Results for “Data Collection” — Page 65

DOE OSTI2020

Genesis and Propagation of Fractal Structures During Photoelectrochemical Etching of n-Silicon

The genesis, propagation, and dimensions of fractal-etch patterns that form anodically on front- or back-illuminated n-Si(100) photoelectrodes in contact with 11.9 M NH 4 F (aqueous) have been investigated during either a linear potential sweep or a constant potential hold ( E = +6.0 V versus Ag/AgCl). Optical images collected in situ during electrochemical experiments reflected the location and underlying mechanism of initiation and propagation of the structures on the surface. X-ray photoelectron spectroscopic (XPS) data collected for samples emersed from the electrolyte at varied times provided detailed information about the chemistry of the surface during fractal etching. The fractal structure was strongly influenced by the orientation of the crystalline Si sample. Furthermore, the etch patterns were initially generated at points along the circumference of bubbles that formed upon immersion of n-Si(100) samples in the electrolyte, most likely due to the electrochemical and electronic isolation of areas beneath bubbles. XPS data showed the presence of a tensile-stressed silicon surface throughout the etching process as well as the presence of SiO x F y on the surface. The two-dimensional fractal dimension, D f,2D , of the patterns increased with etching time to a maximum observed value of D f,2D = 1.82. Promotion of fractal etching near etch masks that electrochemically and electronically isolated areas of the photoelectrode surface enabled the selective placement of highly branched structures at desired locations on an electrode surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Collective Analysis of Alpha Particle Losses Due to Self-Absorption by Mass Loading on Radioactive Particulate Glass Fiber Filters

In this study, we derived a relationship between filter mass loading and the percent loss during analysis using the mass loading data collected from six previous studies of self-absorption. Components of mass loading include particulate dust, radioactive particulates, and filter material. In a research report published in 1984, Higby calculated a minimum burial depth for an alpha particle to be lost due to absorption (100% loss) of about 3.7 mg/cm 2 based on calculations for the range of 239-Pu alpha particles in glass fiber filters. From there, Higby concluded that a correction factor of 0.85 assumes approximately 15% losses in the count rate of both alpha and beta particles. In 2000, Luetzelschwab et al. recommended assuming a 40% loss at a loading of 3.3 mg/cm 2 and a 28% loss for a loading of 2.3 mg/cm 2 which included the frontal face mass of the filter. More recently, the 100% losses due to absorption were reported to be in the 10 mg/cm 2 range. Presented here is a trinomial relationship method of relating percent loss due to self-absorption to filter mass loading, based on data reported by Higby, Luetzelschwab et al., Huang et al., Barnett et al., Smith et al., and Hogue et al. Under normal operating conditions at the stacks monitored by Effluent Management, the mass loading of sample filters averages 0.09 ± 0.12 (2s) mg/cm 2 (excluding negative values and outliers) and ranges from 0 mg/cm 2 to 0.24 mg/cm 2 . Based on current mass loading results for Effluent Management stack sample filters, the forced-zero trinomial relationship method estimated self-absorption losses of less than 5%. Because American National Standards Institute/Health Physics Society N13.1-2011 guidelines indicate a correction factor should be used when the penetration of radioactive material into the collection media or self-absorption of radiation by the material collected would reduce the count rate by more than 5%, it is possible continued application of a correction factor to the Effluent Management stack samples is no longer necessary. Nevertheless, continuing to assign a correction factor at the 5% threshold (i.e., 0.95) would be a conservative approach.

36 MATERIALS SCIENCE↗

DOE OSTI2021

The utility of Bayesian data reconciliation for separations

Data reconciliation methods for separation processes typically rely on classical statistical approaches to generate estimates of true mass flow rates from measurements. Knowledge regarding the uncertainty of these estimates has value in decision making, but is often not acquired. Bayesian approaches intrinsically quantify uncertainty; however, literature for Bayesian data reconciliation of separation processes is scarce. This publication outlines two Bayesian data reconciliation models and provides details for how the models were implemented for the BayesMassBal (V 1.0.0) software package written in R. To demonstrate the advantages of this approach for data reconciliation, the models were first applied to simulated data and then compared to a classical model through a Monte Carlo experiment. In this example, the Bayesian models were found to provide more accurate estimates of the simulated data, while also providing quantitative information on the estimate uncertainty. To demonstrate the use of the technique in a practical problem, the models were also applied to real data collected from a pilot-scale rare earth solvent extraction process. Here, this publication provides a small window into how Bayesian methods can be used for data reconciliation, but findings suggest Bayesian data reconciliation models for separation processes have distinct advantages over classical alternatives.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

The MATHUSLA test stand

The rate of muons from LHC pp collisions reaching the surface above the ATLAS interaction point is measured as a function of the ATLAS luminosity and compared with expected rates from decays of W and Z bosons and b- and c- quark jets. In addition, data collected during periods without beams circulating in the LHC provide a measurement of the background from cosmic ray inelastic backscattering that is compared to simulation predictions. Data were recorded during 2018 in a 2.5 × 2.5 × 6.5 m 3 active volume MATHUSLA test stand detector unit consisting of two scintillator planes, one at the top and one at the bottom, which defined the trigger, and six layers of RPCs between them, grouped into three ( x, y )-measuring layers separated by 1.74 m from each other. Triggers selecting both upward-going tracks and downward-going tracks were used.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Big Data Seismology

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Currently, Big Data Seismology is an emergent revolution involving the use of large, data-dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data-dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long-duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up-to-speed with how the Big Data revolution is advancing the field of seismology.

58 GEOSCIENCES↗

DOE OSTI2023

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

We present a new measurement of the positive muon magnetic anomaly, a μ ≡ ( g μ - 2 ) / 2 , from the Fermilab Muon g - 2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, ω ˜ p ′ , and of the anomalous precession frequency corrected for beam dynamics effects, ω a . From the ratio ω a / ω ˜ p ′ , together with precisely determined external parameters, we determine a μ = 116 592 057 ( 25 ) × 10 - 11 (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain a μ ( FNAL ) = 116 592 055 ( 24 ) × 10 - 11 (0.20 ppm). The new experimental world average is a μ ( exp ) = 116 592 059 ( 22 ) × 10 - 11 (0.19 ppm), which represents a factor of 2 improvement in precision.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗

DOE OSTI2025

2009 Interstate 95 Express Phase 1A Survey

# 2009 Interstate 95 Express Phase 1A Survey The 2009 Phase 1A Survey focused on commuters using Interstate 95 express lanes between downtown Miami and the Golden Glades Interchange in Florida. Survey participants answered questions about their trip's purpose, frequency, and mode as well as demographics such as their age, race/ethnicity, and income. It also asked if and why or why not they had used Interstate 95 before and/or after the installation of two high-occupancy toll lanes, if they were satisfied with the upgrade, and about their related perceptions of safety. ## Data Collection Agency The survey was conducted by and for the Florida Department of Transportation. ## Survey Methodology The survey was conducted online in May 2009. ## Survey Records, Data, and Documentation Survey records include 9,156 participants.