a similarly redshifted $\sim 10^{10}$ M$_\odot$ reservoir of cold ($T\sim 10^2$ K) molecular gas, but it is offset from the young stars by $\sim 1{-}3$ kpc. We propose that the multiphase gas originated from low-entropy gas entrained by the X-ray supercavity, attribute the offset between the young stars and the molecular gas to turbulent intracluster gas motions, and suggest that tidal interactions stimulated the "beads on a string" star formation morphology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Cellulose Nanocrystals’ Role in Critical Mineral Beneficiation: Dual Aggregate-Dispersant Behavior Supports Environmentally Benign Nickel Processing

Environmentally unsustainable and toxic chemical flocculants and dispersants from nickel (Ni) processing contribute to industrial effluents that greatly impact biodiversity and aquatic life. Despite the industry’s efforts to reduce its ecological footprint—primarily due to the lack of commercially available biodegradable, environmentally benign, and non-toxic reagents—these reagents continue to harm natural ecosystems. Chemicals used during the processing operations often target a specific unit operation that negatively affects downstream operations. In this study, we discover the concentration-dependent behavior of cellulose nanocrystals (CNCs) as a dispersant and flocculant, avoiding the use of various harmful chemicals in the nickel processing stages. Electrophoretic, optical brightfield microscopy, and quartz crystal microbalance-dissipation studies detected charge neutralization behavior by renewable and biodegradable CNCs, which can greatly benefit Ni processing operations. Microflotation experiments demonstrated that CNCs enhanced Ni recovery from 62 to 77 wt.% and concentrate grade from 15 to 20 wt.%. Settling and turbidity studies demonstrated the dual flocculant-dispersant behavior of CNCs resulting from the alignment of CNC fibers along the octahedral brucite basal and amphoteric edge plane of serpentine. CNCs can be used as dispersants during froth flotation to improve Ni beneficiation and as post-processing flocculants for tailings management and dewatering, which is one of the major environmental and social concerns facing the mining and mineral processing industry. This study paves the way for CNCs to be used as flocculants and dispersants in a range of industries from healthcare to pharmaceuticals to semiconductor devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Impedance matched superconducting nanowire photodetector for single- and multi-photon detection

Conventional readout of a superconducting nanowire single-photon detector (SNSPD) sets an upper bound on the output voltage to be the product of the bias current and the load impedance, IB×Zload, where Zload is limited to 50Ω in standard RF electronics. This limit is broken/exceeded by interfacing the 50Ω load and the SNSPD using an integrated superconducting transmission line taper. The taper is a transformer that effectively loads the SNSPD with high impedance without latching. The taper increases the amplitude of the detector output while preserving the fast rising edge. Using a taper with a starting width of 500 nm, a 3.6× higher pulse amplitude, 3.7× faster slew rate, and 25.1 ps smaller timing jitter was observed. The taper also makes the detector's output voltage sensitive to the number of photon-induced hotspots and enables photon number resolution.

Zhu, Di↗
DOE OSTI2020

Hypernetwork science via high-order hypergraph walks

We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width. Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients. We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods. Lastly, we apply three generative models to the data and find that basic hypergraph properties, such as density and degree distributions, do not necessarily control these new structural measurements. Our work demonstrates how analyses of hypergraph-structured data are richer when utilizing tools tailored to capture hypergraph-native phenomena, and suggests one possible avenue towards that end.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images

The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.

Science & Technology - Other Topics↗
DOE OSTI2024

LandScan Global 2023: Silver Edition

For a quarter of a century, the LandScan Global (LSG) project has annually released a global, high-resolution gridded population dataset representing the ambient or unwarned population at a 30 arcsecond resolution. LSG supports a range of applications such as emergency management, disaster response, and human health and security for understanding populations at risk. The 2023 release of LSG, the LandScan Silver Edition, represents a major methodological leap forward while also leveraging previous knowledge—the previous year was the baseline for the current annual update carrying forward valuable knowledge of the built environment for the past quarter century—to train the machine learning models. Compared with annual releases over the past 24years, multiple advancements were made to different aspects of the methodology to achieve reproducibility, transparency, and consistent global propagation of solutions to modeling or population distribution issues identified during the review process. These novel changes include incorporation of the latest available geospatial inputs across the globe, machine learning models instead of manual modifications, population feature importance analysis, open-source solutions vs. proprietary software, generation of multiple global versions, analytic validations, and human-in-the-loop revisions to produce the final version. Additionally, algorithms—such as anomaly detection—were introduced to quickly identify areas of focus to develop a new and robust systematic review. Significant changes in modeled population distributions were observed between the 2022 and 2023 releases, largely attributable to improvements in data and methods and discussed thoroughly within this report. In summation, the LandScan Silver Edition leverages the best of the past quarter century of LSG legacy knowledge and continues a tradition of applying cutting-edge enhancements to serve as a new benchmark for accurate, actionable gridded population data

Lebakula, Viswadeep↗
DOE OSTI2023

Off-resonant detection of domain wall oscillations using deterministically placed nanodiamonds

Abstract Nitrogen-vacancy (NV) centers in diamond offer a sensitive method of measuring the spatially localized dynamics of magnetization and associated spin textures in ferromagnetic materials. We use NV centers in a deterministically positioned nanodiamond to demonstrate off-resonant detection of microwave field-driven GHz-scale oscillations of a single domain wall (DW). The technique exploits the enhanced relaxation of NV center spins due to the broadband stray field noise generated by an oscillating DW pinned at an engineered defect in a lithographically patterned ferromagnetic nanowire. Discrepancies between the observed DW oscillation frequency and predictions from micromagnetic simulations suggest extreme sensitivity of DW dynamics to patterning imperfections such as edge roughness. These experiments and simulations identify potential pathways toward quantum spintronic devices that exploit current-driven DWs as nanoscale microwave generators for qubit control, greatly increasing the driving field at an NV center and thus drastically reducing the π pulse time.

Rable, Jeffrey↗
DOE OSTI2025

A Flang Plugin for Fortran Feature Characterization

As new compute systems are developed, there is still a need to compile and execute codes authored in Fortran on these leading edge systems. In order to achieve this, development of compilers that support the latest hardware is continuously under development. Though the specification of Fortran is extensive, it is helpful to compiler authors to be able to prioritize the development of key features in order to get certain codes deemed important, e.g., applications of interest to leadership computing facilities, executable on leading edge compute systems. Identifying key features though is largely done through querying software experts or users of the Fortran applications of interest, who then manually report what features are and are not present. This exercise can both time consuming and error prone. To automate this process, we present a compiler plugin to Flang, the Fortran frontend for LLVM. This plugin is a tool that operates on the parse tree representation generated by Flang and detects key features based on walking parse tree nodes that correspond to features of interest. We show the result of our tool on four applications, three of which were manually profiled by software experts. We show the discrepancies between our tool and the manual characterization of the three applications, as well as generate a characterization for an application not yet profiled. We intend to open-source our tool in order to invite the community to benefit from the tool and make contributions for other features.

Cabrera, Anthony [ORNL]↗