Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Sample”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions

This study compares eight surrogate-model construction approaches using computational experiments. Here, the construction approaches considered include: Artificial Neural Networks (ANNs), Automated Learning of Algebraic Models using Optimization (ALAMO), Radial Basis Networks (RBNs), Extreme Learning Machines (ELMs), Gaussian Progress Regression (GPR), Random Forests (RFs), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines. Each approach is used to construct surrogate models for predicting the outputs of thirty-four test functions. The data used to construct surrogate models were generated using Latin Hypercube Sampling (LHS), Halton and Sobol sampling methods. We studied the impact of sampling method, sample size, and challenge function characteristics on the accuracy of surrogate model predictions. Our results revealed that for smaller sample sizes, on average, surrogate models trained using the data points generated by Sobol sequences provided the best estimation for all surrogate model construction approaches. As the sample size increased, the impact of sampling methods diminished. The comparison of surrogate-modeling construction approaches at large sample sizes revealed that surrogate models trained using ANN, ALAMO, and ELM yielded smaller root mean squared error and higher adjusted R-squared values than the models trained using the rest of the approaches.

42 ENGINEERING↗
DOE OSTI2025

Comparison of measurement techniques and sorption of radium-226 in low and high salinity aqueous samples

Human activities have the potential to redistribute radium (Ra) in the marine environment in a manner that may necessitate monitoring or management of subsequent human or environmental exposures. There is therefore a need to identify accurate and accessible techniques for Ra measurement in high salinity samples and to describe the distribution of Ra in estuarine and marine environments, but most efforts in these areas have focused on low salinity matrices. In addition, rapid and reliable measurements are crucial for time-sensitive samples such as short-lived isotopes or emergency situations. The objective of this study is to describe the limits of detection, cost, and relative ease for measurement of Ra in both low and high salinity aqueous samples via three analytical methods: liquid scintillation counting (LSC), high purity germanium (HPGe) gamma spectrometry, and inductively coupled plasma mass spectrometry (ICP-MS). To contextualize these measurements for real-world scenarios, the partitioning of 226 Ra to substrates relevant to the marine environment was also characterized. Although HPGe detection with solid phase extraction had the lowest limit of detection for low salinity samples (0.27 Bq L −1 ), poor 226 Ra recovery for high salinity samples and high materials costs make this method prohibitive for many users. Limits of detection for high salinity samples were lower for LSC (1.28 Bq L −1 ) than for ICP-MS without dilution (11.4 Bq L −1 ), but significant and unexpected degradation of the high salinity LSC standards was observed after six months. Furthermore, our preferred measurement method for high salinity Ra samples is ICP-MS with sample dilution as necessary to reduce matrix effects.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics

Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Femtosecond Laser Desorption Postionization MS vs ToF-SIMS Imaging for Uncovering Biomarkers Buried in Geological Samples

The study of lipid molecular fossils by traditional biomarker analysis requires bulk sample crushing, followed by solvent extraction, and then the analysis of the extract by gas chromatography-mass spectrometry (GC–MS). This traditional analysis mixes all organic compounds in the sample regardless of their origins, with a loss of information on the spatial distribution of organic molecules within the sample. These shortcomings can be overcome using the chemical mapping of intact samples. Spectroscopic techniques such as UV fluorescence or Raman spectroscopy, laser ablation inductively coupled plasma mass spectrometry, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are among those elemental and molecular mapping techniques. This study employed femtosecond (fs) laser ablation combined with single-photon ionization, a method called fs-laser desorption postionization mass spectrometry (fs-LDPI-MS). In this work, a pulsed ~75 fs, 800 nm laser was used to ablate the geological sample, which was then photoionized after a few microseconds by a pulsed 7.9 eV vacuum ultraviolet laser. An organic carbon-rich geological sample was used for this study to map hydrocarbon biomarkers in sediments that were previously studied by GC–MS. The petrography of this sample was examined by optical and fluorescence microscopy. It is demonstrated here that fs-LDPI-MS combined with petrography for multimodal imaging can expose buried compounds within the sample via in situ layer removal. When used in conjunction with traditional organic geochemical analysis, this method has the potential to determine the spatial distribution of organic biomarkers in geological material. Finally, fs-LDPI-MS imaging data are compared with ToF-SIMS imaging that is commonly used for such studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Direct and rapid uranium isotopic analysis of environmental sample swipes via microextraction – LS-APGD/Orbitrap mass spectrometry

Accurate and precise isotopic analysis of actinides collected on environmental sample swipes from within nuclear facilities is an important safeguarding measure for detecting undeclared materials and activities. Traditional isotope ratio (IR) analysis of actinides using a bulk digestion approach can be laborious and time-consuming. Recently, direct analysis of environmental swipe samples using a microextraction approach has been explored as an alternative to conventional bulk digestion methods. The present study further evaluates this approach for accurate and precise isotopic analysis of uranium (U) in cotton swipe samples using the liquid sampling-atmospheric pressure glow discharge (LS-APGD)/Orbitrap-FTMS Booster detection system. The instrumental parameters were optimized, and quantitative capabilities were demonstrated through excellent linearity (R2 = 0.99) across a deposited mass range of 1 to 500 ng, with a limit of detection of 90 pg (in a 2 × 4 mm region) for total U; well below typical U concentrations (ng–mg range) in environmental sample swipes. The 235U/238U ratio showed excellent accuracy (−0.3% relative difference from the certificate value), with ∼20× improvement over previous microextraction-Orbitrap methods. Precision (∼1% relative standard deviation) was also greatly enhanced by ∼5×. This study also demonstrates that figures of merit achieved with neat U solution were not highly degraded when various external interfering elements/matrices were introduced. Finally, the developed method was successfully applied to analyze practical swipe samples representative of laboratory and outdoor environments. The presented figures of merit and applicability to practical samples further validate the capability of the platform for rapid analysis (<7 minutes) of environmental swipe samples.

Shrestha, Suraj [Clemson University, SC]↗
DOE OSTI2016

A whole-path importance-sampling scheme for Feynman path integral calculations of absolute partition functions and free energies

Using Feynman path integrals, a molecular partition function can be written as a double integral with the inner integral involving all closed paths centered at a given molecular configuration, and the outer integral involving all possible molecular configurations. In previous work employing Monte Carlo methods to evaluate such partition functions, we presented schemes for importance sampling and stratification in the molecular configurations that constitute the path centroids, but we relied on free-particle paths for sampling the path integrals. At low temperatures, the path sampling is expensive because the paths can travel far from the centroid configuration. In this paper, we now present a scheme for importance sampling of whole Feynman paths based on harmonic information from an instantaneous normal mode calculation at the centroid configuration, which we refer to as harmonically guided whole-path importance sampling (WPIS). We obtain paths conforming to our chosen importance function by rejection sampling from a distribution of free-particle paths. Sample calculations on CH 4 demonstrate that at a temperature of 200 K, about 99.9% of the free-particle paths can be rejected without integration, and at 300 K, about 98% can be rejected. Finally, we also show that it is typically possible to reduce the overhead associated with the WPIS scheme by sampling the paths using a significantly lower-order path discretization than that which is needed to converge the partition function.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Quantitative comparison of adaptive sampling methods for protein dynamics

Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. Here, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.

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

Evaluating the effect of sample type on American alligator ( Alligator mississippiensis ) analyte values in a point-of-care blood analyser

The assessment of wildlife health has been enhanced by the ability of point-of-care (POC) blood analysers to provide biochemical analyses of non-domesticated animals in the field. However, environmental limitations (e.g. temperature, atmospheric humidity and rain) and lack of reference values may inhibit researchers from using such a device with certain wildlife species. Evaluating the use of alternative sample types, such as plasma, in a POC device may afford researchers the opportunity to delay sample analysis and the ability to use banked samples. In this study, we examined fresh whole blood, fresh plasma and frozen plasma (sample type) pH, partial pressure of carbon dioxide (PCO 2 ), bicarbonate (HCO 3 ₋), total carbon dioxide (TCO 2 ), base excess (BE), partial pressure of oxygen (PO 2 ), oxygen saturation (sO 2 ) and lactate concentrations in 23 juvenile American alligators (Alligator mississippiensis) using an i-STAT CG4+ cartridge. Our results indicate that sample type had no effect on lactate concentration values (F 2,65 = 0.37, P = 0.963), suggesting that the i-STAT analyser can be used reliably to quantify lactate concentrations in fresh and frozen plasma samples. In contrast, the other seven blood parameters measured by the CG4+ cartridge were significantly affected by sample type. In conclusion, we were able to collect blood samples from all alligators within 2 min of capture to establish preliminary reference ranges for juvenile alligators based on values obtained using fresh whole blood.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Application-Specific Graph Sampling for Frequent Subgraph Mining and Community Detection

Graph mining is an important data analysis methodology, but struggles as the input graph size increases. The scalability and usability challenges posed by such large graphs make it imperative to sample the input graph and reduce its size. The critical challenge in sampling is to identify the appropriate algorithm to insure the resulting analysis does not suffer heavily from the data reduction. Predicting the expected performance degradation for a given graph and sampling algorithm is also useful. In this paper, we present different sampling approaches for graph mining applications such as Frequent Subgrpah Mining (FSM), and Community Detection (CD). We explore graph metrics such as PageRank, Triangles, and Diversity to sample a graph and conclude that for heterogeneous graphs Triangles and Diversity perform better than degree based metrics. We also present two new sampling variations for targeted graph mining applications. We present empirical results to show that knowledge of the target application, along with input graph properties can be used to select the best sampling algorithm. We also conclude that performance degradation is an abrupt, rather than gradual phenomena, as the sample size decreases. We present the empirical results to show that the performance degradation follows a logistic function.

Purohit, Sumit↗
DOE OSTI2023

In Vitro Thrombogenicity Testing of Biomaterials in a Dynamic Flow Loop: Effects of Length and Quantity of Test Samples

Abstract The results of in vitro dynamic thrombogenicity testing of biomaterials and medical devices can be significantly impacted by test conditions. To develop and standardize a robust dynamic in vitro thrombogenicity tool, the key test parameters need to be appropriately evaluated and optimized. We used a flow loop test system previously developed in our laboratory to investigate the effects of sample length and the number of samples per test loop on the thrombogenicity results. Porcine blood heparinized to a donor-specific target concentration was recirculated at room temperature through polyvinyl chloride (PVC) tubing loops containing test materials for 1 h at 200 mL/min. Four test materials (polytetrafluoroethylene (PTFE), latex, PVC, and silicone) with various thrombotic potentials in two sample lengths (12 and 18 cm) were examined. For the 12-cm long materials, two different test configurations (one and two samples per loop) were compared. Thrombogenicity was assessed through percent thrombus surface coverage, thrombus weight, and platelet count reduction in the blood. The test system was able to effectively differentiate the thrombogenicity profile of the materials (latex > silicone > PVC ≥ PTFE) at all test configurations. Increasing test sample length by 50% did not significantly impact the test results as both 12 and 18 cm sample lengths were shown to equally differentiate thrombotic potentials between the materials. The addition of a second test sample to each loop did not increase the test sensitivity and may produce confounding results, and thus a single test sample per loop is recommended.

Engineering↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗
DOE OSTI2022

Leaf structural and chemical traits, and BNL field campaign sample details, San Lorenzo, Panama, 2020

This data package includes leaf traits, canopy traits and sample details for leaves from 71 species sampled from the San Lorenzo forest canopy crane site, Panama (PA-SLZ) during the BNL field campaign in January to March 2020. Each leaf sample is described with species, phenological stage and location within vertical canopy profiles. Leaf area index (LAI) and height is presented for each canopy profile location. Leaf mass per area (LMA), leaf water content (LWC) and leaf carbon and nitrogen content are included for a subset of the samples. This data package includes sample details, processed data for leaf traits and LAI (*.csv), LAI raw data (compressed as *.zip) and digital camera images (*.jpg, compressed as *.zip) of the leaf samples. Metadata files include data descriptions (_dd.csv) for tabular data, a list of all species sampled during the campaign (*.csv) and a detailed description of the field campaign protocol and methods (*.pdf). See related datasets for leaf gas exchange, leaf water potential and leaf spectral measurements made on the samples described here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Portable Automation of Static Chamber Sample Collection for Quantifying Soil Gas Flux

Quantification of soil gas flux using the static chamber method is labor intensive. The number of chambers that can be sampled is limited by the spacing between chambers and the availability of trained research technicians. An automated system for collecting gas samples from chambers in the field would eliminate the need for personnel to return to the chamber during a flux measurement period and would allow a single technician to sample multiple chambers simultaneously. This study describes Chamber Automated Sampling Equipment (FluxCASE) to collect and store chamber headspace gas samples at assigned time points for the measurement of soil gas flux. The FluxCASE design and operation is described, and the accuracy and precision of the FluxCASE system is evaluated. In laboratory measurements of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) concentrations of a standardized gas mixture, coefficients of variation associated with automated and manual sample collection were comparable, indicating no loss of precision. In the field, soil gas fluxes measured from FluxCASEs were in agreement with manual sampling for both N2O and CO2. Slopes of regression equations were 1.01 for CO2 and 0.97 for N2O. The 95% confidence limits of the slopes of the regression lines included the value of one, indicating no bias. Additionally, an expense analysis found a cost recovery ranging from 0.6 to 2.2 yr. Implementing the FluxCASE system is an alternative to improve the efficiency of the static chamber method for measuring soil gas flux while maintaining the accuracy and precision of manual sampling.

Davis, Morgan P.↗
DOE OSTI2016

Results of Hg speciation testing on tank 39 and 1Q16 tank 50 samples

The Savannah River National Laboratory (SRNL) was tasked with preparing and shipping samples for Hg speciation by Eurofins Frontier Global Sciences, Inc. in Seattle, WA on behalf of the Savannah River Remediation (SRR) Mercury Task Team.i,ii The seventeenth shipment of samples was designated to include two Tank 39 samples and the 1Q16 Tank 50 Quarterly WAC sample. The surface Tank 39 sample was pulled at 262.1” from the tank bottom, and the depth Tank 39 sample was pulled at 95” from the tank bottom. The 1Q16 Tank 50 WAC sample was drawn from the 1-L variable depth sample received by SRNL.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

K West Basin Sand Filter Backwash Sample Analysis

A sand filter is used to help maintain water clarity at the K West Basin where highly radioactive sludge is stored. Eventually that sand filter will require disposal. The radionuclide content of the solids trapped in the sand filter will affect the selection of the sand filter disposal pathway. The Pacific Northwest National Laboratory (PNNL) was contracted by the K Basin Operations & Plateau Remediation Project (operations contractor CH2M Hill) to analyze the radionuclide content of the solids collected from the backwash of the K West Basin sand filter. The radionuclide composition in the sand filter backwash solids will be used by CH2M Hill to determine if the sand filter media and retained sludge solids will be designated as transuranic waste for disposal purposes or can be processed through less expensive means. On October 19, 2015, K Basin Operations & Plateau Remediation Project staff backwashed the sand filter into the North Load-Out Pit (NLOP) and immediately collected sample slurry from a sampling tube positioned 24 in. above the NLOP floor. The 764 g sand filter backwash slurry sample, KW-105 SFBW-001, was submitted to PNNL for analysis on October 20, 2015. Solids from the slurry sample were consolidated into two samples (i.e., a primary and a duplicate sample) by centrifuging and measured for mass (0.82 g combined – wet centrifuged solids basis) and volume (0.80 mL combined). The solids were a dark brown/orange color, consistent with iron oxide/hydroxide. The solids were dried; the combined dry solids mass was 0.1113 g, corresponding to 0.0146 weight percent (wt%) solids in the original submitted sample slurry. The solids were acid-digested using nitric and hydrochloric acids. Insoluble solids developed upon dilution with 0.5 M HNO 3 , corresponding to an average 6.5 wt% of the initial dry solids content. The acid digestate and insoluble solids were analyzed separately by gamma spectrometry. Nominally, 7.7% of the 60 Co was present in the insoluble solids; less than 1% of other gamma-emitters (i.e., 137 Cs, 154/155 Eu, and 241 Am) were present in the insoluble solids. Aliquots of the acid digestate were analyzed directly using gamma energy analysis (GEA) and after separations for 238 Pu, 239+240 Pu, 237 Np, and 241 Am radioisotopes using alpha energy analysis (AEA). The 90 Sr was measured by liquid scintillation counting (LSC) on the Sr-separated fraction. The plutonium isotopic distribution of the acid digestate was analyzed following Pu separations by thermal ionization mass spectrometry (TIMS). A table summarizes the results for the primary and duplicate samples. The 239+240 Pu concentration (µCi/g dry) relative to 90 Sr and to 137 Cs concentrations (µCi/g dry) was examined. The K West Basin sludge has a 239+240 Pu/ 90 Sr ranging from 0.1 to 1.2 and the 239+240 Pu/ 137 Cs ratio ranging from 0.10 to 0.47. In contrast, the sand filter backwash solids 239+240 Pu/ 90 Sr ratio was 10.6 and the 239+240 Pu/ 137 Cs ratio was 2.0. The ratio differences indicate a relative enhancement of the Pu concentration in the sand filter solids relative to the 137 Cs and 90 Sr sludge concentrations currently in the K West Basin. A dose-to-curie radioisotope evaluation of the sand filter waste form may need to consider this dissimilarity.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗