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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 OSTI2016

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗
DOE OSTI2017

Detection Identification and Quantification of Keto-Hydroperoxides in Low-Temperature Oxidation.

Keto-hydroperoxides are reactive partially oxidized intermediates that play a central role in chain-branching reactions during the low-temperature oxidation of hydrocarbons. In this Perspective, we outline how these short lived species can be detected, identified, and quantified using integrated experimental and theoretical approaches. The procedures are based on direct molecular-beam sampling from reactive environments, followed by mass spectrometry with single-photon ionization, identification of fragmentation patterns, and theoretical calculations of ionization thresholds, fragment appearance energies, and photoionization cross sections. Using the oxidation of neo-pentane and tetrahydrofuran as examples, the individual steps of the experimental approaches are described in depth together with a detailed description of the theoretical efforts. For neo-pentane, the experimental data are consistent with the calculated ionization and fragment appearance energies of the keto-hydroperoxide, thus adding confidence to the analysis routines and the employed levels of theory. For tetrahydrofuran, multiple keto-hydroperoxide isomers are possible due to the presence of nonequivalent O 2 addition sites. Despite this additional complexity, the experimental data allow for the identification of two to four keto-hydroperoxides. Mole fraction profiles of the keto-hydroperoxides, which are quantified using calculated photoionization cross sections, are provided together with estimated uncertainties as function of the temperature of the reactive mixture and can serve as validation targets for chemically detailed mechanisms.

Hansen, Nils↗
DOE OSTI2018

Pixelated CZT Technology for the Detection, Identification, and Localization of Gamma-Ray Sources - 18311

Radioactive waste-management applications require good measurements to qualitatively and quantitatively characterize waste materials. Often these measurements must be done in situ, and the user wishes to learn something of the isotopic and spatial distribution of the source. Therefore, there is a need for a portable instrument that can map radioactive contamination and quantify sources. This presentation introduces several instruments that have been applied to this task, along with example measurements in relevant applications. All instruments are based on 3D-position-sensitive CdZnTe (CZT) detectors. This technology routinely achieves better than 1.1% FWHM energy resolution at 662 keV and operates at room temperature with a startup time under 2 minutes. All are packaged in portable enclosures with WiFi and Bluetooth capabilities for wireless communication. These capabilities allow the technology to be used in more advanced measurements in the field. A unique capability of these detectors is the ability to image the spatial distribution of gamma rays from a single stationary measurement. The first product, Polaris-H, has been in use at commercial nuclear power plants since 2013. The system, currently used at over 50% of the US nuclear-power fleet, integrates a 3D-position-sensitive CZT detector, associated readout electronics, an embedded computer, a battery with over 7-hour life, an optical camera, and a laser range finder in a portable water-proof enclosure. The system connects wirelessly to a tablet which displays a gamma-ray spectrum and isotope-specific images of the gamma-ray distribution in all directions in real time. The CZT detectors have a dynamic range from 50 keV up to 3 MeV. Coded aperture or pinhole imaging is used for energies below 250 keV, and Compton imaging is used for energies above 250 keV. List-mode data is saved to an external USB memory stick to enable more advanced analysis, including isotopic activity analysis, via H3D's post-processing software. Additional systems based on the same architecture have been developed to optimize the technology for other applications. These include Polaris-H Quad, which provides over 3 times the sensitivity for applications involving weaker sources, and Polaris-H Shield, which uses a collimator to provide images and isotope information over a narrower field of view in order to measure weaker sources in the presence of stronger sources of the same isotope. H3D has also developed custom systems that have been mounted on robots and drones that enable remote operation of the technology. Results from the measurements collected at commercial nuclear power plants and other applicable sites will be presented to show how these instruments can be used to detect, identify, and localize gamma-ray sources. Advanced analysis techniques to show the accurate quantification of the gamma-ray source will also be presented. Applications range from characterizing simple objects, to locating hottest points, estimating contamination distributions on an isotope-by-isotope basis, and differentiating scatter and direct emissions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Radiological Anomaly Detection And Identification (RADAI) v1.0

The Radiological Anomaly Detection and Identification (RADAI) software package is a python library for implementing, training, and storing algorithms that detect and identify anomalies in gamma-ray spectra. The library defines a general framework for implementing detection (binary) and identification (classification) algorithms, objects to encapsulate the results of analyses, a variety of temporal filtering tools that can be used in constructing algorithms, and conceptual design that allows easy reading and writing of algorithms (and their time dependent state). In addition to this framework, the library includes implementations of a variety of algorithms from the scientific literature including: gross-counts k-sigma, SPRT, N-SCRAD, Region of Interest, and Censored Energy Window. The implementation of these algorithms within the RADAI package was done to facilitate user-initiated training and configuration to by applied to different gamma-ray detector types. Finally, benchmarked and synthetic datasets will be made available for standardized algorithm characterization with corresponding utilities in the RADAI package for data access and processing.

Joshi, Tenzing↗
DOE OSTI2023

Computationally Robust Line Outage Detection and Identification in Three-Phase Networks

Detection and identification of individual phase outages remains a challenging problem due to insufficient metering in three-phase unbalanced power networks. This problem was tackled for the transmission systems in our previous work as documented in [1]. In this paper, this work is extended to detect phase outages in three-phase unbalanced systems using only the sparse estimation method. In addition, further improvements are introduced to increase the estimation accuracy for the virtual power injections at the terminal buses of disconnected lines, once the disconnected line is identified by sparse estimation methods. Simulation results are provided to experimentally validate the increased accuracy in detecting phase outages while decreasing the computational time by using the proposed approach.

Sparse Estimation, Line Outage, Phase Outage, LASS↗
DOE OSTI2014

Device for detection and identification of carbon- and nitrogen-containing materials

A device for detection and identification of carbon- and nitrogen-containing materials is described. In particular, the device performs the detection and identification of carbon- and nitrogen-containing materials by photo-nuclear detection. The device may comprise a race-track microtron, a breaking target, and a water-filled Cherenkov radiation counter.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach

Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Anomaly Detection and Identification Using a Leave-One-Variable-Out Method

At nuclear power plants (NPPs), anomaly detection and identification (i.e., determining the causes of anomalies) are important tasks for ensuring the safe and efficient operation of NPPs. These tasks are currently labor-intensive and costly, and are made more difficult by the size and complexity of NPP systems. An alternative approach to conducting these tasks is to automate them, such as via the reconstruction-based contribution method, which is a well-researched unsupervised machine learning method that uses a data-driven model of anomaly-free behavior to detect events and then identify each variable’s contributions to those events. The present effort developed a novel contribution approach that utilized a leave-one-variable-out (LOVO) model, with which each variable is predicted using all the other variables. The novelty lay in transforming this model into a reconstruction model and modifying the identification algorithm to work with the new reconstruction model. To evaluate this method in a controlled environment, a synthetic dataset based on spring-mass-damper (SMD) systems (commonly found in mechanical engineering references) was used, with known anomalies introduced into the system. The proposed method successfully detected the anomalies and afforded insights into their causes, thus enabling the appropriate identifications to be made.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SU-E-T-261: Development of An Automated System to Detect Patient Identification and Positioning Errors Prior to Radiotherapy Treatment

Purpose: To develop a system that can automatically detect patient identification and positioning errors using 3D computed tomography (CT) setup images and kilovoltage CT (kVCT) planning images. Methods: Planning kVCT images were collected for head-and-neck (H&N), pelvis, and spine treatments with corresponding 3D cone-beam CT (CBCT) and megavoltage CT (MVCT) setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. Positioning errors were simulated by misaligning the setup image by 1cm to 5cm in the six anatomical directions for H&N and pelvis patients. Misalignments for spine treatments were simulated by registering the setup image to adjacent vertebral bodies on the planning kVCT. A body contour of the setup image was used as an initial mask for image comparison. Images were pre-processed by image filtering and air voxel thresholding, and image pairs were assessed using commonly-used image similarity metrics as well as custom -designed metrics. A linear discriminant analysis classifier was trained and tested on the datasets, and misclassification error (MCE), sensitivity, and specificity estimates were generated using 10-fold cross validation. Results: Our workflow produced MCE estimates of 0.7%, 1.7%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivities and specificities ranged from 98.0% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 96.2% and 98.4%. MCEs for 1cm H&N/pelvis misalignments were 1.3/5.1% and 9.1/8.6% for TomoTherapy and TrueBeam images, respectively. 2cm MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. Vertebral misalignment MCEs were 4.8% and 4.9% for TomoTherapy and TrueBeam images, respectively. Conclusion: Patient identification and gross misalignment errors can be robustly and automatically detected using 3D setup images of two imaging modalities across three commonly-treated anatomical sites.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Chip-based device for parallel sorting, amplification, detection, and identification of nucleic acid subsequences

An apparatus for chip-based sorting, amplification, detection, and identification of a sample having a planar substrate. The planar substrate is divided into cells. The cells are arranged on the planar substrate in rows and columns. Electrodes are located in the cells. A micro-reactor maker produces micro-reactors containing the sample. The micro-reactor maker is positioned to deliver the micro-reactors to the planar substrate. A microprocessor is connected to the electrodes for manipulating the micro-reactors on the planar substrate. A detector is positioned to interrogate the sample contained in the micro-reactors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Carbene-based Difluoromethylation of Bisphenols: Application to the Instantaneous Tagging of Bisphenol A in Spiked Soil for Its Detection and Identification by Electron Ionization Gas Chromatography-Mass Spectrometry

The rapid and efficient difluoromethylation of a panel of eleven bisphenols (BPs) for their enhanced detection and identification by Electron-Ionization Gas Chromatography-Mass Spectrometry (EI-GC-MS) is presented. The derivatization employs the inexpensive, environmentally benign agent diethyl (bromodifluoromethyl) phosphonate (DBDFP) as a difluorocarbene-generating species that converts the BPs into bis-difluoromethylated ethers that can be detected and identified by GC-MS means. Key attributes of the protocol include its extreme rapidity (30 seconds) at ambient temperature, high specificity for BPs amidst other alcohol-containing analytes, and its biphasic nature that allows for its convenient adaptation to the analysis of BPs in organic as well as aqueous matrices. The protocol furnishes stable, novel BP ethers armed with a total of four fluorine atoms for their subsequent analysis by EI-GC-MS. Furthermore, each derivatized bisphenol exhibits unique retention times vastly different from their native counterparts leading to their unequivocal identification. The effectiveness and robustness of the developed methodology was applied to the tagging of the most famous member of this family of compounds, bisphenol-A (BPA), when spiked (at 1 μg.g –1 concentration) in the physically and compositionally complex Nebraska EPA standard soil. The method detection limit (MDL) for the bis-difluoromethylated BPA was determined to be 0.01 μg.mL –1 . The bis-difluoromethylated BPA was conveniently detected on the organic layers from the biphasic, derivatized mixtures, highlighting the protocol’s practicality and utility in the rapid, qualitative detection of this endocrine disruptor during environmental analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Effective methylation of phosphonic acids related to chemical warfare agents mediated by trimethyloxonium tetrafluoroborate for their qualitative detection and identification by gas chromatography-mass spectrometry

The effective methylation of phosphonic acids related to chemical warfare agents (CWAs) employing trimethyloxonium tetrafluoroborate (TMO·BF 4 ) for their qualitative detection and identification by gas chromatography-mass spectrometry (GC-MS) is presented. The methylation occurs in rapid fashion (1 h) and can be conveniently carried out at ambient temperature, thus providing a safer alternative to the universally employed diazomethane-based methylation protocols. Optimization of the methylation parameters led us to conclude that methylene chloride was the ideal solvent to carry out the derivatization, and that even though methylated products can be observed surfacing after only 1 h, additional time was not found to be detrimental but beneficial to the process particularly when dealing with analytes at low concentrations (~10 μg mL -1 ). Due to its insolubility in methylene chloride, TMO·BF 4 conveniently settles to the bottom during the reaction and does not produce additional interfering by-products that may further complicate the GC-MS analysis. We demonstrated the method to successfully methylate a variety of Schedule 2 phosphonic acids, including their half esters, resulting in derivatives that were readily detected and identified using the instrument's spectral library. Most importantly, the method was shown to simultaneously methylate a mixture of the organophosphorus-based nerve agent hydrolysis products: pinacolyl methylphosphonate (PMPA), cyclohexyl methylphosphonate (CyMPA) and ethyl methylphosphonate (EMPA) (at a 10 μg mL - 1 concentration each) in a fatty acid ester-rich organic matrix (OPCW-PT-O 3 ) featured in the 38th Organisation for the Prohibition of Chemical Weapons (OPCW) Proficiency Test. Additionally, the protocol was found to effectively methylate N,N-diethylamino ethanesulfonic acid and N,N-diisopropylamino ethanesulfonic acid that are products arising from the oxidative degradation of the V-series agents VR and VX respectively. This work described herein represents the first report on the use of TMO·BF 4 as a viable, stable and safe agent for the methylation of phosphonic acids and their half esters and within the context of an OPCW Proficiency Test sample analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multiplex detection and identification of viral, bacterial, and protozoan pathogens in human blood and plasma using an expanded high-density resequencing microarray platform

Introduction: Nucleic acid tests for blood donor screening have improved the safety of the blood supply; however, increasing numbers of emerging pathogen tests are burdensome. Multiplex testing platforms are a potential solution. Methods: The Blood Borne Pathogen Resequencing Microarray Expanded (BBP-RMAv.2) can perform multiplex detection and identification of 80 viruses, bacteria and parasites. This study evaluated pathogen detection in human blood or plasma. Samples spiked with selected pathogens, each with one of 6 viruses, 2 bacteria and 5 protozoans were tested on this platform. The nucleic acids were extracted, amplified using multiplexed sets of primers, and hybridized to a microarray. The reported sequences were aligned to a database to identify the pathogen. To directly compare the microarray to an emerging molecular approach, the amplified nucleic acids were also submitted to nanopore next generation sequencing (NGS). Results: The BBP-RMAv.2 detected viral pathogens at a concentration as low as 100 copies/ml and a range of concentrations from 1,000 to 100,000 copies/ml for all the spiked pathogens. Coded specimens were identified correctly demonstrating the effectiveness of the platform. The nanopore sequencing correctly identified most samples and the results of the two platforms were compared. Discussion: These results indicated that the BBP-RMAv.2 could be employed for multiplex detection with potential for use in blood safety or disease diagnosis. The NGS was nearly as effective at identifying pathogens in blood and performed better than BBP-RMAv.2 at identifying pathogen-negative samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Efficient derivatization of methylphosphonic and aminoethylsulfonic acids related to nerve agents simultaneously in soils using trimethyloxonium tetrafluoroborate for their enhanced, qualitative detection and identification by EI-GC–MS and GC–FPD

Trimethyloxonium tetrafluoroborate (TMO·BF 4 ) has been used here in the simultaneous derivatization of phosphonic and 2-aminoethylsulfonic acids related to nerve agents in different soils for their enhanced detection and identification by electron ionization gas chromatography-mass spectrometry (EI-GC–MS). The panel of acids consisted of five Schedule 2 phosphonic acids (methyl methylphosphonic acid, ethyl methylphosphonic acid, isopropyl methylphosphonic acid, pinacolyl methylphosphonic acid and cyclohexyl methylphosphonic acid) along with two sulfonic acids, N,N-diethyl-2-aminoethylsulfonic acid and N,N-diisopropyl-2-aminoethylsulfonic acid. The acids were converted to their corresponding methyl esters at ambient temperature when present at a 10 μg g -1 concentration in three separate soils: Virginia type A soil, Ottawa sand and Nebraska EPA soil. The concentration of the acids reflects values typically encountered during proficiency tests (PTs) administered annually by the Organisation for the Prohibition of Chemical Weapons (OPCW). Derivatization times to yield detectable signals for the methyl ester products for all the acids was found to vary among all three soil samples, however, it was found that generally the most optimal time across all the matrices involved was 3 h after the addition of TMO·BF 4 . Concomitantly, the analysis of the samples was complemented using GC coupled to flame photometric detection (GC–FPD). The inclusion of GC–FPD in the analysis yielded stronger signals for all seven methylated analytes making their detection after merely 3 h possible relative to the ones initially obtained with EI-GC–MS. Regarding the three soils employed in our study, a greater methylating efficiency was found in the Virginia type A soil and Ottawa sand yielding results that were significantly larger in magnitude to those found during the same time points for the Nebraska EPA soil sample. Prolonged reaction times (up to 72 h) were explored to find the time for the highest yield of methyl ester production were found instead to be deleterious to the process showcasing the importance of the fast yielding nature of the process specifically in situations where time-sensitive analysis is crucial (e.g. OPCW-PT).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗