Dominick P. Guida, Alyssa M. Stavola, Andrew Chihpin Chuang, John S. Okasinski, Matthew T. Wendling, Xiaotong H. Chadderdon and Joshua W. Gallaway*,
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引用次数: 0
Abstract
High-resolution X-ray computed tomography (CT) has become an invaluable tool in battery research for its ability to probe phase distributions in sealed samples. The Cartesian coordinates used in describing the CT image stack are not appropriate for understanding radial dependencies, like that seen in bobbin-type batteries. The most prominent of these bobbin-type batteries is alkaline Zn–MnO2, which dominates the primary battery market. To understand material radial dependencies within these batteries, a method is presented to approximate the Cartesian coordinates of CT data into pseudo-cylindrical coordinates. This is important because radial volume fractions are the output of computational battery models, and this will allow the correlation of a battery model to CT data. A selection of 10 anodes inside Zn–MnO2 AA batteries are used to demonstrate the method. For these, the pseudo-radius is defined as the relative distance in the anode between the central current collecting pin and the separator. Using these anodes, we validate that this method results in averaged one-dimensional material profiles that, when compared to other methods, show a better quantitative match to individual local slices of the anodes in the polar θ-direction. The other methods tested are methods that average to an absolute center point based on either the pin or the separator. The pseudo-cylindrical method also corrects for slight asymmetries observed in bobbin-type batteries because the pin is often slightly off-center and the separator often has a noncircular shape.
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.