Special Issue "Novel Acquisition and Analysis Methods for X-ray Micro-CT in Materials Sciences"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Structure Analysis and Characterization".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Prof. Veerle Cnudde
Website
Guest Editor
Ghent University – PProGRess (UGCT), Belgium
Chairholder “Porous media Imaging techniques” - Department of Earth Sciences, Utrecht University, The Netherlands
Interests: pore scale processes; geomaterials; weathering; 4D imaging; chemical characterization
Prof. Matthieu N. Boone
Website
Guest Editor
Ghent University – Radiation Physics (UGCT), Belgium
Interests: X-ray micro-CT; phase contrast imaging; hyperspectral X-ray imaging; CT reconstruction

Special Issue Information

Dear Colleagues,

High-resolution X-ray computed tomography (micro-CT) is an increasingly popular tool in a wide variety of research areas. This includes materials sciences, where the nondestructive nature of the technique is of great importance, allowing, e.g., for imaging dynamic processes in situ or even operando, and to complement this information with data from other (destructive) techniques on the material after micro-CT characterization.

In recent years, micro-CT has evolved drastically, both in lab environments, such as at synchrotron facilities, and in terms of data acquisition, as well as data analysis. With this Special Issue, we want to create an overview of these recent developments applied on materials research. The focus is on the methodological perspective of any of the aspects of X-ray micro-CT imaging illustrated with an example in materials sciences, as well as on novel applications of recent innovations in micro-CT imaging.

Topics may include:

  • X-ray phase contrast and/or dark-field imaging;
  • Spectral and hyperspectral X-ray micro-CT;
  • Dual-energy X-ray imaging;
  • High-speed or dynamic X-ray micro-CT;
  • In-situ or operando X-ray imaging;
  • Micro-CT at novel X-ray sources;
  • 3D analysis;
  • Digital volume correlation;
  • Conversion to numerical models.

Prof. Veerle Cnudde
Prof. Matthieu N. Boone
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • X-ray micro-CT
  • Synchrotron imaging
  • Laboratory imaging
  • 3D analysis
  • Novel methods

Published Papers (5 papers)

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Research

Open AccessArticle
In-situ X-ray Differential Micro-tomography for Investigation of Water-weakening in Quasi-brittle Materials Subjected to Four-point Bending
Materials 2020, 13(6), 1405; https://doi.org/10.3390/ma13061405 - 20 Mar 2020
Abstract
Several methods, including X-ray radiography, have been developed for the investigation of the characteristics of water-saturated quasi-brittle materials. Here, the water content is one of the most important factors influencing their strength and fracture properties, in particular, as regards to porous building materials. [...] Read more.
Several methods, including X-ray radiography, have been developed for the investigation of the characteristics of water-saturated quasi-brittle materials. Here, the water content is one of the most important factors influencing their strength and fracture properties, in particular, as regards to porous building materials. However, the research concentrated on the three-dimensional fracture propagation characteristics is still significantly limited due to the problems encountered with the instrumentation requirements and the size effect. In this paper, we study the influence of the water content in a natural quasi-brittle material on its mechanical characteristics and fracture development during in-situ four-point bending by employing high-resolution X-ray differential micro-tomography. The cylindrical samples with a chevron notch were loaded using an in-house designed four-point bending loading device with the vertical orientation of the sample. The in-house designed modular micro-CT scanner was used for the visualisation of the specimen’s behaviour during the loading experiments. Several tomographic scans were performed throughout the force-displacement diagrams of the samples. The reconstructed 3D images were processed using an in-house developed differential tomography and digital volume correlation algorithms. The apparent reduction in the ultimate strength was observed due to the moisture content. The crack growth process in the water-saturated specimens was identified to be different in comparison with the dry specimens. Full article
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Open AccessArticle
Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow
Materials 2020, 13(6), 1397; https://doi.org/10.3390/ma13061397 - 19 Mar 2020
Abstract
Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution [...] Read more.
Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution versus sample size trade-off. Therefore, a super-resolution method using generative adversarial neural networks (GANs) was used to artificially improve the resolution. Firstly, the influence of resolution on pore network properties and single-phase, unsaturated, and two-phase flow was analysed to verify that pores and pore throats become larger on average and surface area decreases with worsening resolution. These observations are reflected in increasingly overestimated single-phase permeability, less moisture uptake at lower capillary pressures, and high residual oil fraction after waterflooding. Therefore, the super-resolution GANs were developed which take low (12 µm) resolution input and increase the resolution to 4 µm, which is compared to the expected high-resolution output. These results better predicted pore network properties and fluid flow properties despite the overestimation of porosity. Relevant small pores and pore surfaces are better resolved thus providing better estimates of unsaturated and two-phase flow which can be heavily influenced by flow along pore boundaries and through smaller pores. This study presents the second case in which GANs were applied to a super-resolution problem on geological materials, but it is the first one to apply it directly on raw CT images and to determine the actual impact of a super-resolution method on fluid predictions. Full article
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Open AccessArticle
Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites
Materials 2020, 13(4), 936; https://doi.org/10.3390/ma13040936 - 20 Feb 2020
Abstract
The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (µ-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on µ-CT) for [...] Read more.
The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (µ-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on µ-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of µ-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author’s view, both strategies present a novel and reliable ground for the segmentation of µ-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our “ground truth”, which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network. Full article
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Open AccessArticle
Quantification of Uncertainty and Best Practice in Computing Interfacial Curvature from Complex Pore Space Images
Materials 2019, 12(13), 2138; https://doi.org/10.3390/ma12132138 - 03 Jul 2019
Cited by 2
Abstract
Recent advances in high-resolution three-dimensional X-ray CT imaging have made it possible to visualize fluid configurations during multiphase displacement at the pore-scale. However, there is an inherited difficulty in image-based curvature measurements: the use of voxelized image data may introduce significant error, which [...] Read more.
Recent advances in high-resolution three-dimensional X-ray CT imaging have made it possible to visualize fluid configurations during multiphase displacement at the pore-scale. However, there is an inherited difficulty in image-based curvature measurements: the use of voxelized image data may introduce significant error, which has not—to date—been quantified. To find the best method to compute curvature from micro-CT images and quantify the likely error, we performed drainage and imbibition direct numerical simulations for an oil/water system on a bead pack and a Bentheimer sandstone. From the simulations, local fluid configurations and fluid pressures were obtained. We then investigated methods to compute curvature on the oil/water interface. The interface was defined in two ways; in one case the simulated interface with a sub-resolution smoothness was used, while the other was a smoothed interface extracted from synthetic segmented data based on the simulated phase distribution. The curvature computed on these surfaces was compared with that obtained from the simulated capillary pressure, which does not depend on the explicit consideration of the shape of the interface. As distinguished from previous studies which compared an average or peak curvature with the value derived from the measured macroscopic capillary pressure, our approach can also be used to study the pore-by-pore variation. This paper suggests the best method to compute curvature on images with a quantification of likely errors: local capillary pressures for each pore can be estimated to within 30% if the average radius of curvature is more than 6 times the image resolution, while the average capillary pressure can also be estimated to within 11% if the average radius of curvature is more than 10 times the image resolution. Full article
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Open AccessArticle
X-ray Computed Tomography for Characterization of Expanded Polystyrene (EPS) Foam
Materials 2019, 12(12), 1944; https://doi.org/10.3390/ma12121944 - 17 Jun 2019
Cited by 1
Abstract
Expanded polystyrene (EPS) foam is widely used in building and construction applications for thermal and acoustic insulation. This material is nearly transparent for X-rays, making it difficult to characterize its pore structure in 3D with X-ray tomography. Because of this difficulty, the pore [...] Read more.
Expanded polystyrene (EPS) foam is widely used in building and construction applications for thermal and acoustic insulation. This material is nearly transparent for X-rays, making it difficult to characterize its pore structure in 3D with X-ray tomography. Because of this difficulty, the pore network is often not investigated and is, thus, poorly known. Since this network controls different physical properties, such as the sound absorption, it is crucial to understand its overall structure. In this manuscript, we show how to reveal the pore network of EPS foams through the combination of high resolution X-ray tomography (micro-CT) and saturation techniques. The foams were saturated with CsCl-brine, which acts as a contrasting agent in X-ray micro-CT imaging. This allowed us to separate the beads, making up the foam, from the pore network. Based on the 3D micro-CT results, we were able to assess a representative elementary volume for the polystyrene, which allows for calculating the acoustical parameters from the Johnson–Champoux–Allard (JCA) model, the pore and bead size distribution. The 3D data was also used as input to simulate sound absorption curves. The parametric study showed that an increase in the bead size influenced the sound absorption of the material. We showed that, by doubling the diameter of beads, the absorption coefficient was doubled in certain ranges of frequency. Full article
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