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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 OSTI2024

Finding a needle in a haystack: quantitative HERFD-XRF imaging and HERFD-XANES characterization of trace platinum in gold solidi from the Late Roman and Byzantine Empires

High-Energy Resolution Fluorescence Detection X-Ray Fluorescence (HERFD-XRF) imaging and HERFD X-ray Absorption Near Edge Structure (XANES) spectroscopy are used to quantify and characterize trace platinum (Pt) in gold solidi from the Late Roman and Byzantine Empires. Historically, the elemental analysis of coins has been pivotal in distinguishing authentic artifacts from forgeries, elucidating minting practices, and understanding economic shifts. Notably, a new gold source with high platinum content appeared in the fourth century CE, transforming the Roman economy. Traditional methods struggled to detect platinum due to the overwhelming gold matrix. Here, this study demonstrates the effectiveness of HERFD techniques in resolving this challenge. Three gold solidi, minted between 654 and 659 CE, were analyzed alongside reference gold materials with known Pt concentrations. The HERFD-XRF imaging revealed spatial distributions of platinum, highlighting non-uniformities within the coins. Additionally, HERFD-XANES spectroscopy identified the oxidation states and chemical speciation of platinum. Results demonstrate that platinum in the solidi primarily exists as metallic Pt, with some surface oxidation. The findings align with previous measurements but reveal higher Pt concentrations and significant inhomogeneities. This research confirms the reliability of HERFD methods for quantifying trace elements and provides new insights into the raw material sources and minting techniques of ancient gold coins. The non-destructive nature of this approach allows for extensive analyses, offering valuable data for historical, economic, and archaeological studies. This innovative application of HERFD-XRF imaging and XANES in cultural heritage research underscores the potential for detailed material characterization and conservation, enhancing our understanding of ancient economies and trade patterns.

Van Loon, Lisa L.↗
DOE OSTI2024

A multi-mass and multi-hit two-camera 3D ion momentum imaging system

We demonstrate an improved two-camera system for multi-mass and multi-hit three-dimensional (3D) momentum imaging of ions. The imaging system employs two conventional complementary metal–oxide–semiconductor cameras. We have shown previously that the system can time slice ion Newton spheres with a time resolution of 8.8 ns, limited by camera timing jitter [J. Chem. Phys., 158, 191104 (2023)]. In this work, a jitter correction method was developed to suppress the camera jitter and improve the time resolution to better than 2 ns. With this resolution, full 3D momentum distributions of ions can be obtained. We further show that this method can detect two ions with different masses when utilizing both the rising and falling edges of the cameras.

Instruments & Instrumentation↗
DOE OSTI2022

Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: a computational investigation

Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers, which limits measurement accuracy. Here, the performance of a different method for measuring RIXS, i.e. photoelectron spectrometry for analysis of X-rays (PAX), is computationally investigated. This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of high-throughput, compact electron spectrometers. X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. A deconvolution algorithm for analysis of such PAX data is proposed. It is shown that the deconvolution algorithm works well on data recorded with ~0.5 eV resolution. Additional simulations show the potential of PAX for estimation of RIXS features with smaller widths. For simulations using the 3d levels of Ag as a converter material, and with 10 5 simulated detected electrons, it is estimated that features with a few hundred meV width can be accurately estimated in a model RIXS spectrum. For simulations using a sharp Fermi edge to encode RIXS spectra, it is estimated that one can accurately distinguish 100 meV FWHM peaks separated by 45 meV with 10 5 simulated detected electrons that were photoemitted from within 0.4 eV of the Fermi level.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

First evaluation of fast neutron imaging with LiInSe 2 semiconductors

Fast neutron imaging is a powerful tool to investigate elemental/isotopic compositions of objects, supporting both scientific studies as well as cargo scanning. Current neutron imaging systems are faced with challenges associated with timing, detection efficiency, and/or spatial resolution. Here, we report on the use of a semiconducting lithium indium diselenide neutron sensor coupled to a Timepix ASIC for fast neutron imaging. Using a 15 cm thick copper knife edge, the spatial resolution of the neutron imager was found to be 1.55 mm for 9 MeV neutrons. In conclusion, the experimental detection efficiency at 9 MeV was in general agreement with calculations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

On the Robustness of Network Community Structure Under Addition of Edges

Communities represent important functional modules in networked systems. A key goal in preserving such communities is understanding their robustness under perturbations. Previous research has studied the impact of node removals and edge removals on the community structure. However, the impact of edge additions on the robustness of the community structure is relatively unknown. Edge additions or false positive edges may simulate measurement errors or external exceptional events that threaten the functionality of networked systems. Here, we study the impact of edge additions on the community structure using Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. We show that, for a fixed network size, the impact of edge additions is greater on networks with initially weak community structure than on networks with strongly clustered structures. In addition, we find that the perception of the impact is also dependent on the community detection algorithm used to uncover communities. In particular, we found that modularity-based methods such as Leiden and Louvain are less affected than information-theoretic and message passing-based methods such as Infomap and Label Propagation. Our results demonstrate that edge addition can (a) significantly impact the community structure of networks based on their initial conditions, and (b) the perception of the impact is dependent on the community detection algorithm used. We describe limitations, open challenges, and how this methodology can inform the design of resilient networked systems under edge additions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2022

Scintillation light detection in the 6-m drift length ProtoDUNE Dual Phase liquid argon TPC

The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE-Dual Phase (DP) is a 6 × 6 × 6 m$^{3}$ liquid argon time-projection-chamber (LArTPC) operated at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE far detector. In ProtoDUNE-DP, the scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, we present the performance of the ProtoDUNE-DP photon detection system, comparing different wavelength-shifting techniques and the use of xenon-doped LAr as a promising option for future large LArTPCs. The scintillation light production and propagation processes are analyzed and compared to simulations, improving understanding of the liquid argon properties.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Controlling T c of iridium films using the proximity effect

A superconducting Transition-Edge Sensor (TES) with low-$T_c$ is essential in a high resolution calorimetric detection. With a motivation of developing sensitive calorimeters for applications in cryogenic neutrinoless double beta decay searches, we have been investigating methods to reduce the $T_c$ of an Ir film down to 20 mK. Utilizing the proximity effect between a superconductor and a normal metal, we found two room temperature fabrication recipes of making Ir-based low-$T_c$ films. In the first approach, an Ir film sandwiched between two Au films, a Au/Ir/Au trilayer, has a tunable $T_c$ in the range of 20-100 mK depending on the relative thicknesses. In the second approach, a paramagnetic Pt thin film is used to create Ir/Pt bilayer with a tunable $T_c$ in the same range. We present detailed study of fabrication and characterization of Ir-based low-$T_c$ films, and compare the experimental results to theoretical models. We show that Ir-based films with predictable and reproducible critical temperature can be consistently fabricated for use in large scale detector applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Young but fading radio sources: searching for remnants among compact steep-spectrum radio sources

The incidence of young but fading radio sources provides important information on the life cycle of radio emission in radio-loud active galactic nuclei. Despite its importance for constraining the models of radio source evolution, there are no systematic studies of remnants in complete samples of young radio sources. We report results of the study of 18 compact steep-spectrum (CSS) radio sources, selected from the statistically complete B3-VLA (Very Large Array) CSS sample, characterized by a steep optically thin spectrum (α ≥ 1.0) and no core detection in earlier studies. Our deep multifrequency VLA, pc-scale Very Long Baseline Array, and enhanced Multi Element Remotely Linked Interferometer Network (e-MERLIN) observations allowed us to locate the core component in 10 objects. In three CSS sources, there is no clear evidence of present-time active regions, suggesting that they are likely in a remnant phase. Among sources with core detection, we find three objects that have no clear active regions (hotspots) at the edges of the radio structure, suggesting that the radio emission may have just restarted. Our results support a power-law distribution of the source ages, although the poor statistics prevents us from setting solid constraints on the percentage of remnants and restarted sources in subpopulations of radio sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Elucidating Cathode Degradation Mechanisms in LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)/Graphite Cells Under Fast Charge Rates Using Operando Synchrotron Characterization

Li-ion batteries capable of extreme fast charging (XFC) are in demand to facilitate widespread electric vehicle (EV) adoption. While the impact of fast charge on the negative electrode has been studied, degradation of state-of-the-art NMC811 under XFC conditions has not been studied in detail. Herein, cathode degradation is probed in NMC811/graphite batteries by analysis of structural and chemical changes for recovered samples previously cycled under XFC conditions and during typical cycling. NMC surface reconstruction, as determined by soft X-ray absorption, was not detected for recovered electrodes. Furthermore, bulk redox activity from X-ray absorption near edge structure measurements showed more change in the oxidation state of Ni and Co under the 1C charge rate compared to the 4C rate consistent with the electrochemistry. Increased unit cell volume contraction under the 1C rate as determined by operando X-ray diffraction suggests that higher charge rates may provide a protective effect on the cathode by reducing structural distortion due to less delithiation.

25 ENERGY STORAGE↗
DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2024

Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Characterization of dangling bond defects at the crystalline Si/SiO x interface in a polycrystalline Si passivating contact solar cell at room temperature with electrically detected magnetic resonance spectroscopy

Monocrystalline silicon solar cells can achieve photoconversion efficiencies exceeding 26%; however, performance-limiting defects that trap carriers continue to be a challenge. In this work, we have characterized Si solar cells with tunneling SiO x /polycrystalline-Si (poly-Si) passivating contacts (TOPCon) on As-doped Czochralski Si wafers with electrically detected magnetic resonance (EDMR) spectroscopy. We fabricated 2 × 20 mm 2 TOPCon-like mini solar cells with edge passivation alongside larger 4 cm 2 sister cells and obtained similar device characteristics. We performed EDMR spectroscopy at 300 K on two minicells with different degrees of surface passivation based on the recombination parameter, J o , values of 40 and 310 fA/cm2. We optimized the resolution and the signal-to-noise ratio of the EDMR response of the minicells by varying the forward bias voltage and the magnetic field modulation amplitude. We detect two distinct signals with EDMR spectroscopy, an axial-like signal at g = 2.009, 2.0087, and 2.0015, and an isotropic signal at g = 2.0024, which we attribute to Si dangling bonds (P b0 and P b centers) and boron–oxygen related defects, respectively, at or near the c-Si/SiO x interface. The EDMR signals were lower for the cell with a lower value of J o , while the ratio of the two defect populations was very similar. The EDMR signal increases with forward bias but drops to zero at bias voltages >0.5 V, consistent with interface defects within or near the boron-doped emitter depletion region. Our study demonstrates a method to fabricate minicells that can be characterized with EDMR spectroscopy to detect industrially relevant defects in TOPCon cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Probing Basal and Prismatic Planes of Graphitic Materials for Metal Single Atom and Subnanometer Cluster Stabilization

Abstract Supported metal single atom catalysis is a dynamic research area in catalysis science combining the advantages of homogeneous and heterogeneous catalysis. Understanding the interactions between metal single atoms and the support constitutes a challenge facing the development of such catalysts, since these interactions are essential in optimizing the catalytic performance. For conventional carbon supports, two types of surfaces can contribute to single atom stabilization: the basal planes and the prismatic surface; both of which can be decorated by defects and surface oxygen groups. To date, most studies on carbon‐supported single atom catalysts focused on nitrogen‐doped carbons, which, unlike classic carbon materials, have a fairly well‐defined chemical environment. Herein we report the synthesis, characterization and modeling of rhodium single atom catalysts supported on carbon materials presenting distinct concentrations of surface oxygen groups and basal/prismatic surface area. The influence of these parameters on the speciation of the Rh species, their coordination and ultimately on their catalytic performance in hydrogenation and hydroformylation reactions is analyzed. The results obtained show that catalysis itself is an interesting tool for the fine characterization of these materials, for which the detection of small quantities of metal clusters remains a challenge, even when combining several cutting‐edge analytical methods.

Vidal, Mathieu↗
DOE OSTI2021

Probing the interplay between lattice dynamics and short-range magnetic correlations in CuGeO 3 with femtosecond RIXS

Investigations of magnetically ordered phases on the femtosecond timescale have provided significant insights into the influence of charge and lattice degrees of freedom on the magnetic sub-system. However, short-range magnetic correlations occurring in the absence of long-range order, for example in spin-frustrated systems, are inaccessible to many ultrafast techniques. Here, we show how time-resolved resonant inelastic X-ray scattering (trRIXS) is capable of probing such short-ranged magnetic dynamics in a charge-transfer insulator through the detection of a Zhang–Rice singlet exciton. Utilizing trRIXS measurements at the O K-edge, and in combination with model calculations, we probe the short-range spin correlations in the frustrated spin chain material CuGeO 3 following photo-excitation, revealing a strong coupling between the local lattice and spin sub-systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Community detection in hypergraphs via mutual information maximization

Abstract The hypergraph community detection problem seeks to identify groups of related vertices in hypergraph data. We propose an information-theoretic hypergraph community detection algorithm which compresses the observed data in terms of community labels and community-edge intersections. This algorithm can also be viewed as maximum-likelihood inference in a degree-corrected microcanonical stochastic blockmodel. We perform the compression/inference step via simulated annealing. Unlike several recent algorithms based on canonical models, our microcanonical algorithm does not require inference of statistical parameters such as vertex degrees or pairwise group connection rates. Through synthetic experiments, we find that our algorithm succeeds down to recently-conjectured thresholds for sparse random hypergraphs. We also find competitive performance in cluster recovery tasks on several hypergraph data sets.

97 MATHEMATICS AND COMPUTING↗