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Determining the uncertainty in microstructural parameters extracted from tomographic data

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Abstract

In the past few years, tomography has emerged as an indispensable tool to characterize and quantify the microstructural parameters of porous media, including lithium ion battery electrodes and separators, fuel cell parts, and supercapacitor electrodes, as well as electrolysis diaphragms. However, tomographic data often suffer from low image contrast between the pore space and the solid phase. As a result, data binarization is error-prone, which translates into uncertainties in the calculated parameters, such as porosity, tortuosity, or specific surface area. We systematically investigate this error propagation on a set of seven commercial negative lithium ion battery electrodes imaged with X-ray tomographic microscopy, and find that, for low porosity electrodes, the selection of binarization criteria can lead to uncertainties in the calculated tortuosity exceeding a factor of two. Our analysis shows that Otsu's interclass variance, which can be determined from a simple and computationally cheap analysis of the histogram of gray values, is a good predictive indicator of the expected uncertainty in the microstructural characteristics.

Graphical abstract: Determining the uncertainty in microstructural parameters extracted from tomographic data

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Publication details

The article was received on 13 Oct 2017, accepted on 11 Dec 2017 and first published on 11 Jan 2018


Article type: Paper
DOI: 10.1039/C7SE00498B
Citation: Sustainable Energy Fuels, 2018, Advance Article
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    Determining the uncertainty in microstructural parameters extracted from tomographic data

    P. Pietsch, M. Ebner, F. Marone, M. Stampanoni and V. Wood, Sustainable Energy Fuels, 2018, Advance Article , DOI: 10.1039/C7SE00498B

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