A casting method using contrast-enhanced diethylphthalate for micro-computed tomography of snow

Michael Lombardo,Martin Schneebeli,Henning Löwe

doi:10.1017/jog.2021.35

Abstract

AbstractCasting snow is necessary to prevent metamorphism and deformation prior to X-ray micro-computed tomography (μCT) imaging. Current methods are insufficient for large-scale field sampling of snow due to safety considerations associated with the casting medium and/or lengthy sample preparation times. Here, a casting method using contrast-enhanced diethylphthalate (DEP) for μCT of snow is presented. The X-ray contrast of DEP is enhanced with barium titanate nanoparticles (BaTiO3) and iodine (I2). A partially unsupervised, three-phase segmentation method utilizing traditional Gaussian smoothing followed by a three-step process to address transition voxels is also presented. Synthetic images derived from real snow samples are used to evaluate the segmentation method with various configurations of trapped air bubbles. Real snow samples spanning a range of specific surface areas (SSAs) (8–28 m2kg−1) and densities (135–463 kg m−3) are used to assess the performance of the segmentation method on real, cast samples. The method yields SSA, density and correlation length errors of less than 10% for synthetic images with air bubble surface areas less than 333 m−1per sample volume for eight of the nine snow samples. For eight of the nine cast samples, the method yields errors of less than 10% for all three parameters.

Highlights

The microstructure of snow can be used to calculate physical properties such as mechanical strength (Hagenmuller and others, 2014), thermal conductivity (Kaempfer and others, 2005), albedo (Haussener and others, 2012; Ishimoto and others, 2018) and permeability (Courville and others, 2010)
We demonstrate a casting method using contrast-enhanced diethylphthalate for μCT of snow
A 1:1 line is provided in each figure as a visual guide and a zoomed in view is provided in Figure 8a to better differentiate the samples with similar specific surface areas (SSAs)

Summary

Introduction

The microstructure of snow can be used to calculate physical properties such as mechanical strength (Hagenmuller and others, 2014), thermal conductivity (Kaempfer and others, 2005), albedo (Haussener and others, 2012; Ishimoto and others, 2018) and permeability (Courville and others, 2010). SSA and density can be used to calculate thermal conductivity and air permeability (Calonne and others, 2012), while the correlation length can be used for determining the anisotropy of permittivity (Leinss and others, 2020) or thermal conductivity (Löwe and others, 2013), and for forcing microwave models (Wiesmann and Mätzler, 1999). Several methods such as manual density cutting and optical techniques (Matzl and Schneebeli, 2006; Gallet and others, 2009; Montpetit and others, 2012) can be used to obtain the density and SSA, respectively, but cannot provide the correlation length. As serial sectioning is prohibitively slow (ten sections per hour (Perla and others, 1986)), μCT is the most practical technique for obtaining the complete snow microstructure

Methods

Results

Discussion

Conclusion