Search NASA⌕ Search

SEARCH · Search NASA

Results for “samples”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improved quantitation of SIMS depth profile measurements of niobium via sample holder design improvements and characterization of grain orientation effects

The importance of SIMS analyses for “N-doped” impurity alloyed niobium and other surface-alloyed materials continues to increase. A major hurdle is the uncertainty of instrument calibration due to changes in sample height either from sample surface topography or from the sample holder itself. The CAMECA sample holder design allows for many types of samples to be analyzed. However, a drawback is that the holder faceplate can bend, contributing an uncertainty in the relative sensitivity factor (RSF) used to quantify the SIMS results. Here, we describe an improved sample holder having a reinforced faceplate, which prevents deflection and reduces uncertainty. Simulations show that the new design significantly reduces deflection from 10 μm to 5 nm. Sample measurements show a reduction of RSF uncertainty from this source from 4.1% to 0.95%. Grain orientation has long been suspected to affect RSF measurement as well. A bicrystal implant standard, consisting of randomly oriented and [001] grains, was successively rotated 15° between analyses. It was observed that 20% of the analyses performed on the randomly oriented grain exhibited anomalously high RSF values as well as slow sputter rates. These features were associated with the changing grain normal orientation with respect to the primary Cs + beam. The grain orientation associated with the rise in RSF was simulated and determined to be the [101] crystallographic plane, thus indicating that ion channeling was responsible for the significantly increased RSF. Focused ion beam analysis confirmed slower sputter rates for the cardinal crystallographic orientations, indicating that ion channeling occurred for each.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were sampled at sub-Nyquist rates. In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube. Perhaps most surprisingly, chemical signal amplification generally seems to increase as the level of sampling decreases. In some examples, the chemical signal is significantly stronger in a data cube reconstructed from 10% CS sampling than it is in the raw, 100% sampled data cube. We explore this phenomenon in two real-world datasets including the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of these datasets contains the release of a chemical simulant, such as glacial acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we use the adaptive coherence estimator (ACE) to detect a target signal in the hyperspectral data cube. We end the paper by suggesting some theoretical justifications for why chemical signals would be amplified in CS sampled and reconstructed hyperspectral data cubes and discuss some practical implications.

Hyperspectral imagery, Compressive Sensing, single↗
DOE OSTI2024

RT-QuIC detection of chronic wasting disease prion in platelet samples of white-tailed deer

Chronic wasting disease (CWD) is a prion disease of captive and free-ranging cervids. Currently, a definitive diagnosis of CWD relies on immunohistochemistry detection of PrP Sc in the obex and retropharyngeal lymph node (RPLN) of the affected cervids. For high-throughput screening of CWD in wild cervids, RPLN samples are tested by ELISA followed by IHC confirmation of positive results. Recently, real-time quacking-induced conversion (RT-QuIC) has been used to detect CWD positivity in various types of samples. To develop a blood RT-QuIC assay suitable for CWD diagnosis, this study evaluated the assay sensitivity and specificity with and without ASR1-based preanalytical enrichment and NaI as the main ionic component in assay buffer. A total of 23 platelet samples derived from CWD-positive deer (ELISA +/IHC +) and 30 platelet samples from CWD-negative (ELISA-) deer were tested. The diagnostic sensitivity was 43.48% (NaCl), 65.22% (NaI), 60.87% (NaCl-ASR1) or 82.61% (NaI-ASR1). The diagnostic specificity was 96.67% (NaCl), 100% (NaI), 100% (NaCl-ASR1), or 96.67% (NaI-ASR1). The probability of detecting CWD prion in platelet samples derived from CWD-positive deer was 0.924 (95% CRI: 0.714, 0.989) under NaI-ASR1 experimental condition and 0.530 (95% CRI: 0.156, 0.890) under NaCl alone condition. The rate of amyloid formation (RFA) was greatest under the NaI-ASR1 condition at 10 -2 (0.01491, 95% CRI: 0.00675, 0.03384) and 10 -3 (0.00629, 95% CRI: 0.00283, 0.01410) sample dilution levels. Incorporation of ASR1-based preanalytical enrichment and NaI as the main ionic component signifcantly improved the sensitivity of CWD RT-QuIC on deer platelet samples. Blood test by the improved RT-QuIC assay may be used for antemortem and postmortem diagnosis of CWD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or “feather spots”—known to be of avian origin but not readily assignable to species via morphology—may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

14 SOLAR ENERGY↗
DOE OSTI2020

Analysis Of The Sludge Batch 7b (Macrobatch 9) DWPF Pour Stream Glass Sample (Rev. 1)

The Defense Waste Processing Facility (DWPF) began processing Sludge Batch 7b (SB7b), also referred to as Macrobatch 9 (MB9), in January 2012. SB7b is a blend of the heel of Tank 40 from Sludge Batch 7a (SB7a) and the SB7b material that was transferred to Tank 40 from Tank 51. SB7b was processed using Frit 418. During processing of each sludge batch, the DWPF is required to take at least one glass sample to meet the objectives of the Glass Product Control Program (GPCP), which is governed by the DWPF Waste Form Compliance Plan, and to complete the necessary Production Records so that the final glass product may be disposed of at a Federal Repository. Two pour stream glass samples were collected while processing SB7b. The samples were transferred to the Savannah River National Laboratory (SRNL) where one was analyzed and the other was archived. The following conclusions were drawn from the analytical results provided in this report: The sum of oxides for the official SB7b pour stream glass is within the Product Composition Control System (PCCS) limits (95-105 wt%); The average calculated Waste Dilution Factor (WDF) for SB7b is 2.3. In general, the measured radionuclide content of the official SB7b pour stream glass is in good agreement with the calculated values from the Tank 40 dried sludge results from the SB7b Waste Acceptance Program Specification (WAPS) sample; As in previous pour stream samples, ruthenium and rhodium inclusions were detected by Scanning Electron Microscopy-Electron Dispersive Spectroscopy (SEM-EDS) in the SB7b pour stream sample; The Product Consistency Test (PCT) results indicate that the official SB7b pour stream glass meets the waste acceptance criteria for durability with a normalized boron release of 0.8 g/L, which is an order of magnitude less than the Environmental Assessment (EA) glass; The measured density of the SB7b pour stream glass was 2.70 g/cm 3 ; The Fe 2+ /ΣFe ratio of the SB7b pour stream samples was 0.07.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Analysis of 2H-Evaporator Acid Cleaning Samples

The 2H-Evaporator acid cleaning solution samples were analyzed by SRNL to determine a composition for the scale present in the evaporator before recent acid cleaning. Composite samples were formed from the solution samples from the two acid cleaning cycles. The solution composition was converted to a weight percent scale solids basis under an assumed chemical composition. The scale composition produced from the acid cleaning solution samples indicates a concentration of 6.85 wt% uranium. An upper bound, onesided 95% confidence interval on the weight percent uranium value may be given as 6.9 wt% + 1.645 × 0.596 wt% = 7.9 wt%. The comparison of the composition from the current acid cleaning solutions with the composition of recent scale samples along with the thermodynamic modeling results provides reasonable assurance that the sample results provide a good representation of the overall scale composition in the evaporator prior to acid cleaning. The small amount of scale solids dissolved in the 1.5 M nitric acid during the evaporator cleaning process likely produced only a small amount of precipitation based on modeling results and the visual appearance of the samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Analysis of Tank 30H (HTF-30-18-54, -55), Tank 32H (HTF-32-18-56, -57), and Tank 37H (HTF-37-18-58, -59) Samples for Support of the Evaporator Feed Qualification and Corrosion Control Programs for the 3H-Evaporator

SRNL analyzed samples from Tank 30H, Tank 32H, and Tank 37H to support the Evaporator Feed Qualification and Corrosion Control Programs for the 3H-Evaporator system. The six samples from Tanks 30H, 32H, and 37H all contain highly concentrated salt solution s with sodium concentrations ranging from 7.89 M to 15.0 M. The samples from Tank 30H show small differences in the concentrations of the major cations and anions between the surface sample and VDS. The two Tank 32H samples show significant differences in the concentrations of the major anions and cations with the VDS being nearly twice as concentrated as the surface sample. The surface sample and VDS from Tank 37H contain very similar compositions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Analytical Results from the Salt Batch 10 Routine DSSHT and SEHT Monthly Samples

Strip Effluent Hold Tank (SEHT) and Decontaminated Salt Solution Hold Tank (DSSHT) samples from several of the “microbatches” of Integrated Salt Disposition Project (ISDP) Salt Batch (“Macrobatch”) 10 have been analyzed for 238Pu, 90Sr, 137Cs, elemental constituents (Inductively Coupled Plasma Emission Spectroscopy - ICPES), and in some cases anions (Ion Chromatography Anions - IC-A). The included tables contain the data. These samples are from Salt Batch 10 material recently introduced into the Modular Caustic-Side Solvent Extraction Unit (MCU) system. The DSSHT results are typical for this type of sample, although the dilution due to processing is lower than expected. The August sample is known to have incorporated large amounts of rain water and cannot be compared to other DSSHT samples. The SEHT sample results are typical except for the May sample which is known to have rain water and Contactor Drain Tank (CDT) material in it.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Preparation of Four Large-format Hot Isostatically Pressed I-AgZ Waste Form Samples for Performance Testing

Hot isostatic pressing (HIP) is being considered as a process to directly convert 129 I-bearing materials to a durable radiological waste form. Zeolites, specifically silver-exchanged mordenite (AgZ), have been studied extensively as potential radioactive iodine sorbents and will contain 129 I as chemisorbed AgI, a relatively insoluble compound with a high melting point. Oak Ridge National Laboratory has conducted several recent studies on the HIP of both I–AgZ and other iodine-bearing zeolites, resulting in an inventory of samples prepared with a suite of zeolites with varied iodine incorporation methods, pressing conditions, and sizes. These samples have been made available to researchers at other national laboratories in support of test method development to assess the stability of heterogenous iodine-bearing waste forms. The work described here generated four large-format HIPed I–AgZ samples. These samples are 1.5 in. diameter and 2 in. length and contain 49 g of I–AgZ. Two samples were prepared at an iodine loading of 64 mg I/g I– AgZ, and two samples were prepared at an iodine loading of 135 mg I/g I–AgZ. These two sets of duplicate samples will be made available upon request in support of iodine waste form durability method development efforts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Characterization Results of SRS Tank 26 Samples FTF-26-19-12 and -13

Savannah River Remediation (SRR) has requested that the Savannah River National Laboratory (SRNL) characterize several samples from the Savannah River Site (SRS) Tank Farm in preparation for Sludge Batch (SB) 10. This report documents the characterization of Tank 26 material received by SRNL in May of 2019. SRR pulled 2 x 200 mL samples (FTF-26-19-12 and FTF-26-19-13) with Tank 26 slurry pumps at 60 inches. Two samples were taken to ensure SRNL has adequate material for the requested analyses. Because both samples were taken at the same time and at the same tank level, SRNL combined the two Tank Farm samples into one sample prior to analyses. Results presented in this report include: weight percent solids, density, elemental composition of supernate and slurry, Cs-137 in supernate and slurry, and slurry total alpha and beta. The sample contained higher than expected total sulfur with a significant insoluble fraction, prompting SRR-E to request a washing study to predict how sulfur will be removed during Tank Farm washing.

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

Analysis of Tank 38H (HTF-38-20-7, -8) and tank 43H (HTF-43-20-9, -10) samples for Support of the Enrichment Control and Corrosion Control Programs

SRNL analyzed samples from Tank 38H and Tank 43H to support ECP and CCP. The results indicate the concentrations of most species in the Tank 38H surface sample decreased significantly from the previous surface sample. The Tank 38H sub-surface sample shows only small changes in concentration for most major species in the solution from the previous sample. The large differences in the concentrations of major components between the Tank 38H surface and sub-surface samples indicate significant stratification of solution species within the tank. The Tank 38H sub-surface sample contained a small amount of sludge solids (<1 wt%).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Activation Study of Isotopically Enriched Samples During Neutron Time-of-Flight Measurements

Over the course of a time-of-flight neutron cross section measurement, the sample isotope(s) of interest are irradiated by neutrons and photons. Ideally, the enriched sample will not have any additional radioactivity after the measurement as compared to the baseline detectable radioactivity (0.6 Bq/g) established prior to acquisition of the sample. This document presents the process for estimating the activation of a stable enriched isotope exposed to a mixed photon and neutron source using tools from the SCALE code system developed at Oak Ridge National Laboratory. The report also presents the activation analysis for the isotopes of current interest: 90;91;92;94 Zr. This information is provided for two sets of Zr samples: one set of ZrO 2 samples, and one set of metal Zr samples. It was determined that, following measurements at the GELINA linear accelerator facility, all of the enriched Zr samples would contain less than 0.6 Bq/g of radioactivity within 183 days after irradiation.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Results of the Analyses of SMECT Mercury Sample Collected During Mercury Pump (MB1) Functional Check

A pump added to remove mercury from the sump of the Slurry Mix Evaporator Condensate Tank (SMECT) in DWPF was successfully tested in 2020, resulting in a sample of contaminated mercury obtained from the Mercury Purification Process (MPP) cell. The mercury in the sump of the SMECT was present as a result of steam stripping of sludge slurry in the Sludge Receipt and Adjustment Tank (SRAT) at DWPF. A schematic of the MPP system provided by DWPF personnel is shown in Appendix A. DWPF personnel supplied the following detailed description of the Hg sampling: “The sample provided is the result of DWPF extraction of mercury by means of the Mercury Pump-Water Cart. The Mercury Pump extracted the sample from the SMECT mercury sump. The pump uses pressurized water to transfer a small amount of mercury to a leachate bucket located at the Lab Mercury cell. The mercury and water were allowed to be self separated, and mercury was gravity decanted from the leachate bucket.” DWPF personnel indicate that the leachate bucket is equipped with both a top stopcock/drain and a lower/bottom stopcock/drain from which the mercury was decanted or drained out, while the separated water layer remained above the mercury during draining. The DWPF customer requested SRNL to analyze the sample for the following: gross alpha/nonvolatile beta analysis and perform a Toxicity Characteristic Leaching Procedure (TCLP) per the Environmental Protection Agency's Test Methods for Evaluating Solid Waste: Physical/Chemical Methods, SW-846. While the TCLP was preferred, a Total Metals analysis of the eight Resource Conservation and Recovery Act (RCRA) metals was deemed acceptable.2 The analytical data collected on this mercury sample will be used in planning for sample disposition as well to start a profile for future Hg disposition of Hg collected from steam stripping.

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

Analysis of Tank 38H (HTF-38-21-95, -96) and Tank 43H (HTF-43-21-97, -98) Samples for Support of the Enrichment Control and Corrosion Control Programs

SRNL analyzed samples from Tank 38H and Tank 43H to support ECP and CCP. The results indicate the concentrations of most species in the Tank 38H surface sample increased from the previous surface sample. The Tank 38H sub-surface sample shows only minor changes in concentration for soluble species in the solution (e.g., Na, Al, Cs-137) relative to the previous sample, but a small decrease in concentrations for species typically associated with sludge solids (e.g., U, Pu, Fe, Si) likely because of a decrease in sludge solids from the previous sample. The large differences in the concentrations of major components between the Tank 38H surface and sub-surface samples indicate significant stratification of solution species within the tank.

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

Intelligent Sampling for Surrogate Modeling, Hyperparameter Optimization, and Data Analysis

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints specific to that field such as uniform coverage of the range of each dimension or random samples that are at least a certain distance apart from each other. When an application imposes new constraints, for example, by requiring samples in a non-rectangular domain or the addition of new samples to an existing set, a common solution is to modify the algorithm currently in use, often with less than satisfactory results. As an alternative, we propose the concept of intelligent sampling, where we devise algorithms specifically tailored to meet our sampling needs, either by creating new algorithms or by modifying suitable algorithms from other fields. Surprisingly, both qualitative and quantitative comparisons indicate that some relatively simple algorithms can be easily modified to meet the many sampling requirements of surrogate modeling, hyperparameter optimization, and data analysis; these algorithms outperform their more sophisticated counterparts currently in use, resulting in better use of time and computer resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

IDP Analysis of Tank 9H Mined Well Samples

The Savannah River Site (SRS) Tank Farm Transfer Control Program Description Document (PDD) requires Inhalation Dose Potential (IDP) testing to determine whether a transfer qualifies as a “HIGH-REM” (> 2.08E+08 rem/gal) or a “LOW-REM” (≤ 2.08E+08 rem/gal) waste transfer. Due to the unexpected presence of solids in a variable depth sample taken from Tank 9H, transfers from salt tanks potentially containing insoluble solids are currently being assessed as sludge slurry transfers. The insoluble solids concentration of such samples were requested to be adjusted to 16.7 wt% for the results to be used to bound future transfers from that tank. Two samples, HTF-09-21-100 and -101, were received from Tank 9H for analysis to determine IDP. The initial percent insoluble solids concentrations of HTF-09-21-100 and -101 were measured at 4.04 wt% and 6.23 wt%, respectively. Sample HTF-09-21-101 was adjusted to a target of at least 16.7 wt% insoluble solids by removing supernate. Analysis measured the adjusted insoluble solids concentration at 19.0 wt%. A representative sample was obtained for analysis. Then, based on calculations, supernate was added back to HTF-09-21-101 to achieve a percent insoluble solids concentration of 17.1 wt%, and a representative sample was collected for analysis. Based on duplicate analyses at 17.1 wt% and 19.0 wt% insoluble solids, the samples contain less than 0.0012 Ci/L of gross alpha activity. Therefore, the Tank 9H material can be transferred as a “LOW REM” transfer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Characterization of the SRNL-Washed Tank 51 Sludge Batch 10 Qualification Sample

Savannah River National Laboratory (SRNL) personnel have been requested to qualify the next sludge batch (Sludge Batch 10 – SB10) for processing at the Defense Waste Processing Facility (DWPF). To accomplish this task, Savannah River Remediation (SRR) sent SRNL two 3-L samples of Tank 51H slurry to be characterized, washed, and then used in a lab-scale demonstration of the DWPF flowsheet. Sample HTF-51-19-114 was received on January 28, 2020, and HTF-51-20-15 was received on February 4, 2020. SRNL washed the Tank 51H sample per the Tank Farm washing strategy. During washing, material from H Canyon Tanks 16.3 and 16.4 was also added to the Tank 51 samples to simulate canyon discharges subsequent to sampling. A part of the qualification process is extensive radionuclide and chemical characterization of the SRNL-washed Tank 51H slurry. This report documents the chemical characterization of the washed slurry; radiological characterization will be documented in a separate report. The major supernatant components, elements on a weight percent calcined basis, and the weight percent solids of the SRNL-washed sample were comparable to the Tank Farm projections, with the exception of free hydroxide and carbonate. Therefore, this SRNL-washed sample is suitable for further SB10 qualification activities and SRR planning for SB10.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗