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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Results of In-Depth Analysis of Data Obtained in the Experimental Search for 2K(2ν)-Capture in {sup 78}Kr

A brief description of the results of joint analysis of data obtained in long-term measurements of krypton samples differing in {sup 78}Kr content is presented. Low-background high-pressure proportional gas counters were used as detectors. The comparative analysis of experimental data on single and double K -capture provided the first estimate of the probability of production of a double K -shell vacancy in the process of K -shell electron capture in {sup 81}Kr: P{sub KK}=[5.7±0.8 (stat.)±0.4 (syst.)]×10{sup −5}. A new result for the half-life of {sup 78}Kr with respect to the 2K(2ν)-mode was also obtained: T{sub 1/2}=[1.9{sub −0.7}{sup +1.3} (stat.)±0.3 (syst.)]×10{sup 22} years (90% CL).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

RAVEN Beta Release

This documents the release of the Risk Analysis Virtual Environment (RAVEN) code. A description of the RAVEN code is provided, and discussion of the release process for the M2LW-16IN0704045 milestone. The RAVEN code is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN has now increased in maturity enough for the Beta 1.0 release.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

2D Graphene Sheets as a Sensing Material for the Electroanalysis of Zileuton

Zileuton (ZLT) is an active oral inhibitor of enzyme 5-lipoxygenase, and long-term intake and overdose of ZLT cause adverse effects, leading to critical conditions in patients. This is a well-recognized issue that necessitates a better approach for ZLT sensing. Given the increasing interest in ZLT sensing and the limitations of previous techniques, there is a need for a highly sensitive, robust, and fast operation method that is inexpensive and easy to use. Thus, for the sensitive detection and determination of ZLT, an electrochemical sensor based on graphene was fabricated. Graphene has excellent properties, such as high surface area, low toxicity, conductivity, and electroactive conjugation with biomolecules, making it suitable for sensing. The electrocatalytic property of graphene promotes the redox-coupled reaction of ZLT. Electrochemical investigation of the modifier was carried out by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). An optimization and analysis of the influence of different parameters on the electrochemical behavior of ZLT were carried out using the CV approach. The scan rate study aided in exploring the physicochemical properties of the electrode process, and two electrons with two protons were found to be involved in the electrooxidation of ZLT. The fabricated sensor showed a wide range of linearity with ZLT, from 0.3 µM to 100.0 µM, and the detection limit was evaluated as 0.03 µM under optimized conditions. The analysis of spiked urine samples, with good recovery values for percent RSD, provided support for the efficiency and applicability of the developed electrode.

2D Material↗
DOE OSTI2012

Safeguards Verification Measurements using Laser Ablation, Absorbance Ratio Spectrometry in Gaseous Centrifuge Enrichment Plants

Laser Ablation Absorbance Ratio Spectrometry (LAARS) is a new verification measurement technology under development at the US Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL). LAARS uses three lasers to ablate and then measure the relative isotopic abundance of uranium compounds. An ablation laser is tightly focused on uranium-bearing solids producing a small plume containing uranium atoms. Two collinear wavelength-tuned spectrometry lasers transit through the plume and the absorbance of U-235 and U-238 isotopes are measured to determine U-235 enrichment. The measurement has high relative precision and detection limits approaching the femtogram range for uranium. It is independent of chemical form and degree of dilution with nuisance dust and other materials. High speed sample scanning and pinpoint characterization allow measurements on millions of particles/hour to detect and analyze the enrichment of trace uranium in samples. The spectrometer is assembled using commercially available components at comparatively low cost, and features a compact and low power design. Future designs can be engineered for reliable, autonomous deployment within an industrial plant environment. Two specific applications of the spectrometer are under development: 1) automated unattended aerosol sampling and analysis and 2) on-site small sample destructive assay measurement. The two applications propose game-changing technological advances in gaseous centrifuge enrichment plant (GCEP) safeguards verification. The aerosol measurement instrument, LAARS-environmental sampling (ES), collects aerosol particles from the plant environment in a purpose-built rotating drum impactor and then uses LAARS-ES to quickly scan the surface of the impactor to measure the enrichments of the captured particles. The current approach to plant misuse detection involves swipe sampling and offsite analysis. Though this approach is very robust it generally requires several months to obtain results from a given sample collection. The destructive assay instrument, LAARS-destructive assay (DA), uses a simple purpose-built fixture with a sampling planchet to collect adsorbed UF6 gas from a cylinder valve or from a process line tap or pigtail. A portable LAARS-DA instrument scans the microgram quantity of uranium collected on the planchet and the assay of the uranium is measured to ~0.15% relative precision. Currently, destructive assay samples for bias defect measurements are collected in small sample cylinders for offsite mass spectrometry measurement.

Enrichment Monitoring↗
DOE OSTI2016

Refining Groundwater Monitoring Approaches Through an Objectives-Driven Process - 16333

Groundwater monitoring is conducted throughout the groundwater remediation process, from initial site characterization through demonstration of cleanup objectives attainment. The DOE uses the groundwater monitoring data to define plumes, manage ongoing remediation, and determine how close the contamination levels are to meeting an endpoint criteria, such as a drinking water standard. Due to the expense of groundwater sampling and analysis, the DOE Office of Environmental Management compiled a synopsis of monitoring technologies and approaches, and defined opportunities for improving environmental remediation monitoring. This information included techniques to optimize and streamline groundwater monitoring. At the Hanford Site, years of extensive groundwater monitoring have been applied to support characterization and remedy-selection activities. Continuation of this monitoring intensity would be costly and would need to be reviewed in the context of future activities for groundwater remedy implementation. A structured review of monitoring plans is underway and has identified significant potential changes to the monitoring approach. These changes have been developed based on careful definition of monitoring objectives and consideration of innovative and targeted techniques to gather the appropriate data for remedy performance monitoring and transition to longer-term monitoring and closure. (authors)

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

3-D Radiological Data Acquisition, Visualization and Modeling - 20211

The U.S. Army Corps of Engineers (USACE) was tasked to investigate and remediate low activity radiological contamination from research and production of the nation's first nuclear weapons at the former DuPont Chambers Works Formerly Utilized Sites Remedial Action Program (FUSRAP) site (DuPont). The DuPont site had several buildings used for the Manhattan project that were demolished in the 1940's and 1950's apparently using heavy earthmoving equipment. Some of the contaminated rubble from the demolition appears to have been spread out by this equipment resulting in somewhat random scattering of radiologically contaminated soil and debris along with aqueous spills. Traditional investigative methods such as soil borings, test pits and 2-dimensional gamma walkovers were only partially successful in delineating the radiological contamination at the site. It was feared that even 'chasing' the contamination during remediation would miss contamination if the demolition resulted in discontinuous trails of radiologically contaminated soils. In evaluating the data generated over the interceding decades, the USACE determined that a better method to collect and process the remedial action radiological data was needed to enable the project team to optimize predictive planning and meet documentation expectations. The purpose of this paper is to provide an overview of the effort and progress to combine and organize radiological survey methods into a highly flexible sampling, modeling, and decision analysis approach that emphasizes the quality of decision-making during remediation. This innovative system blends multiple tools to develop a methodology that can extend MARRSIM [1] into the subsurface and provide tools that can be applied to other sites. (authors)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

3013 DE Inner Container Closure Weld Corrosion Evaluation

Destructive evaluation (DE) of 3013 containers is one part of the U. S. Department of Energy Integrated Surveillance Program. During standard DE of 3013 containers, visual examinations for pitting and stress corrosion cracking (SCC) are performed on the accessible surfaces of the outer, inner, and convenience containers, which make up the 3013 container. As a result of 3013 DE additional analysis, the area near the inner container closure weld has been identified as being a region of increased corrosion susceptibility, which may provide a pathway for corrosive gases to the outer container. This area has a higher residual stress, an altered microstructure, and less corrosion resistant weld oxides as a result of the welding process as well as a lower temperature than other areas of the container, which may increase the absorption of moisture on the surface. The deposition of moisture in this stressed region could lead to pitting and stress corrosion cracking. During FY2013, the inner container closure weld area was more closely evaluated on several archived samples from DE containers. These containers included FY09 DE2, FY12 DE4, FY12 DE6 and FY12 DE7 and the Hanford High Moisture Container. The additional examinations included visual observations with a stereomicroscope, scanning electron microscopy along with energy dispersive spectroscopy for chemical analysis, and serial metallography of the sidewall and lid that are part of the inner container closure weld region. Pitting was observed in all the samples taken from the closure weld regions of the examined inner containers. This pitting was generally less 20 µm with most less than 5µm. These pits were similar in depth to those observed in the vapor exposed surfaces of teardrops in the shelf life corrosion testing. Cracking was not observed on either the vapor-exposed surfaces of the teardrop coupons or the inner container closure weld region. Further testing is necessary to determine if the conditions in the welded inner container could support SCC during the 50 year life time for the 3013 container.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Sustainable network design for multi-purpose pellet processing depots under biomass supply uncertainty

This work develops a two-stage stochastic mixed-integer programming model to manage multi- purpose pellet processing depots under feedstock supply uncertainty. The proposed optimization model would help to minimize cost and to mitigate emissions from the supply chain network. We consider three alternative Biomass Processing and Densi cation Depot (BPDD) technologies; namely, conventional pellet processing, high moisture pellet processing, and ammonia ber expansion. These three technologies pre-process/pre-treat and densify different types of biomass into more highly densi ed intermediate products for different markets in order to improve movability and overall supply network performance in terms of costs and emissions. A hybrid decomposition algorithm was developed that combines sample average approximation with an enhanced Progressive Hedging (PH) algorithm to solve this challenging NP -hard problem. Some heuristics such as Rolling Horizon (RH) heuristic, variable xing technique were later applied to further enhance the PH algorithm. Mississippi and Alabama were selected as a testing ground and ArcGIS was employed to visualize and validate the modeling results. The results of the analysis reveal promising insights that could lead to recommendations to help decision makers achieve a more cost-effective environmentally-friendly supply chain network.

09 BIOMASS FUELS↗
DOE OSTI2026

Rapid Analytical Methodology for Chloride Molten Salt Reactor Safeguards and Process Monitoring

Technologies that enable near real-time isotopic analysis of advanced molten salt reactor (MSR) fuels are critically needed to safeguard these reactors, increase their operational efficiency, and enable their widespread deployment with confidence. We present a systematic approach to developing near real-time dissolution and chemical isolation of U, Pu, and major fission products from highly radioactive chloride molten salt samples. Chemical yields greater than 95% were observed for both uranium and the lanthanides. Interference reduction enabled the detection and quantification of key diagnostic isotopes (including 112Ag, 147Nd, and 153Sm) that were previously undetectable in the original sample. The results from this initial scoping study lay the foundation for the development of future automated systems that can enable cost-efficient, near-real time chemical separation and analysis of extremely highly radioactive molten salt samples.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2022

Analysis of Inlier and Outlier Compounds with Respect to Artificial Neural Network Cetane Number Prediction Accuracy

Artificial neural networks (ANNs) are exceptional at forming non-linear correlations between multivariate input and target variables; however, they are often seen as a “black box” approach, since how ANNs form these correlations is somewhat ambiguous. Furthermore, the process underlying how ANNs learn from inlier and outlier samples within the input dataset is not fully understood. Intuitively, it is expected that training ANNs with inlier samples will increase prediction accuracy and training with outlier samples will reduce prediction accuracy; though, in practice, this is not always true. The present work identifies and analyzes inliers and outliers of existing experimental cetane number (CN) data encompassing a variety of compounds and compound groups. It also investigates how ANNs trained to predict CN perform with and without outliers included in the training data, and whether a relationship exists between inliers/outliers and ANN prediction accuracy across the whole dataset and for individual samples. Additionally, individual outlier compounds are analyzed, highlighting how they structurally differ from inlier compounds.

09 BIOMASS FUELS↗
DOE OSTI2014

Micro-Spectroscopic Imaging and Characterization of Individually Identified Ice Nucleating Particles from a Case Field Study

The effect of anthropogenic and biogenic organic particles on atmospheric glaciation processes is poorly understood. We use an optical microscopy (OM) setup to identify the location of ice nuclei (IN) active in immersion freezing and deposition ice nucleation for temperatures of 200-273 K within a large population of particles sampled from an ambient environment. Applying multi-modal micro-spectroscopy methods we characterize the physicochemical properties of individual IN in particle populations collected in central California. Chemical composition and mixing state analysis of particle populations are performed to identify characteristic particle-type classes. All particle-types contained organic material. Particles in these samples take up water at subsaturated conditions, induce immersion freezing at subsaturated and saturated conditions above 226 K, and act as deposition IN below 226 K. The identified IN belong to the most common particle-type classes observed in the field samples: organic coated sea salt, Na-rich, and secondary and refractory carbonaceous particles. Based on these observations, we suggest that the IN are not always particles with unique chemical composition and exceptional ice nucleation propensity; rather, they are common particles in the ambient particle population. Thus, particle composition and morphology alone are insufficient to assess their potential to act as IN. The results suggest that particle-type abundance is also a crucial factor in determining the ice nucleation efficiency of specific IN types. These findings emphasize that ubiquitous organic particles can induce ice nucleation under atmospherically relevant conditions and that they may play an important role in atmospheric glaciation processes.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2022

An approach to nearest neighbor analysis of pigment-protein complexes using chemical cross-linking in combination with mass spectrometry

Herein, protein cross-linking is the process of chemically joining two amino acids in a protein or protein complex by a covalent bond. When combined with mass spectrometry, it becomes one of the structural mass spectrometry techniques gaining in importance for deriving valuable three-dimensional structural information on proteins and protein complexes. This platform complements existing structural methods, such as NMR spectroscopy, X-ray crystallography, and cryo-EM. Photosynthetic pigment protein complexes serve as light-energy harvesting systems and perform photochemical conversion as part of the “early events” of photosynthesis. This chapter outlines how to prepare cross-linking pigment protein complex samples for LC-MS/MS analysis, including identification of the cross-linked species, network analysis in a protein complex, and structural modeling and justification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Atmospheric Solid Analysis Probe Coupled to Ion Mobility Spectrometry-Mass Spectrometry, a Fast and Simple Method for Polyalphaolefin Characterization

Polyalphaolefins (PAOs) are polymers produced from linear alpha olefins through catalytic oligomerization processes. The PAOs are known as synthetic high-performance base stock fluids used to improve the efficiency of many other synthetic products. In this study, we report the direct characterization of PAOs using atmospheric solid analysis probe (ASAP) coupled with ion mobility spectrometry-mass spectrometry (IMS-MS). We studied different PAOs grades exhibiting low- and high-viscosity index. Specific adjustments of the ASAP source parameters permitted the monitoring of ionization processes as three mechanisms could occur for these compounds: hydride abstraction, nitrogen addition, and/or the formation of [M−2H]{sup +•} ions. Several series of fragment ions were obtained, which allowed the identification of the alpha olefin used to synthesize the PAO. The use of the ion mobility separation dimension provides information on isomeric species. In addition, the drift time versus m/z plots permitted rapid comparison between PAO samples and to evidence their complexity. These 2D plots appear as fingerprints of PAO samples. To conclude, the resort to ASAP-IMS-MS provides a rapid characterization of the PAO samples in a direct analysis approach, without any sample preparation. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Sidewall passivation layer thickness and composition profiles of etched silicon patterns from angle resolved x-ray photoelectron spectroscopy analysis

In this study, we present a technique to analyze side wall passivation layers formed on silicon sidewalls after plasma processing. The thickness and chemical composition are derived from angle resolved x-ray photoelectron spectroscopy analyses. It is a non-destructive, quasi in situ method to determine profiles of the thickness and the chemical composition of passivation layers in trenches up to an aspect ratio of about 3. The performance of this technique to quantify the passivation layer thickness is compared to a standard technique using secondary electron microscopy images with respect to two different samples and is found to be at least equivalent. The possible uncertainties and limitations of this technique are discussed as well.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview

Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.

Biofuels↗
DOE OSTI2024

Machine learning framework for predicting uranium enrichments from M400 CZT gamma spectra

A machine learning framework was developed for predicting uranium enrichments from M400 CZT gamma spectra. This framework leverages the availability of a large amount of measured M400 gamma spectra and uses a recently updated version of Gamma Detector Response and Analysis Software (GADRAS) for gamma spectrum analysis and generation. It also leverages the existing machine learning modules in Python for gamma spectrum data processing, curation, model training, benchmarking, and optimization of the deep machine learning models. The framework is used to develop a deep learning model to analyze gamma spectra from a set of U 3 O 8 samples with enrichments ranging from 0.31 to 93.17% and UF 6 cylinders with enrichments ranging from 0.2 to 4.95%, and the model performance is tested using a set of measured spectra and the respective declared enrichment values. Results show that the model can correctly classify 99.35% of the U 3 O 8 sample enrichments, and can predict the samples’ enrichments within an average absolute error of 0.099% (in percentage points of enrichment). For the UF 6 cylinders, the average absolute error was approximately 0.03%, with an accuracy of 98% in classifying discrete enrichment values of UF 6 samples. Finally, the results also show that the model has performed significantly better in terms of predicting enrichments in UF 6 cylinders based on measured gamma spectra than the GEM code, with a standard deviation (of the relative errors) of 2.23% (compared with the 11.51% value for the GEM code) based on results from a set of test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2016

Gradient elution moving boundary electrophoresis enables rapid analysis of acids in complex biomass-derived streams

Biomass conversion processes such as pretreatment, liquefaction, and pyrolysis often produce complex mixtures of intermediates that are a substantial challenge to analyze rapidly and reliably. To characterize these streams more comprehensively and efficiently, new techniques are needed to track species through biomass deconstruction and conversion processes. Here, we present the application of an emerging analytical method, gradient elution moving boundary electrophoresis (GEMBE), to quantify a suite of acids in a complex, biomass-derived streams from alkaline pretreatment of corn stover. GEMBE offers distinct advantages over common chromatography-spectrometry analytical approaches in terms of analysis time, sample preparation requirements, and cost of equipment. As demonstrated here, GEMBE is able to track 17 distinct compounds (oxalate, formate, succinate, malate, acetate, glycolate, protocatechuate, 3-hydroxypropanoate, lactate, glycerate, 2-hydroxybutanoate, 4-hydroxybenzoate, vanillate, p-coumarate, ferulate, sinapate, and acetovanillone). The lower limit of detection was compound dependent and ranged between 0.9 and 3.5 umol/L. Results from GEMBE were similar to recent results from an orthogonal method based on GCxGC-TOF/MS. Altogether, GEMBE offers a rapid, robust approach to analyze complex biomass-derived samples, and given the ease and convenience of deployment, may offer an analytical solution for online tracking of multiple types of biomass streams.

09 BIOMASS FUELS↗