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At least 523 records · Page 29

VAPOR PRESSURE ISOTOPE EFFECTS IN THE MEASUREMENT OF ENVIRONMENTAL TRITIUM SAMPLES.

Standard procedures for the measurement of tritium in water samples often require distillation of an appropriate sample aliquot. This distillation process may result in a fractionation of tritiated water and regular light water due to the vapor pressure isotope effect, introducing either a bias or an additional contribution to the total tritium measurement uncertainty. The magnitude of the vapor pressure isotope effect is characterized as functions of the amount of water distilled from the sample aliquot and the heat settings for the distillation process. The tritium concentration in the distillate is higher than the tritium concentration in the sample early in the distillation process, it then sharply decreases due to the vapor pressure isotope effect and becomes lower than the tritium concentration in the sample, until the high tritium concentration retained in the boiling flask is evaporated at the end of the process. At that time, the tritium concentration in the distillate again overestimates the sample tritium concentration. The vapor pressure isotope effect is more pronounced the slower the evaporation and distillation process is conducted; a lower heat setting during the evaporation of the sample results in a larger bias in the tritium measurement. The experimental setup used and the fact that the current study allowed for an investigation of the relative change in vapor pressure isotope effect in the course of the distillation process distinguish it from and extend previously published measurements. The separation factor as a quantitative measure of the vapor pressure isotope effect is found to assume values of 1.034 {+-} 0.033, 1.052 {+-} 0.025, and 1.066 {+-} 0.037, depending on the vigor of the boiling process during distillation of the sample. A lower heat setting in the experimental setup, and therefore a less vigorous boiling process, results in a larger value for the separation factor. For a tritium measurement in water samples, this implies that the tritium concentration could be underestimated by 3 - 6%.

54 ENVIRONMENTAL SCIENCES↗

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

97 MATHEMATICS AND COMPUTING↗

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Morrow, Mitchell Hedges↗

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Tohlen, Michael Aaron↗

Multimodal Mass Spectrometry Imaging (MSI) of Archean and Jurassic Geologic Samples

Current geochemical analytical techniques such as gas chromatography-mass spectrometry (GC-MS and $G\acute{C}GC-MS$) can effectively determine the composition and structure of the organic biosignatures within a sample but cannot resolve the spatial distribution of organic biosignatures within rocks or sediments. The spatial distribution can be used to determine the character of the organic biosignatures [1], which can be indigenous (deposited with host rocks), non-indigenous (incorporated after deposition via fluid migration), or contaminant. Determining the character of organic biosignatures is critical for ancient samples [2] and will be critical if organics are observed in samples returned from Mars [3].Fs-LDPI-MS has the ability to map organic compounds across the surface of samples at 2 - 10 μm lateral resolution. Furthermore, fs-LDPI-MS can be used to carry out multiple analyses in the same location for micron-scale analysis of previously buried material [4]. We used fs-LDPI-MS to examine a ~164 million year old organic-rich mudstone from SW England (14.2 wt. % total organic carbon) [5] and determined the spatial distribution of likely indigenous components buried below potentially contaminated surface layers.We then used ToF-SIMS to re-analyze the mudstone to directly compare ToF-SIMS to fs-LDPI-MS datasets, and to analyze a series of ~2.7 billion year old geologic samples from Timmins, ON, CA [6]. Ref. [6] previously analyzed the Archean samples and observed a series of archaeal biomarkers, as well as hopanes and steranes. Using ToF-SIMS analysis, we were able to detect hopanes and steranes in the mudstone samples, but not in the Archean samples indicating that the hopanes and steranes are most likely contaminants in the Archean samples. We also did not observe the archaeal biomarkers; however, this may be from the lack of molecular ion preservation during ToF-SIMS.

Pasterski, Michael↗

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Morrow, Mitchell Hedges↗

Single-Use Destructive Assay for Uranium Hexafluoride Sampling

Sampling uranium hexafluoride (UF6) for the determination of enrichments by destructive analysis (DA) is a critical component in the International Atomic Energy Agency’s layered safeguards approach for uranium processing facilities. Typically, gram-quantity UF6 samples are collected during inspections and stored under tag-and-seal until transportation to an off-site analytical laboratory. The shipping times can be long, and evolving restrictions on radioactive/corrosive materials shipments may increasingly limit the IAEA’s ability to transport UF6 samples easily. Pacific Northwest National Laboratory has developed a low-cost UF6 sampling technology called Single-Use Destructive Assay (SUDA) that addresses these challenges, as well as provides DA sample geometries that can be tailored for different analytical methods, including potential on-site analyses. The SUDA samplers, along with a unique holder, are designed for direct attachment to existing taps at uranium processing facilities, allowing gaseous UF6 to come into direct contact with a zeolite film. The SUDA technology features the ability to capture uranium in a more easily shipped and handled form as the solid, more stable, and relatively less hazardous hydrated uranyl fluoride (UO2F2•nH2O), which is formed through the controlled hydrolysis of UF6. We have recently simulated uranium collection under enrichment plant sampling conditions to further improve our understanding of SUDA sampling. Presented here is our recent work on measuring the relationship between sampling conditions and uranium collection, which includes control of the uranium-mass-to-zeolite ratio and assessing variable UF6 gas and sampling parameters that can affect collection using the SUDA sampler.

Pope, Timothy R.↗

Data Report on Post-Irradiation Dimensional Change of AGC-1 Samples

This report documents the measured post irradiation dimensional change in the AGC-1 samples. The AGC-1 capsule is the first in six planned irradiation capsules comprising the Advanced Graphite Creep (AGC) test series. AGC-1 irradiation began September 5, 2009 in the Advanced Test Reactor (ATR) and was completed on January 8, 2011. The capsule was cooled for 3 months in the ATR Canal, and then shipped to MFC in April 2011 for disassembly and sample extraction. After extraction the samples were shipped to the INL Research Center (IRC) for initial post-irradiation examination (PIE) and storage in the irradiated graphite vault. The AGC-1 capsule design contained “matched pair” samples to ascertain the irradiation-induced dimensional changes and levels of creep experienced in different graphite types. The irradiation-induced dimensional changes and creep levels are determined by comparing the total dimensional change for stressed and unstressed samples of the same type of graphite exposed to the same dose levels and at similar temperatures. Under irradiation creep (i.e. permanent strain due to irradiation, stress, and temperature) the stressed samples should demonstrate more dimensional change than the unstressed samples. This additional dimensional change in the stressed samples is designated as “irradiation-induced creep” in graphite. The data are further presented using the parameters influencing dimensional change in graphite; levels of induced stress, temperature, graphite type, and dose. However, the AGC-1 post-irradiation examination is a significant endeavor and this data report serves to provide irradiation-induced dimensional change data for AGC capsule design refinement as well as a status on the progress of the PIE activities. The dimensional changes of both the samples and graphite body are very important to the design of the future AGC capsules (AGC-3 through AGC-6) and are provided as soon as the data are available in order to determine whether design changes to the next capsule are required. A complete evaluation of the irradiation-induced dimensional change data will be performed for a final AGC-1 PIE report that will include full analysis of pre- and post-irradiation data, with verified AGC-1 irradiation conditions of temperature and dose.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Per-instruction energy debugging using instruction sampling hardware

A processor utilizes instruction based sampling to generate sampling data sampled on a per instruction basis during execution of an instruction. The sampling data indicates what processor hardware was used due to the execution of the instruction. Software receives the sampling data and generates an estimate of energy used by the instruction based on the sampling data. The sampling data may include microarchitectural events and the energy estimate utilizes a base energy amount corresponding to the instruction executed along with energy amounts corresponding to the microarchitectural events in the sampling data. The sampling data may include switching events associated with hardware blocks that switched due to execution of the instruction and the energy estimate for the instruction is based on the switching events and capacitance estimates associated with the hardware blocks.

Wei, Shijia↗

Prompt Gamma Ray Analysis of Soil Samples

Neutron moderation effects were measured in bulk soil samples through prompt gamma ray measurements from water and benzene contaminated soil samples using 14 MeV neutron inelastic scattering. The prompt gamma rays were measured using a cylindrical 76 mm x 76 mm (diameter x height) LaBr{sub 3}:Ce detector. Since neutron moderation effects strongly depend upon hydrogen concentration of the sample, for comparison purposes, moderation effects were studied from samples containing different hydrogen concentrations. The soil samples with different hydrogen concentration were prepared by mixing soil with water as well as benzene in different weight proportions. Then, the effects of increasing water and benzene concentrations on the yields of hydrogen, carbon and silicon prompt gamma rays were measured. Moderation effects are more pronounced in soil samples mixed with water as compared to those from soil samples mixed with benzene. This is due to the fact that benzene contaminated soil samples have about 30% less hydrogen concentration by weight than the water contaminated soil samples. Results of the study will be presented. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SNS Sample Activation Calculator Experimental Validation - Paper 79

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) uses the Sample Activation Calculator (SAC) to calculate the activation of all samples after they have been exposed to the neutron beam in any of the neutron scattering instruments at the SNS beamlines. The SAC web page takes user inputs (choice of beamline, the mass, composition and area of the sample, irradiation time, decay time, etc.) and then calculates the activation for the sample. In recent years, the SAC has been incorporated into the user proposal and sample handling process, and beamline scientists and users have noticed discrepancies in the predicted activation of their samples. As a part of the Neutronics Analysis Group at SNS, we were tasked with validating the SAC tool and diagnosing the origin of the discrepancies seen by the beamline scientists and the users. In the process of performing these measurements, we additionally found that several aspects of realistic neutron scattering operations, and thus sample exposure, cannot be captured with current configuration of the SAC tool. Typical neutron scattering samples are irradiated for multiple periods with different neutron energy ranges; the realities of accelerator operation indicate that not all of those periods will be at the same accelerator power, and scattering samples are frequently not fully illuminated by the well-collimated neutron beam. Finally, we will explore whether neutron beams, as modified by neutron guide systems with energy-dependent intensity modifications and by precise energy-band defining mechanical choppers, are suitably analyzed with multigroup cross section libraries in which there are typically very few energy groups below one eV. (authors)

43 PARTICLE ACCELERATORS↗

WHOLESCALE Catalog of Rock Samples at San Emidio Nevada collected in January 2021

This submission contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.

15 GEOTHERMAL ENERGY↗

Integrated sample-handling and mounting system for fixed-target serial synchrotron crystallography

Serial synchrotron crystallography (SSX) is enabling the efficient use of small crystals for structure–function studies of biomolecules and for drug discovery. An integrated SSX system has been developed comprising ultralow background-scatter sample holders suitable for room and cryogenic temperature crystallographic data collection, a sample-loading station and a humid `gloveless' glovebox. The sample holders incorporate thin-film supports with a variety of designs optimized for different crystal-loading challenges. These holders facilitate the dispersion of crystals and the removal of excess liquid, can be cooled at extremely high rates, generate little background scatter, allow data collection over >90° of oscillation without obstruction or the risk of generating saturating Bragg peaks, are compatible with existing infrastructure for high-throughput cryocrystallography and are reusable. The sample-loading station allows sample preparation and loading onto the support film, the application of time-varying suction for optimal removal of excess liquid, crystal repositioning and cryoprotection, and the application of sealing films for room-temperature data collection, all in a controlled-humidity environment. The humid glovebox allows microscope observation of the sample-loading station and crystallization trays while maintaining near-saturating humidities that further minimize the risks of sample dehydration and damage, and maximize working times. This integrated system addresses common problems in obtaining properly dispersed, properly hydrated and isomorphous microcrystals for fixed-orientation and oscillation data collection. Its ease of use, flexibility and optimized performance make it attractive not just for SSX but also for single-crystal and few-crystal data collection. Fundamental concepts that are important in achieving desired crystal distributions on a sample holder via time-varying suction-induced liquid flows are also discussed.

47 OTHER INSTRUMENTATION↗

EMP Attachment 1: DOE-SC PNNL Site Sampling and Analysis Plan

This Sampling and Analysis Plan (SAP) is written for the radiological environmental air surveillance program for the DOE-SC PNNL Site, Richland Washington. It provides the requirements for planning sampling events, and the requirements imposed on the analytical laboratory analyzing the air samples. The actual air sampling process is in procedure EPRP-AIR-029. The rationale for analyte selection, media, and sampling site location has been vetted through the data quality objectives (DQO) process (Barnett et al. 2010). The results from the DQO process have been reviewed and approved by the Washington State Department of Health. The DQO process (Barnett et al. 2010) identified seven specific radionuclides for analysis along with the need for gross alpha and gross beta radiological analyses. The analytes are 241<\sup>Am, 243<\sup>Am, 244<\sup>Cm, 60<\sup>Co, 238<\sup>Pu, 239<\sup>Pu, and 233<\sup>U. The report also determined that air samples for particulates are the only sample matrix required for the monitoring program. These samples are collected on 47-mm glass-fiber filters.

54 ENVIRONMENTAL SCIENCES↗

Underground Test Area Calendar Year 2014 Annual Sampling Analysis Report Nevada National Security Site, Nevada, Revision 0

This report presents the analytical data for the 2014 fiscal year (FY) and calendar year (CY) (October 1, 2013, through December 31, 2014), and an evaluation of the data to ensure that the Sampling Plan’s objectives are met. In addition to samples collected and analyzed for the Sampling Plan, some NNSS wells are monitored by NNSA/NFO to demonstrate compliance with State-issued water discharge permits; with protection of groundwater from ongoing radiological waste disposal activities (compliance wells); and to demonstrate that the onsite drinking water supply is below SDWA maximum contaminant levels (MCLs) (public water system [PWS] wells). While not all sampled locations are required by the Sampling Plan, these samples are relevant to its objectives and are therefore presented herein for completeness purposes. Special investigations that took place in 2014 that are relevant to the Sampling Plan are also presented. This is the first annual report released to support Sampling Plan implementation.

54 ENVIRONMENTAL SCIENCES↗

How to avoid multiple scattering in strongly scattering SANS and USANS samples

Small Angle Neutron Scattering (SANS) and Ultra Small Angle Neutron Scattering (USANS) are the only available experimental techniques to provide seamless non-destructive measurements of the geometry of the accessible and inaccessible pore structure of rocks from sub-nanopore size to the scale of macropores. They have therefore become the measurement of choice for tight reservoir rocks such as organic rich shales. A simplifying assumption in the analysis is, however, that during the path of neutrons through the sample each neutron is only scattered once. Shales are samples with a high scattering power and Multiple Scattering (MS) may occur which requires special modelling for deconvolution of the results. The approach to avoid MS is to simply reduce the sample thickness to <0.15–0.5 mm. Here, in this work, we present a systematic method on wavelength selection and preparation of samples to optimise extraction of microstructural data and minimise parasitic errors. Experimentally measured SAS transmission (TSAS) values are used as a practical criterion for estimation of the extent of MS. Generous beamtime allocations allowed robust testing revealing that sample thicknesses can be twice as thick as predicted using the standard protocol. Analysing thicker samples is particularly beneficial for statistically relevant characterisation of heterogeneous samples making the new protocol the method of choice for such samples.

(U)SANS↗

Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗