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Sub-pixel Tomographic Methods for Characterizing the Solid Architecture of Foams.

Paula Cimavilla-Román¹, Saúl Pérez-Tamarit¹, Suset Barroso-Solares^1,2

¹Cellular Materials Laboratory (CellMat), Condensed Matter Physics Department, University of Valladolid, Paseo Belen 7, Valladolid47011, Spain.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|March 18, 2022

Summary

This summary is machine-generated.

New methods analyze solid mass distribution in foams using X-ray tomography under sub-pixel conditions. Deconvolution methods show high accuracy, outperforming segmentation approaches for foam analysis.

Keywords:

3D analysis cellular structure foams characterization fraction of mass in the struts microtomography sub-pixel

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Area of Science:

Materials Science
Imaging Science

Background:

X-ray tomography is crucial for characterizing foam structures.
Sub-pixel conditions pose challenges for accurate solid mass distribution analysis in foams.

Purpose of the Study:

To develop and validate independent analysis methods for determining solid mass distribution in foams.
To assess method accuracy under sub-pixel conditions using X-ray tomography.

Main Methods:

Development of three independent analysis methods for X-ray tomography data.
Validation against high-resolution tomography results for foam samples.
Comparison of deconvolution and segmentation-based approaches.

Main Results:

Foams exhibited varied solid mass distributions, from high strut mass fractions (near 0.6) to low (under 0.15).
Deconvolution method closely matched high-resolution characterization results.
Segmentation methods showed higher deviations but allowed analysis of strut heterogeneity.

Conclusions:

The developed deconvolution method accurately quantifies solid mass distribution in foams under sub-pixel conditions.
Segmentation-based methods offer insights into heterogeneity but require normalization and show lower accuracy.
Accurate foam characterization is achievable even with limited resolution.