Journal Article Controlling the Reaction Process in Operando STEM by Pixel Sub-Sampling Get access B Layla Mehdi, B Layla Mehdi Physical and Computational Science Directorate, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Andrew Stevens, Andrew Stevens National Security Directorate, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Libor Kovarik, Libor Kovarik Environmental Molecular Sciences Laboratory, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Andrey Liyu, Andrey Liyu Environmental Molecular Sciences Laboratory, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Bryan Stanfill, Bryan Stanfill National Security Directorate, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Sarah Reehl, Sarah Reehl National Security Directorate, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Lisa Bramer, Lisa Bramer National Security Directorate, PNNL, Richland, WA 99352, USA Search for other works by this author on: Oxford Academic Google Scholar Nigel D Browning Nigel D Browning Physical and Computational Science Directorate, PNNL, Richland, WA 99352, USAMaterials Science and Engineering, University of Washington, Seattle, WA 98195, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 23, Issue S1, 1 July 2017, Pages 98–99, https://doi.org/10.1017/S1431927617001179 Published: 04 August 2017
Data as three-dimensional rotations have application in computer science, kinematics, and materials sciences, among other areas. Estimating the central orientation from a sample of such data is an important problem, which is complicated by the fact that several different approaches exist for this, motivated by various geometrical and decision-theoretical considerations. However, little is known about how such estimators compare, especially on common distributions for location models with random rotations. We examine four location estimators, three of which are commonly found in different literatures and the fourth estimator (a projected median) is newly introduced. Our study unifies existing literature and provides a detailed numerical investigation of location estimators for three commonly used rotation distributions in statistics and materials science. While the data-generating model influences the best choice of an estimator, the proposed projected median emerges as an overall good performer, which can be suggested without particular distributional assumptions. We illustrate the estimators and our findings with data from a materials science study by approximating the central orientation of cubic crystals on the microsurface of a metal. Accompanying supplementary materials are available online.
Primary biological aerosol particles (PBAPs) are microscopic solids suspended in the atmosphere emitted by biological systems and play critical roles in the atmosphere and the atmosphere–biosphere system, impacting human health, climate, and the ecosystem function. Understanding the sources of PBAPs is necessary to decipher the mechanistic interactions between aerosols, climate, and other ecosystem components. However, the detection of specific PBAPs in complex ambient aerosol samples is challenging. We performed metabolomics analyses of pollen from three pollinating tree species and ambient samples collected during the peak pollination period of each species. Random Forest and sPLS-DA machine learning methods were employed to evaluate whether metabolic signatures of ambient samples can reveal the source of the main pollen particles present in the atmosphere. Our results suggest that atmospheric ecometabolomics techniques combined with sophisticated statistical methods can decipher the origin of abundant PBAPs from complex ambient samples. Developing complete libraries containing high-resolution metabolomic fingerprints of the major PBAPs present in the atmosphere would significantly advance future research to accurately understand the role of PBAPs in the atmosphere, ecosystems, and human health.
Subsampled image acquisition followed by image inpainting in a scanning transmission electron microscope is a novel approach to control dose and increase the image frame rate during experiments, thereby allowing independent control of the spatial and temporal dose envelope during image acquisition. Here, subsampled imaging is shown to permit precise in situ observations of the fundamental kinetic processes behind nucleation and growth of silver (Ag) nanoparticles from an aqueous solution. At high sampling-levels, nanoparticles can be observed with morphologies that are consistent with strong interface interactions, i.e., rafts and pillars, whereas at low sampling-levels, the particles exhibit regular spherical morphologies. The relative numbers of rafts/pillars and regular nanoparticles, their sizes, and their incubation times can be attributed to local changes in the molar concentration of the Ag ions in the aqueous solution; higher sampling-levels significantly increase the reactants in the vicinity of the window, leading to rapid supersaturation and the precipitation on the window surface. These precisely controlled kinetics highlight subsampled imaging as a method by which the driving force for nucleation and growth (i.e., the electron beam) can be disentangled from the spatial/temporal resolution of the observation in all in situ experiments, providing a pathway to identify and quantify the importance of individual kinetic factors behind nucleation and growth in a wide variety of complex materials systems and architectures.
This report summarizes and analyzes the data collected on a second test matrix of 42 low-activity waste (LAW) glass compositions intended to expand the LAW glass composition region over which glass property-composition models are valid. The 42 LAW glass compositions were selected using a statistical layered experimental design approach to explore a new glass composition region that overlaps and expands beyond the previously explored LAW glass composition region. The layered design of 42 LAW glasses consisted of 21 outer-layer glasses, 15 inner-layer glasses, a center glass (three replicates), and two glasses previously tested at another laboratory. The outer-layer glass compositions include extrema based on current projections of the LAW feeds and melter operating temperatures. One of the outer-layer compositions was eliminated due to the inability to form a glass. Therefore, only 41 glasses were analyzed. The analyses performed on these glasses include chemical composition (for target compositional verification), density, viscosity, electrical conductivity, crystal fraction, canister centerline cooling with crystal identification, the Product Consistency Test, sulfur solubility, and the Vapor Hydration Test. This report discusses the results obtained from this testing.
The U.S. Department of Energy's (DOE) Office of River Protection (ORP) requested Pacific Northwest National Laboratory (PNNL) to support the River Protection Project vitrification in an effort to support operations upon completion of startup activities (DOE 2012). This work was performed under the PNNL project titled "ORP Glass Support Work." One task of this project—Enhanced Hanford Waste Glass Models—is the subject of this report. A previous task focused on generating property-composition data and models for the Hanford site low-activity waste (LAW) glasses with lower waste loadings, which are relevant to the commissioning of the LAW vitrification facility. The current task has the long-term objective of expanding the Hanford site LAW glass database and property composition models for the balance of the Hanford site tank waste treatment and immobilization mission. During the balance of the mission, LAW glasses with higher waste loadings will be produced. This report presents the glass compositions and glass property data developed in Phase 2 of the enhanced Hanford LAW glass property data development effort. When this effort is complete, enhanced LAW glass property models will be developed. Section 1.1 summarizes the status of the LAW glass composition regions and waste loading constraints prior to the data development effort documented in this report. Section 1.2 summarizes the LAW Phase 2 glass composition region and test matrix. Section 1.3 documents the quality assurance program used in performing the work discussed in this report.
Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.
Survey agencies in the United States continue to move many map-based surveys from paper to handheld computers. With large highly diverse workforces, it is necessary to test software with a diverse population. The present work examines the performance of participants grouped by their level of spatial visualization. The participants were tested in either the field or in a fully immersive virtual environment. The methodology of the study is explained. The performance of the participants in the two environments is modeled with least squares regression. Results of the study are presented and discussed.
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