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Abstract: Natural marine sediments are heterogeneous with respect to sediment-physical properties, and have a wide range in composition and structures. For many years, sediment-physical characterization has relied primarily on laboratory experiments. However, the investigation of small-(grain-)scale sedimentary structures, which appear to control many sediment (re-)depositional and emplacement mechanisms, requires new analytical methods. Here, we test high-resolution X-ray synchrotron micro-tomography (μCT) to qualitatively and quantitatively investigate structural differences, in 3D, between two lithological end-member types of marine sediments: a coarse-grained, sandy sediment and a fine-grained, silty-clay sediment. Our results show clear compositional and structural differences between the two end-members, as well as between samples taken from the same lithological unit. These differences can be attributed partly to different sediment types, that is, coarse-versus fine-grained sediments, but also reveal a dependency on the sedimentation regime. We find that pore space distribution is highly spatially variable, even down to a sub-millimeter scale. Such high variability in porosity would be missed by standard geotechnical experiments, which only provide information averaged over far larger sediment samples. The identification of small-(grain-)scale changes in pore space, however, directly impacts sediment properties such as permeability, which in turn is crucial for the understanding of geological processes such as fluid flow and storage capacity of sediments and assessing hazards such as the preconditioning of submerged slopes to collapse. Our results therefore demonstrate the potential of μCT to investigate the internal structure of natural sediments, obtaining information that is not resolved or lost in data acquired through other analytical methods.

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Abstract: gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate x-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and x-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and x-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT.

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Abstract: The aqueous Al-ion battery potentially has improved safety and environmental advantages over the incumbent lithium-ion technology [1,2]. Using 3D carbon-felt matrix electrodes, the performance of these batteries can be optimised. Typically, the electrodes are compressed to improve electrical conductivity by improving contact between adjacent fibres, which also closes the electrolyte pores, reducing the ionic diffusivity [3]. In this work, we set out to understand the role of electrode compression on the interplay between these key performance parameters for the aq. Al-ion battery. Synchrotron X-ray computed tomography was used to examine the 3D morphology of the carbon felt electrodes under 11 different compression ratios [4]. A bespoke in-situ tensile/compression rig was used to measure displacement and loading for three electrode types: raw carbon felt, positive electrodes loaded with a copper hexacyanoferrate ink and negative electrodes loaded with TiO2 anatase powder. Data was acquired using two voxel sizes (330nm, and 540nm) to compare the effects of spatial resolution and imaged volume size on subsequent image analysis. The tomograms were reconstructed using Paganin phase retrieval, to improve the contrast of the weakly attenuating carbon, and a filtered back projection algorithm. A U-net convolutional neural network was trained on uncompressed, fully compressed and partially compressed (three volumes) data, and then used to fully segment all tomograms. A high-throughput, image-based model was then used to analyse how compression affects the porosity, tortuosity, volume-specific area, ionic diffusivity, and electrical conduction of the electrodes. A finite differences-based model was used to solve the equilibrium partial differential equations directly on the voxel datasets, with no additional regularisation [5]. The heterogeneity of the electrode samples is quantified by comparing the effect of representative elementary volume on the value of the computed parameters [6]. Finally, aq. Al-ion batteries are manufactured using the predicted optimal compression ratio and compared to the simulated results. This is the first in-situ study of the compression effect on the aqueous aluminium-ion battery, and the largest XCT-based modelling study known to the authors (99 XCT tomograms). This work aims to improve the understanding of the effect of manufacturing parameters on the aqueous Al-ion battery and other similar batteries, and ultimately, its performance.

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Abstract: Understanding when gravel moves in river beds is essential for a range of different applications but is still surprisingly hard to predict. Here we consider how our ability to predict critical shear stress (τc) is being improved by recent advances in two areas: (1) identifying the onset of bedload transport; and (2) quantifying grain-scale gravel bed structure. This paper addresses these areas through both an in-depth review and a comparison of new datasets of gravel structure collected using three different methods. We focus on advances in these two areas because of the need to understand how the conditions for sediment entrainment vary spatially and temporally, and because spatial and temporal changes in grain-scale structure are likely to be a major driver of changes in τc. We use data collected from a small gravel-bed stream using direct field-based measurements, terrestrial laser scanning (TLS) and computed tomography (CT) scanning, which is the first time that these methods have been directly compared. Using each method, we measure structure-relevant metrics including grain size distribution, grain protrusion and fine matrix content. We find that all three methods produce consistent measures of grain size, but that there is less agreement between measurements of grain protrusion and fine matrix content.

Open Access

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Abstract: The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source (Didcot, U.K.) implements a correlative approach to the dynamic study of materials based on concurrent analysis of identical sample locations using complementary X-ray modalities to reveal structural detail at various length scales. Namely, the underlying beamline principle and its practical implementation allow the collocation of chosen regions within the sample and their interrogation using real-space imaging (radiography and tomography) and reciprocal space scattering (diffraction). The switching between the two principal modes is made smooth and rapid by design, so that the data collected is interlaced to obtain near-simultaneous multimodal characterization. Different specific photon energies are used for each mode, and the interlacing of acquisition steps allows conducting static and dynamic experiments. Building on the demonstrated realization of this state-of-the-art approach requires further refining of the experimental practice, namely, the methods for gauge volume collocation under different modes of beam–sample interaction. To address this challenge, experiments were conducted at DIAD devoted to the study of human dental enamel, a hierarchical structure composed of hydroxyapatite mineral nanocrystals, as a static sample previously affected by dental caries (tooth decay) as well as under dynamic conditions simulating the process of acid demineralization. Collocation and correlation were achieved between WAXS (wide-angle X-ray scattering), 2D (radiographic), and 3D (tomographic) imaging. While X-ray imaging in 2D or 3D modes reveals real-space details of the sample microstructure, X-ray scattering data for each gauge volume provided statistical nanoscale and ultrastructural polycrystal reciprocal-space information such as phase and preferred orientation (texture). Careful registration of the gauge volume positions recorded during the scans allowed direct covisualization of the data from two modalities. Diffraction gauge volumes were identified and visualized within the tomographic data sets, revealing the underlying local information to support the interpretation of the diffraction patterns. The present implementation of the 4D microscopy paradigm allowed following the progression of demineralization and its correlation with time-dependent WAXS pattern evolution in an approach that is transferable to other material systems.