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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Some challenging points in the identification of defects in floating-zone n-type silicon irradiated with 8 and 15 MeV protons

Electrical properties of defects formed in n-Si(FZ) following 8 and 15 MeV proton irradiation are investigated by Hall effect measurements over the wide temperature range of T ≈ 25 to 300 K. Close attention is paid to the damaging factor of proton irradiation, leaving aside passivation effects by hydrogen. The concept of defect production and annealing processes being accepted in the literature so far needs to be reconsidered. Contrary to expectations the dominant impurity-related defects produced by MeV protons turn out to be electrically neutral in n-type material. Surprisingly, radiation acceptors appear to play a minor role. Annealing studies of irradiated samples of such complex defects as a divacancy tied to a phosphorus atom and a vacancy tied to two phosphorus atoms. The latter defect features high thermal stability. Identification of the dominant neutral donors, however, remains unclear and will require further, more detailed, studies. The electric properties of the material after proton irradiation can be completely restored at T = 800°C.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Identification of stress biomarkers for drought and increased soil temperature in seedlings of European beech ( Fagus sylvatica )

Drought is an environmental stress that impacts plant productivity. Projections show both an increase in intense rain events and a reduction in the number of rain days, conditions that leads to increased risk of drought. Consequently, the identification of molecular biomarkers suitable for evaluating the impact of water deprivation conditions on forest plant seedlings is of significant value for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of drought stress coupled with variable soil temperature in European beech (Fagus sylvatica L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the specific activities and isoform profile of superoxide dismutases and glutathione peroxidases together with the variation of phenolic compounds enable discrimination between seedlings with different degrees of photosynthetic activity. This approach represents a promising platform for the assessment of drought stress in forest trees and could serve for enhancing selection and breeding practices, allowing for plants that are more tolerant of abiotic stress.

beach↗
DOE OSTI2020

JEDI-net: a jet identification algorithm based on interaction networks

Abstract We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Characterization of the scintillation response of water-based liquid scintillator to alpha particles, and implications for particle identification

Next-generation large-scale neutrino detectors, from EOS, at the 1 t scale, to THEIA, at the 10 s-of-kt scale, will utilize differences in both the scintillation and Cherenkov light emission for different particle species to perform background rejection. This manuscript presents measurements of the scintillation light yield and emission time profile of water-based liquid scintillator samples in response to α radiation. These measurements are used as input to simulation models used to make predictions for future detectors. In particular, we present the timing-based particle identification achievable in generic water-based scintillator detectors at the 4 t, 1 kt, and 100 kt scales. We find that α/β discrimination improves with increasing scintillation concentration and we identify better than 80% α rejection for 90% β acceptance in 10% water-based liquid scintillator, at the 4 t scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

CUPID, the Cuore upgrade with particle identification

CUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay (0 νββ ) and other rare events using enriched Li 2 100 MoO 4 scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for 0 νββ of 100 Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10 −4 cts/(keV·kg·yr), 240 kg isotope mass, 5 keV FWHM energy resolution at 3 MeV and 10 live-years of data taking, CUPID will have a 90% C.L. half-life exclusion sensitivity of 1.8 · 10 27 yr, corresponding to an effective Majorana neutrino mass ( m ββ ) sensitivity of 9–15 meV, and a 3σ discovery sensitivity of 1 · 10 27 yr, corresponding to an m ββ range of 12–21 meV.

Alfonso, K. [Virginia Polytechnic Institute and St↗
DOE OSTI2026

Evaluation of Compton suppression for enhancing trace element identification in neutron activation analysis of reference materials

Neutron activation analysis (NAA) is a powerful technique for identifying and quantifying trace elements in materials. However, challenges such as high dead times, spectral interferences, and high Compton continuum often arise. This study employs a Compton suppression system (CSS) to enhance NAA sensitivity by reducing the Compton continuum, thereby improving the peak-to-Compton ratio. National Institute of Standards and Technology certified reference materials 1632d, 1633c, and Canadian National Research Council TORT-1 were irradiated in a thermal and epithermal neutron flux under various irradiation, decay and counting times and analyzed using high-resolution gamma-ray spectroscopy with and without Compton suppression. The reduction factor was calculated to evaluate the effectiveness of the CSS, demonstrating significant background reduction and improved detection limits for trace elements. Additional experiments with a 137Cs point source demonstrated the impact of detector-source geometry on system performance, showing a decrease in the reduction factor as the source was moved further from the NaI detector. An optimum distance between the source and HPGe detector was observed, yielding the highest peak-to-Compton ratio. The results highlight the CSS's ability to minimize spectral interference and enhance elemental identification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Spatiotemporal Visualization and Chemical Identification of the Metal Diffusion Layer at the Electrochemical Interface

The diffusion layer created by transition metal dissolution is ubiquitous at the electrochemical solid-liquid interface and plays a key role in determining electrochemical performance. Tracking the spatiotemporal dynamics of the diffusion layer has remained an unresolved challenge. With spatially resolved synchrotron X-ray fluorescence microscopy and micro-X-ray absorption spectroscopy, we demonstrate the in situ visualization and chemical identification of the dynamic diffusion layer near the electrode surface under electrochemical operating conditions. Finally, our method allows for direct mapping of the reactive electrochemical interface and provides insights into engineering the diffusion layer for improving electrochemical performance.

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

Identification of therapeutic targets in a murine model of severe exertional heat stroke

Exertional heat stroke (EHS) is a potentially lethal condition resulting from high core body temperatures (T C ) in combination with a systemic inflammatory response syndrome (SIRS) with varying degrees of severity across victims, and limited understanding of the underlying mechanism(s). We established a mouse model of severe EHS to identify mechanisms of hyperthermia/inflammation that may be responsible for organ damage. Mice were forced to run on a motorized wheel in a 37.5°C chamber until loss of consciousness and were either removed immediately (exertional heat injury or EHI; T CMax = 42.4 ± 0.2°C) or remained in the chamber an additional 20 min (EHS; T CMax = 42.5 ± 0.4°C). Exercise control mice (ExC) experienced identical procedures to EHS at 25°C. At 3 h post-EHS, there was evidence for an immune/inflammatory response as elevated blood chemokine [interferon γ-induced protein 10 (IP-10), keratinocytes-derived chemokine (KC), macrophage inflammatory proteins (MIP-1α), MIP-1β, MIP-2] and cytokine [granulocyte colony-stimulating factor (G-CSF), interleukins (IL-10), IL-6] levels peaked and were highest in EHS mice compared with EHI and ExC mice. Immunoblotting of organs susceptible to EHS damage indicated that several kinases were sensitive to stress associated with heat/inflammation and exercise; specifically, phosphorylation of liver c-Jun NH 2 -terminal kinase (JNK) at threonine 183/tyrosine 185 immediately (0 h) postheating related to heat illness severity. We have established a mouse EHS model, and JNK [or its downstream target(s)] could underlie EHS symptomatology, allowing the identification of molecular pathways or countermeasure targets to mitigate heat illness severity, enable complete recovery, and decrease overall EHS-related fatalities.

Physiology↗
DOE OSTI2022

Drug Target and Chemotype Identification Pipeline

The TargetID pipeline enables rapid identification and characterization of binding sites in SARS-CoV-2 proteins as well as the core chemical components with which these sites interact.

Sandholtz, SarahH↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2022

Latent Space Dynamics Identification

LaSDI is a data-driven physical simulation software that forms a latent space for a given high-fidelity model and discovers a set of ordinary differential equations for the latent space dynamics. It allows a fast and accurate solution process, which is useful for multi-query decision making applications, such as design optimization and uncertainty quantification. The performance of the LaSDI framework is demonstrated on four different problems, i.e., 1D and 2D Burgers equations, nonlinear heat conduction, and radial advection problems. Both linear and nonlinear compression techniques, such as neural network and proper orthogonal decomposition, are used to form a latent space. A concept of local dynamics identification procedure is introduced to enable a parametric model, which enhances the accuracy level over a given parameter space.

Fries, William↗
DOE OSTI2024

Thermodynamics-informed latent space dynamics identification

This software showcases a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.

Cheung, Siu Wun↗
DOE OSTI2025

Identification of shared viral sequences in peat moss metagenomes reveals elements of a possible Sphagnum core virome

Viruses are an understudied component of plant microbiomes. Identifying viruses that are shared between individual plants, or members of the “core virome”, could reveal stable viral populations with the potential to modulate the composition and function of the microbiome. Here, we examined the virome associated with Sphagnum mosses, a keystone species that has direct influence over the fate of peatland carbon stores. We analyzed bulk metagenomes and metatranscriptomes generated from Sphagnum field samples collected over a ten-month period to identify virus-like sequences shared among plants. Individual Sphagnum samples harbored distinct DNA and RNA viromes where only a small percentage (< 1%) of the total number of identified viral contigs were shared among all samples. Based on taxonomic classification, the shared viral contigs represent bacterial viruses, or phage (Caudoviricetes), as well as viruses of eukaryotes, namely nucleocytoplasmic large DNA viruses (Nucleocytoviricota) and RNA viruses (Riboviria). We linked the shared phage-like contigs to viral regions within sequenced genomes of bacterial taxa that are members of the Sphagnum core microbiome, suggesting that these contigs represent temperate phage or degraded prophage. The putative nucleocytoplasmic large DNA viruses and RNA viruses were phylogenetically diverse and showed sequence similarity to viruses associated with a broad range of hosts and environmental sources. The identification of shared viral contigs suggested that, despite the compositional heterogeneity between samples, Sphagnum mosses may harbor a core virome. Future work validating the presence of the core virome is warranted as it may aid in understanding how persistent viruses impact microbiome ecology and symbiont evolution within this climatically relevant keystone species.

Metagenomics↗
DOE OSTI2024

Enhanced material identification via momentum-integrated muon scattering tomography

Cosmic ray muons, originating from interactions in the upper atmosphere, possess high energy and unique penetrative capabilities suitable for non-traditional radiographic inspection. This study explores their application in various fields such as nuclear fuel cask monitoring, nuclear reactor imaging, and archaeology, leveraging the principle of multiple Coulomb scattering for imaging dense materials. While muon scattering tomography has shown promise, accurately measuring muon momentum remains challenging. This research introduces the Momentum Integrated Point-of-Closest Approach (mPoCA) algorithm, integrating muon momentum data into the traditional Point-of-Closest Approach (PoCA) framework. Utilizing the Cherenkov muon spectrometer, renowned for precise muon momentum estimation, the mPoCA algorithm offers a novel imaging approach. Simulations conducted with GEANT4 evaluate the mPoCA algorithm’s performance against the standard PoCA method, demonstrating superior image resolution and enhanced material identification capabilities, particularly in distinguishing materials like uranium and lead. These findings underscore the potential of the mPoCA algorithm for advancing muon scattering tomography applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Driver Identification Dataset

The ORNL Driver Identification Dataset was created to collect and analyze driving behavior data from 50 different drivers. Each driver operated a 2014 Kenworth T270 Class 6 truck around Fort Collins, Colorado while various data sources recorded their driving behavior and vehicle performance. The dataset includes CANbus (Controller Area Network) data, GPS data, inertial measurement data, and biometric data from a heart rate monitor. A cyberattack was executed during each drive, which caused multiple dashboard warning lights to illuminate and set the tachometer and speedometer to zero, regardless of actual speed. The attack was stopped either after one minute or if the driver pulled over. By downloading the dataset, you agree to the following: 1) I will not use or disclose the data for any purpose other than Research as that term is defined in 10 CFR 745.102. 2) I will not, under any circumstances, request or accept private or linking identifiers for the data used. 3) I will not attempt to determine the identity of the individuals associated with the data. 4) I will use appropriate safeguards to prevent the use or disclose of the data for any purpose other than Research.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Identification of Genome-Wide Mutations in Ciprofloxacin-Resistant F. tularensis LVS Using Whole Genome Tiling Arrays and Next Generation Sequencing

Francisella tularensis is classified as a Class A bioterrorism agent by the U.S. government due to its high virulence and the ease with which it can be spread as an aerosol. It is a facultative intracellular pathogen and the causative agent of tularemia. Ciprofloxacin (Cipro) is a broad spectrum antibiotic effective against Gram-positive and Gram-negative bacteria. Increased Cipro resistance in pathogenic microbes is of serious concern when considering options for medical treatment of bacterial infections. Identification of genes and loci that are associated with Ciprofloxacin resistance will help advance the understanding of resistance mechanisms and may, in the future, provide better treatment options for patients. It may also provide information for development of assays that can rapidly identify Cipro-resistant isolates of this pathogen. In this study, we then selected a large number of F. tularensis live vaccine strain (LVS) isolates that survived in progressively higher Ciprofloxacin concentrations, screened the isolates using a whole genome F. tularensis LVS tiling microarray and Illumina sequencing, and identified both known and novel mutations associated with resistance. For genes containing mutations encode DNA gyrase subunit A, a hypothetical protein, an asparagine synthase, a sugar transamine/perosamine synthetase and others. Finally, structural modeling performed on these proteins provides insights into the potential function of these proteins and how they might contribute to Cipro resistance mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗