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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated Hybrid Variance Reduction on Advanced Architectures in the Shift Monte Carlo Code

Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.

Denovo↗
DOE OSTI2023

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.

16S rRNA↗
DOE OSTI2017

Streamlining and Transparently Automating Complex Waste Determinations at Los Alamos National Laboratory - 17286

Environmental programs at Los Alamos National Laboratory (LANL) generate a large amount of sampling activities that can result in waste that needs to be characterized and properly disposed. Since 2011, sampling results are managed in the cloud-based Environmental Information Management (EIM) database, which contains over 16 million current and historical data records. In many cases, the database's analytical records are used to characterize and dispose waste from environmental operations. Three factors contribute to streamlining of the waste disposition operations: (1) if possible, use of the existing analytical data for waste characterization to avoid additional laboratory analysis; (2) reduction in time spent on data evaluation for various waste acceptance criteria; and (3) choice of the proper and least expensive disposal alternative. Use of the existing analytical data, when allowed by law, creates some paradoxical problems: when chemical analysis is requested with the purpose of characterizing waste, it creates only 100 to 400 analytical records. However, use of exiting analytical data at LANL may lead to evaluation of tens of thousands of analytical records, with many records for each single analyte. This occurs because data were collected for environmental characterization and not waste characterization. To streamline data analysis for waste disposition using existing data, LANL has created a Waste Data Summary (WDS) module in the site's EIM database. Recognizing that waste determination is performed by groups outside the environmental program at LANL, the WDS allows for logically guided data selection for a specific waste determination (solids, liquids, radiological, etc.) and reduces potentially thousands of rows of data into simple formatted data reports that are easy to comprehend and process. This allows the waste evaluators to review data using multiple approaches to make their own determination on the most appropriate disposal method. There are four basic principles of WDS operation: (1) The highest detected analytical result is evaluated for the selected samples (or the lowest undetected result if there are no detected results); (2) All approved disposal pathways are evaluated; (3) Any failures of the Waste Acceptance Criteria are visibly highlighted to focus the attention of the evaluator; and (4) The decision of the disposal path is left to the trained waste evaluator (WDS does not choose or suggest any disposal paths). The summary results are presented as the set of short data reports. These reports can be divided into two groups: general comparison reports and disposal pathway specific forms. The general comparison forms show all Resource Conservation and Recovery Act (RCRA) analytes with pass/fail evaluation and with preference for toxicity characteristic leaching procedure (TCLP) results (or 20x rule) for solid samples, the potential RCRA hazard codes assignment for detected analytes, and all rad results. The detected chemical and radioactivity results are compared to the background values and to Soil Screening and Soil Action limits (SSL/SAL limits). Disposal pathway specific forms follow Waste Acceptance Criteria requirements for all disposal pathways available at LANL. The programming of the WDS tries to maximize existing data by analyte substitutions (like using gross alpha for necessary alpha emitting isotopes) or by making calculations when analytical results do not agree with legal requirements (like total chlordane in the RCRA regulations, which is most often analyzed and reported as individual isomers). All forms may be annotated by the waste evaluator to provide additional information to support decision making. Upon final decision of a disposal path, the complete dataset used in the evaluation and the WDS forms with annotations is 'frozen' as an un-editable PDF export to document the data used in the waste disposal determination. The streamlined process results in less work for waste evaluators and reduces overall labor and analytical costs. By comparison, a full review of a 10,000 record parent dataset may take a waste evaluator over a week to review, whereas with the WDS module, the task can be accomplished in less than one hour with clear, reproducible results. These results can be reviewed and checked at any time by quality assurance personnel, thus ensuring transparency in the decision making process. LANL believes this approach has broad applicability to other sites where waste determination is based on review of existing data collected for more generalized environmental purposes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Optimizing Cryo-Focused Pyrolysis GC/MS for Tracing Soil Organic Matter Across Diverse Ecosystems

The cycling of organic matter in terrestrial soils and sediments is central to a range of biogeochemical processes that regulate nutrient cycling, crop productivity, trace gas emissions, and contaminant transport. Pyrolysis-gas chromatography/mass spectrometry (py-GC/MS) is a powerful tool for characterizing bulk soil organic matter (SOM) at the molecular level. In this study, we used a cryo-focused py-GC/MS system to analyze soil samples from seven diverse ecosystems: vernal pool, prairie pothole, temperate forest, tropical forest, tundra, wildfire-affected boreal forest, and grassland. We addressed a key bottleneck in molecular-level SOM characterization by developing an automated data analysis pipeline to optimize py-GC/MS and complementary evolved gas analysis/mass spectrometry (EGA/MS) methods, incorporating advanced tools for peak deconvolution, developing a custom compound class library, and implementing fragmentation spectrum-based molecular networking for the first time. This improved workflow was applied to soil samples from all seven ecosystems, including multiple depths and density fractions. Our findings demonstrate that ecosystem type plays a dominant role in shaping compositional differences in SOM. We also identified trends in the source of SOM compounds (e.g., microbial vs plantderived) across soil depth and density fractions, which are critical for understanding persistence and turnover of SOM. Our molecular networking analysis indicated that although many compounds are widespread across ecosystems, others are restricted to specific environments, such as wetlands. This underscores the utility of molecular-level data in elucidating the complexity of SOM composition and the environmental drivers that shape it. Such molecular-level insights can deepen our knowledge of biogeochemical SOM cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California’s coastline, 30–40 days in the northwest and Midwest, and 100 days in California’s Central Valley.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

MetaHCR: a web-enabled metagenome data management system for hydrocarbon resources

The ever-increasing metagenomic data necessitate appropriate cataloguing in a way that facilitates the comparison and better contextualization of the underlying investigations. To this extent, information associated with the sequencing data as well as the original sample and the environment where it was obtained from is crucial. To date, there are not any publicly available repositories able to capture environmental metadata pertaining to hydrocarbon-rich environments. As such, contextualization and comparative analysis among sequencing datasets derived from these environments is to a certain degree hindered or cannot be fully evaluated. The metagenomics data management system for hydrocarbon resources (MetaHCRs) enables the capturing of marker gene and whole metagenome sequencing data as well as over 300 contextual attributes associated with samples, organisms, environments and geological properties, among others. Moreover, MetaHCR implements the Minimum Information about any Sequence–hydrocarbon resource specification from the Genomic Standards Consortium; it integrates a user-friendly web interface and relational database model, and it enables the generation of complex custom search. MetaHCR has been tested with 36 publicly available metagenomic studies, and its modular architecture can be easily customized for other types of environmental and metagenomics studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Background optimization of powder electron diffraction for implementation of the e-PDF technique and study of the local structure of iron oxide nanocrystals

The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as pair distribution function (PDF) (also known as reduced density function) analysis. The PDF profiles are derived from background-corrected powder electron diffraction patterns (the e-PDF technique). Due to the strong Coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron–specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least-square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Molecular phylogenetic analysis of the Paguristes tortugae Schmitt, 1933 complex and selected other Paguroidea (Crustacea: Decapoda: Anomura)

Morphological characters, as presently applied to describe members of the Paguristes tortugae Schmitt, 1933 species complex, appear to be of limited value in inferring phylogenetic relationships within the genus, and may have similarly misinformed understanding of relationships between members of this complex and those presently assigned to the related genera Areopaguristes Rahayu & McLaughlin, 2010 and Pseudopaguristes McLaughlin, 2002. Previously undocumented observations of similarities and differences in color patterns among populations additionally suggest genetic divergences within some species, or alternatively seem to support phylogenetic groupings of some species. In the present study, a Maximum Likelihood (ML) phylogenetic analysis was undertaken based on the H3, 12S mtDNA, and 16S mtDNA sequences of 148 individuals, primarily representatives of paguroid species from the western Atlantic. This molecular analysis supported a polyphyletic Diogenidae Ortmann, 1892, although incomplete taxonomic sampling among the genera of Diogenidae limits the utility of this finding for resolving family level relationships. Several hypotheses regarding the evolutionary relationships among hermit crab genera were refuted by the Kishino-Hasegawa (KH). ShimodairaHasegawa (SH) and Approximately Unbiased (AU) tree topology tests, among them the hypothesis that Areopaguristes is monophyletic. A lack of support for the monophyly of Areopaguristes calls into question the phylogenetic validity of gill number for the differentiation of Paguristes, Areopaguristes, and Pseudopaguristes. The study was inconclusive with regard to the relationships among these three genera, but previously unknown diversity within both Paguristes and Areopaguristes was demonstrated. Existence of an undescribed species confounded under the name Paguristes tortugae Schmitt, 1933 was supported by genetics, morphology, and coloration. A second undescribed species with remarkable similarity to Areopaguristes hummi Wass, 1955 was discovered based on genetics and coloration.

12S↗
DOE OSTI2025

Cosmological Constraints from Full-Scale Clustering and Galaxy-Galaxy Lensing with DESI DR1

We present constraints on cosmic structure growth from the analysis of galaxy clustering and galaxy-galaxy lensing with galaxies from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. We analyze four samples drawn from the Bright Galaxy Survey (BGS) and the Luminous Red Galaxy (LRG) target classes. Projected galaxy clustering measurements from DESI are supplemented with lensing measurements from the Dark Energy Survey (DES), the Kilo-Degree Survey (KiDS), and the Hyper Suprime-Cam (HSC) survey around the same targets. Our method relies on a simulation-based modeling framework using the AbacusSummit simulations and a complex halo occupation distribution model that incorporates assembly bias. We analyze scales down to $0.4 \, h^{-1} \, \mathrm{Mpc}$ for clustering and $2.5 \, h^{-1} \, \mathrm{Mpc}$ for lensing, leading to stringent constraints on $S_8 = σ_8 \sqrt{Ω_\mathrm{m} / 0.3}$ and $Ω_\mathrm{m}$ when fixing other cosmological parameters to those preferred by the CMB. We find $S_8 = 0.794 \pm 0.023$ and $Ω_\mathrm{m} = 0.295 \pm 0.012$ when using lensing measurements from DES and KiDS. Similarly, for HSC, we find $S_8 = 0.793 \pm 0.017$ and $Ω_\mathrm{m} = 0.303 \pm 0.010$ when assuming the best-fit photometric redshift offset suggested by the HSC collaboration. Overall, our results are in good agreement with other results in the literature while continuing to highlight the constraining power of non-linear scales.

Lange, Johannes U. [American U.] (ORCID:0000000224↗
DOE OSTI2023

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Single-particle chemical analysis reveals organic-rich detonation soot products

The detonation of high explosives produces a wide variety of particulate matter (PM) with distinct properties, not all of which are traditionally studied for chemical composition, formation processes, and forensic applications. We report particle-resolved measurements of Composition B detonation soot using soot particle aerosol mass spectrometry (SP-AMS), identifying carbonaceous species and metals not previously characterized on a single-particle basis. Results are combined with scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) to enhance source-dependent signatures. Black carbon, including graphitic carbon and detonation nanodiamonds, contributed 50.5-71.4% of PM 2.5 mass, while 22.5-43.4% was non-refractory organic carbon, a previously overlooked component that exhibited a complex and varying composition. Detonations were performed with and without PMMA confinement and under steady and overdriven conditions. Distinct particles enriched with polycyclic aromatic hydrocarbons (PAHs) were observed in experiments utilizing confinement, with quantities dependent on manufacturing method. SEM-EDS validated SP-AMS findings of metals internally mixed with carbonaceous species and extended the particle size range to 100 µm. This work makes detecting non-refractory organics using single-particle techniques more feasible for detonation forensics and understanding high-energy soot formation. While this analysis uses offline sample collection, SP-AMS could be deployed for in-situ measurements of detonation PM 2.5 transported in the atmosphere.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Mechanical properties as an indicator of interstitials in niobium for superconducting accelerator cavities

A preliminary investigation was conducted into the feasibility of using simple mechanical properties experiments to evaluate interstitial impurity uptake from processing environments. Two types of tests were examined: tensile tests and complex modulus measurements using a dynamic mechanical analyzer (DMA). For the tensile tests, samples were cut from a single crystal of niobium, with the same orientation, and then prepared following different procedures. Significant differences were observed during tensile tests, with yielding strength and strain-to-failure clearly related to interstitial uptake. When the strain rate was reduced by an order of magnitude, the strain-to-failure was reduced by 18 % indicating that interstitial hydrogen is responsible for this behavior. For the complex modulus measurement, polycrystalline samples from different locations of two different ingots were examined at a frequency of 1.0 Hz while the temperature was increased at the rate of 1.0 °C per minute. Anaelastic peaks were found for C, N, and O in all samples, but the lower limit of the system did not allow for detection of a peak for H. It is concluded that mechanical properties could be developed as a measurement tool to guide the development of processing methods for producing reduced interstitial content material, but additional research, and uncertainty analysis, is required for these tools to be reliable in this application.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Iterative Bragg peak removal on X-ray absorption spectra with automatic intensity correction

This study introduces a novel iterative Bragg peak removal with automatic intensity correction (IBR-AIC) methodology for X-ray absorption spectroscopy (XAS), specifically addressing the challenge of Bragg peak interference in the analysis of crystalline materials. The approach integrates experimental adjustments and sophisticated post-processing, including an iterative algorithm for robust calculation of the scaling factor of the absorption coefficients and efficient elimination of the Bragg peaks, a common obstacle in accurately interpreting XAS data, particularly in crystalline samples. The method was thoroughly evaluated on dilute catalysts and thin films, with fluorescence mode and large-angle rotation. The results underscore the technique's effectiveness, adaptability and substantial potential in improving the precision of XAS data analysis. While demonstrating significant promise, the method does have limitations related to signal-to-noise ratio sensitivity and the necessity for meticulous angle selection during experimentation. Overall, IBR-AIC represents a significant advancement in XAS, offering a pragmatic solution to Bragg peak contamination challenges, thereby expanding the applications of XAS in understanding complex materials under diverse experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Dispersal behaviour of Euwallacea nr. fornicatus (Coleoptera: Curculionidae: Scolytinae) in avocado groves and estimation of lure sampling range

Abstract Invasive ambrosia beetles in the Euwallacea nr. fornicatus species complex (Coleoptera: Curculionidae: Scolytinae) threaten avocado production in multiple countries. These pests are currently monitored with lures containing quercivorol; α‐copaene was also recently discovered to be an effective attractant. We investigated dispersal behaviour of female E . nr . fornicatus in two Florida avocado groves using mark–release–recapture experiments and laboratory flight mills. Additionally, we estimated the sampling range of quercivorol and α‐copaene lures deployed alone and in tandem. The two‐component lure recaptured 31.2% of marked beetles, which was significantly more than quercivorol (11.2%) or α‐copaene (8.8%) alone. Contour analysis indicated that wind speed and direction can influence the number of females that disperse, as well as the direction and distance of their flight. Two methods were used to estimate sampling range and both of them indicated comparable effective ranges for the three lure treatments, with 80% of marked beetles being recaptured within 30–35 m of the release point. Average total flight distance in laboratory tests was 81.0 m in 24 h, with a maximum distance recorded at 400 m. The combination of quercivorol and α‐copaene lures provides the best detection of pest E . nr. fornicatus in Florida, with a recommended trap spacing of approximately 30 m in surveillance programmes for this pest in avocado groves.

Owens, David↗
DOE OSTI2017

Regression with Small Data Sets: A Case Study using Code Surrogates in Additive Manufacturing

There has been an increasing interest in recent years in the mining of massive data sets whose sizes are measured in terabytes. While it is easy to collect such large data sets in some application domains, there are others where collecting even a single data point can be very expensive, so the resulting data sets have only tens or hundreds of samples. For example, when complex computer simulations are used to understand a scientific phenomenon, we want to run the simulation for many different values of the input parameters and analyze the resulting output. The data set relating the simulation inputs and outputs is typically quite small, especially when each run of the simulation is expensive. However, regression techniques can still be used on such data sets to build an inexpensive \surrogate" that could provide an approximate output for a given set of inputs. A good surrogate can be very useful in sensitivity analysis, uncertainty analysis, and in designing experiments. In this paper, we compare different regression techniques to determine how well they predict melt-pool characteristics in the problem domain of additive manufacturing. Our analysis indicates that some of the commonly used regression methods do perform quite well even on small data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

42 ENGINEERING↗
DOE OSTI2017

Laser-induced breakdown spectroscopy of light water reactor simulated used nuclear fuel: Main oxide phase

We show the analysis of light water reactor simulated used nuclear fuel using laser-induced breakdown spectroscopy (LIBS) is explored using a simplified version of the main oxide phase. The main oxide phase consists of the actinides, lanthanides, and zirconium. The purpose of this study is to develop a rapid, quantitative technique for measuring zirconium in a uranium dioxide matrix without the need to dissolve the material. A second set of materials including cerium oxide is also analyzed to determine precision and limit of detection (LOD) using LIBS in a complex matrix. Two types of samples are used in this study: binary and ternary oxide pellets. The ternary oxide, (U,Zr,Ce)O 2 pellets used in this study are a simplified version the main oxide phase of used nuclear fuel. The binary oxides, (U,Ce)O 2 and (U,Zr)O 2 are also examined to determine spectral emission lines for Ce and Zr, potential spectral interferences with uranium and baseline LOD values for Ce and Zr in a UO 2 matrix. In the spectral range of 200 to 800 nm, 33 cerium lines and 25 zirconium lines were identified and shown to have linear correlation values (R 2 ) > 0.97 for both the binary and ternary oxides. The cerium LOD in the (U,Ce)O 2 matrix ranged from 0.34 to 1.08 wt% and 0.94 to 1.22 wt% in (U,Ce,Zr)O 2 for 33 of Ce emission lines. The zirconium limit of detection in the (U,Zr)O 2 matrix ranged from 0.84 to 1.15 wt% and 0.99 to 1.10 wt% in (U,Ce,Zr)O 2 for 25 Zr lines. Finally, the effect of multiple elements in the plasma and the impact on the LOD is discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗