Structural Information on Supramolecular Copper(II) β-Diketonate Complexes from Atomic Force Microscopy and Analytical Ultracentrifugation
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Comprehensive analysis of the molecular weight distribution of raw and catalytic fast pyrolysis oils remains a key technical hurdle to understanding oil quality and multiple methods may be necessary to accurately represent all components present.
Multimodal in situ experimental data probing a complex reaction have been integrated via a multi-level analysis involving non-negative matrix factorization and correlation analysis. This strategy can be applied generally to multimodal experiments.
Here, we assess the error in three treatments of a critical electron beam diagnostic for laser-plasma acceleration (LPA) experiments: a permanent magnet electron spectrometer. Since LPA electron spectrometers are often difficult to calibrate due to the scarcity of well-characterized, tunable electron beam sources in the appropriate energy range and the mechanical complexity of electron spectrometers, the standard of calibration is Hall probe measurements. We first compare the electron spectrometer performance between SIMION calculations and the Hall probe measurements. We find up to a 60% (<8%) error in determining the absolute energy for electrons below (above) 80 MeV when using the SIMION-modeled magnetic field vs the measured magnetic field due to SIMION overpredicting fringe field strength. The difference in spectrometer energy resolution is within ∼10% between the modeled and measured fields. We then assess a simple block model, which is commonly used. We find that the simple block model presented here sufficiently captures the predicted as-built spectrometer performance for any application provided that the user can tolerate uncertainties on absolute energy determination up to 5% and errors on energy resolution up to 1%.
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Carbohydrates make up a major portion of biomass samples. These carbohydrates are polysaccharides constructed primarily of glucose, xylose, arabinose, galactose, and mannose monomeric subunits. One such polysaccharide is starch, which is present in certain types of biomass. Starch will contribute to the overall glucose measurement when it breaks into the glucose subunit. Starch is often a more readily accessible source of glucose than structurally bound glucose polymers. Due to the different chemical nature of the glucan species, it is often advantageous to determine the amount of cellulosic glucose separately from the starch glucan.
An internal audit was performed in ALAB 8/25/21 to the NELAC 2016 Standard requirements using the NELAC 2016 checklist (TNI 2016 CSDP-Volume1Checklist-Master-2-15-18-v9). The scope of the internal audit was a document traceability of the environmental testing in LRF-00118, LRF00095, and LRF 00133 performed in ALAB. The internal audit resulted in five deficiencies and two comments. The deficiencies were addressed using the ALAB non-conformance reporting system, see ALAB NCRs 10002742-10002746. FRM-5100 Management Observation and NCRs are attached.
The modernization of SAMMY continued with consolidation of access to covariance information for adjusted parameters and data in SAMMY. This consolidation allowed for removal of many scratch files. In addition, work was initiated to make the 0K cross section calculation more modular and less dependent on SAMMY global parameters. An initial application programming interface (API) was added to expose cross sections (including resolution broadening) generated by SAMMY to external fitting routines. The processing for thermal moderators in AMPX was updated for selected moderators for which the generated grid was not fine enough. Updated libraries were generated for SCALE. In addition, work continued to fully support new Evaluated Nuclear Data File (ENDF) formats, including the Generalized Nuclear Database Structure (GNDS) in AMPX.
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These experiments were performed by Bramblett and Czirr at UCRL (i.e., LLNL) in the late 1960s through the 1970s and evaluated by Mark Lee (LFO, retired) for the ICSBEP Handbook. Dimensions, geometry, and benchmark RSFR values are taken from the Handbook. All sample calculational results used COG 11.3 with ENDF/B-VIII.0 cross-sections. A sample input listing is provided in Appendix A.
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This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.
Overview of Project and Progress
This is a Battery Innovation Summit Presentation with poster and oral presentations.
Sharing EBR-II driver fuel cladding hull data with General Atomics under NDA No. 20NDA155. Data share is needed to advance discussions regarding process technologies to decontaminate separated cladding hulls
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