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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Template size and proper overlap detection in Laser Confocal Microscope (LCM) images [Slides]

The LCM has been used to interrogate the inside of hazardous material 3013 storage containers resulting in many LCM images. Joining these images together to create one large image allows one to see features, potential cracking and potential pitting, that extend from one image to another. The images have been found to cover some (5% – 10% at the edges) of the same sample area. This overlap appears to change slightly from image to image so that a global overlap value does not solve the stitching problem. It will be necessary to determine the proper overlap for two adjacent, horizontal or vertical, images. The methodology presented here shows how to estimate the overlap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Detection of the Chiral Spin Structure in Ferromagnetic SrRuO 3 Thin Film

SrRuO3 (SRO) thin films and their heterostructure have brought much attention because of the recently demonstrated fascinating properties, such as topological Hall effect and skyrmions. Critical to the understanding of those SRO properties is the study of the spin configuration. Here, we conduct resonant soft x-ray scattering (RSXS) at the oxygen K-edge to investigate the spin configuration of a 4 unit-cell SRO film that was grown epitaxially on a single crystal SrTiO 3 . The RSXS signal under a magnetic field (~0.4 Tesla) clearly shows a magnetic dichroism pattern around the specular reflection. Model calculations on the RSXS signal demonstrate that the magnetic dichroism pattern originates from a Néel-type chiral spin structure in this SRO thin film. We believe that the observed spin structure of the SRO system is a critical piece of information for understanding its intriguing magnetic and transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Template‐Directed Growth of Palladium Nanoparticles in Holey Single‐Layer Graphene for High‐Performance Room‐Temperature Hydrogen Sensing

Holey graphene (HG), formed by introducing nanoscale perforations into graphene sheets, combines the structural advantages of continuous sp 2 conjugation with the beneficial effects of high surface area and enhanced chemical reactivity associated with nanoscopic holes. Conventional HG fabrication methods often rely on harsh oxidative treatments that compromise graphene's intrinsic electronic properties by disrupting its sp 2 conjugation. A new approach is introduced to fabricate HG directly from single-layer graphene (SLG), using two-dimensional covalent organic frameworks (COFs) as a template followed by controlled oxygen plasma etching. This method preserves the integrity of the SLG's sp 2 network while producing well-defined holes with an average diameter of 2.7 nm. These hole edges act as reactive sites that facilitate the confined, autoreductive growth of palladium nanoparticles (PdNPs) without external reducing agents. The confined hole geometry prevents NP agglomeration and ensures a uniform size distribution. The resulting Pd@HG hybrid exhibits exceptional chemiresistive hydrogen sensing characteristics, including ultrahigh sensitivity, low detection limits, rapid response and recovery, and long-term stability under both dry and humid conditions. Mechanistic investigations reveal a two-step sensing process involving surface redox interactions and hydrogen absorption into the PdNP lattices. This strategy presents a scalable platform for integrating metal NPs within conductive carbon frameworks for advanced sensing applications.

chemical vapor deposition↗
DOE OSTI2025

Soft x-ray spectroscopy of ammonium sulfate aerosols at varied pH conditions

Aqueous aerosols are useful model systems for understanding the physical chemistry that takes place in the Earth’s atmosphere, as well as the chemistry of other solar system objects. Ammonium sulfate aerosols are impacted by anthropogenic sources such as farming and shipping and are understood to be important seeds of cloud nucleation in the atmosphere, affecting climate. X-ray photoelectron spectroscopy and near edge x-ray absorption fine structure spectroscopy were used in tandem to probe the surface and bulk properties of aqueous (NH 4 ) 2 SO 4 , respectively. Aerosolized solutions of (NH 4 ) 2 SO 4 , some altered with NaOH to modify pH, were introduced via an aerodynamic lens to a velocity map imaging instrument for detection. The results show that as pH and sodium cation concentration increase, sulfate anion and ammonia become more prevalent at the surface, and the aerosol surface appears to become drier. However, most of the aerosol remains aqueous, with ion concentrations insensitive to the addition of NaOH. Density functional theory calculations were also performed to probe the coordination environment at the aerosol surfaces to understand the underlying phenomena. Finally, the changes seen on the surface of the aerosol underscore the importance of pH in regulating the structure and chemical properties of atmospherically relevant aerosols.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Eos: conceptual design for a demonstrator of hybrid optical detector technology

Eos is a technology demonstrator, designed to explore the capabilities of hybrid event detection technology, leveraging both Cherenkov and scintillation light simultaneously. With a fiducial mass of four tons, Eos is designed to operate in a high-precision regime, with sufficient size to utilize time-of-flight information for full event reconstruction, flexibility to demonstrate a range of cutting edge technologies, and simplicity of design to facilitate potential future deployment at alternative sites. Results from Eos can inform the design of future neutrino detectors for both fundamental physics and nonproliferation applications. This paper describes the conceptual design and potential applications of the Eos

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2020

Hexagonal boron nitride: Epitaxial growth and device applications

As a newest family member of the III-nitrides, BN is considered amongst the remaining frontiers in wide energy bandgap semiconductors with potentials for technologically significant applications in deep UV (DUV) optoelectronics, solid-state neutron detectors, electron emitters, single photon emitters, switching/memory devices, and super-capacitors. It was shown that it is possible to produce h-BN epilayers with high hexagonal phase purity, UV transparency, and film stoichiometry by employing nitrogen-rich growth conditions. The quasi-2D nature of h-BN supports unusually strong optical transitions near the band edge and a large exciton binding energy on the order of 0.7 eV. Since the isotope of B-10 has a large capture cross-section for thermal neutrons, h-BN is an ideal material for the fabrication of solid-state neutron detectors for special nuclear materials detection, well and geothermal logging, and medical imaging applications. Freestanding B-10 enriched h-BN (h- 10 BN) epilayers with varying thicknesses up to 200 μm have been successfully synthesized by metal organic chemical vapor deposition (MOCVD) as of this writing. By utilizing the conductivity anisotropy nature of h-BN, 1 cm 2 lateral detectors fabricated from 100 μm thick h-10BN epilayers have demonstrated a detection efficiency of 59% for thermal neutrons, which is the highest on record among all solid-state neutron detectors as of today. It was noted that high growth temperatures, long growth times and the use of sapphire substrate tend to incorporate oxygen related impurities into h- 10 BN epilayers, which strongly impacted the carrier mobility-lifetime (μτ) products and charge collection efficiencies of h- 10 BN neutron detectors. As the h-BN material technology further develops, improved carrier mobilities and μτ products will allow the fabrication of h-BN devices with enhanced performance.

42 ENGINEERING↗
DOE OSTI2024

Understanding Formation of Irradiation-Induced Defects through 4D-STEM, Electron Tomography, and WBDF-STEM

A major challenge in advancing nuclear materials for next-generation fission and proposed fusion reactors is to comprehensively understand the formation of irradiation-induced defects. Here it is essential to correlate the evolution of irradiation-induced defects and the degradation of mechanical properties, as they collectively dictate the material's lifespan and ensure nuclear safety. Scanning transmission electron microscopy (STEM) based techniques have emerged as indispensable tools for irradiation-induced defect characterization, offering high spatial resolution imaging and chemical analysis, such as electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDXS). These techniques have been effectively used to obtain an atomic-scale view of the defect structure. Recent advances in electron microscopy, particularly in 4D-STEM, offer detailed insight into microstructural evolution by capturing full 2D diffraction patterns at every pixel position. Using high-speed direct electron detectors, this technology generates a four-dimensional dataset, overcoming the limitations of traditional STEM imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Gravitation and Mesh Adaption

The Gravitation and Mesh Adaptation (GaMA) toolbox consists of a set of Matlab classes for modeling the environment around asteroids and comets. A variety of gravitational models and supporting algorithms from are consolidated alongside a custom meshing utility tailored for the application. This allows the user to work within a single streamlined environment to import and manipulate surface definitions, probe the dynamical environment, integrate trajectories, and post-process results. For other applications, modified surface meshes can be exported. Gravity Models: 1) Werner's analytic polyhedron model 2) Mascon model 3) Gottlieb's spherical harmonic model 4) Approximate polyhedron models 5) Curvilinear surface models 6) Custom composite models Meshing Features: 1) Array-based half-edge data structure 2) Supports curvilinear surface definitions up to degree 4 3) Ray-tracing 4) Coarsening 5) Feature-based refinement 6) Projection 7) Smoothing 8) Mesh quality validity tests 9) Mesh repair Additional Features: 1) Solar radiation pressure model 2) Distant 3rd body model 3) Collision detection 4) Trajectory integration and post-processing 5) Visualization of surface fields

Pearl, Jason↗
DOE OSTI2020

Can We Fix It Automatically? Development of Fault Auto-Correction Algorithms for HVAC and Lighting Systems

A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.

Lin, Guanjing↗
DOE OSTI2022

ECEI characterization of pedestal fluctuations in quiescent H-mode plasmas in DIII-D

Here, electron cyclotron emission imaging (ECEI) is employed to characterize the magneto hydraulics dynamics (MHD) fluctuations at the quiescent H-mode pedestals in DIII-D. Pedestal MHD fluctuations cause ECE radiation temperature fluctuations $\delta {T_{e{\text{,rad}}}}$ in both the pedestal and scrape-off-layer (SOL). A synthetic ECE platform is utilized for detailed interpretation of the ECEI signals in the SOL and pedestal regions. It is observed that the ECE radiation $\delta {T_{e{\text{,rad}}}}$, which is located in the SOL region according to the cold and optically thick plasma resonance assumption, is extremely sensitive to MHD radial displacements near the separatrix, exhibiting radiation inversion to $\delta {T_{e{\text{,rad}}}}$ at the pedestal. Here, the radiation inversion refers to the opposite phase between the radiation fluctuation at the pedestal and the radiation fluctuation at the SOL. Consequently, the quasi-coherent MHD (QCM), which displays a radiation inversion, is found to be consistent with an MHD radial structure that has a strong displacement near the separatrix. In contrast, the edge harmonic oscillation (EHO), which displays weak or no inversion, is found to be consistent with an MHD radial displacement structure peaking at the pedestal top. The ECEI data, interpreted with synthetic ECE, are in qualitative agreement with beam emission spectroscopy measurements on DIII-D for the relative radial extent and localization of the EHO and QCM. The high sensitivity of ECE radiation to separatrix displacements can be used to detect turbulence or MHD fluctuations near the separatrix, which may affect the transport across the separatrix and the wetted area in the divertor. The $\delta {T_{e{\text{,rad}}}}$ inversion measured with an ECE or ECEI system potentially provides important information on the magnetic field $\left| {{B_{{\text{sep}}}}} \right|$ at the separatrix, which helps constrain the pedestal equilibrium reconstruction and achieve an unambiguous mapping of the ECE/ECEI system with respect to the separatrix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

3D-Reconstruction of Tau Neutrinos in LArTPC Detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment currently under construction. DUNE will consist of two high-resolution neutrino interaction imaging detectors exposed to the world’s most intense neutrino beam, with the Near Detector at Fermilab and the Far Detector 1,300 km away in the Sanford Underground Research Facility in South Dakota, US. The high statistics and excellent resolution capabilities of DUNE's $^{40}$Ar detector will allow us to make precision studies of oscillation parameters capable of searching for CP violation in the lepton sector, testing interaction models, and studying phenomena that have until now, seemed too complex to measure, like $\nu_\tau$ detection and therefore, providing the completion of the 3-flavor neutrino paradigm. Knowledge of the $\nu_\tau$ detection can impact a broad spectrum of open questions. These include searching for non-standard neutrino interactions, constraining the unitarity of the PMNS matrix, searching for sterile neutrinos, and studying neutrino interactions. In the case of LArTPC data, the detector hits can be considered nodes in a graph, and the edges represent the spatial and temporal relationships between them. By using graph neural networks, it is possible to exploit these relationships and improve the accuracy of particle identification and reconstruction. During my presentation and specifically for tau neutrino reconstruction, I will show the effectiveness and reliability of our in-house developed graph neural network (GNN), NuGraph. This GNN classifies detector hits based on the particle type responsible for their production, assuring that the system accurately identifies and categorizes information based on its unique characteristics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

University Research to Support the MPACT 2020 Milestone

University research is a strong focus of the Office of Nuclear Energy within the Department of Energy. This research complements existing work in the various program areas and provides support and training for students entering the field. Four university projects have provided support to the Material Protection Accounting and Controls Technologies (MPACT) 2020 milestone focused on safeguards for electrochemical processing facilities. The University of Tennessee Knoxville has examined data fusion of NDA measurements such as Hybrid K-Edge Densitometry and Cyclic Voltammetry. Oregon State University and Virginia Polytechnic Institute have examined the integration of accountancy data with process monitoring data for safeguards. The Ohio State University and the University of Utah have developed a Ni-Pt SiC Schottky diode capable of high temperature alpha spectroscopy for actinide detection of molten salts. Finally, the University of Colorado has developed a key enabling technology for the use of Microcalorimetry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2021

Investigation of a digitizer for the plastic scintillation detectors of time-of-flight mass measurements

A CAEN DT5742 digitizer has been investigated to process the fast signals from the photomultiplier tubes of the time-of-flight detectors for fast ion beams. A test setup consisting of two plastic scintillation detectors and a pulsed laser source provided signals which were recorded by the digitizer and systematically analyzed with different algorithms to derive the amplitude, rise time and arrival time of the detection signal. To obtain the best amplitude and time resolutions, various optimization techniques including peak fitting and signal smoothing, interpolation improvement, and time-walk correction by amplitude and rise time have been performed and compared in detail. Here we compared the time resolutions obtained by three digital algorithms of leading-edge, zero-crossing constant-fraction (ZC-CFD) and direct constant-fraction discriminations. Finally, we found that the best time-of-flight resolution between two PMTs can be achieved as 12 ps by the method using sample minimum for peak location and using the time-walk-resistant ZC-CFD with 4-point interpolation for timing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Identity of the JET M-mode and the ASDEX Upgrade I-phase phenomena

An H-mode plasma state free of edge-localized mode (ELM), close to the L-H transition with clear density and temperature pedestal has been observed both at the Joint European Torus (JET) and at the ASDEX Upgrade (AUG) tokamaks usually identified by a low frequency (LFO, 1–2 kHz), m = 1, n = 0 oscillation of the magnetics and the modulation of pedestal profiles. The regime at JET is referred to as M-mode while at AUG as intermediate phase or I-phase. This contribution aims at a comparative analysis of these phenomena in terms of the density and temperature pedestal properties, the magnetic oscillations and symmetries. Lithium beam emission spectroscopy (Li-BES) and reflectometer measurements at JET and AUG show that the M-mode and the I-phase modulates the plasma edge density. A high frequency oscillation of the magnetics and the density at the pedestal is also associated with both the M-mode and the I-phase, and its power is modulated with the LFO frequency. The power modulation happens simultaneously in every Mirnov coil signal where it can be detected. The bursts of the magnetic signals and the density at the pedestal region are followed by the flattening of the density profile, and by a radially outward propagating density pulse in the scrape-off layer (SOL). The analysis of the helium line ratio spectroscopy (He-BES) signals at AUG revealed that the electron temperature is modulated in phase with the density, thus the I-phase modulates the pressure profile gradient. Overall, this analysis gave opportunity to compare Li-BES and He-BES density profiles at different locations suggesting a toroidal and poloidal symmetry of the density modulation. The presented results indicate that the regimes, the AUG I-phase and the JET M-mode, exhibit similar characteristics, which leads to the conclusion that the regimes are likely the same.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗