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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.

Open Access

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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: Understanding when gravel moves in river beds is essential for a range of different applications, but is still surprisingly hard to predict. The critical shear stress at which a grain will move depends on its relative size and structure within the bed, and spatial and temporal changes in grain-scale structure are likely to be a major driver of changes in critical shear stress. Consequently grain-structure metrics such as protrusion, pivot angle and contact with any surrounding fine grained matrix are used as parameters in models to predict critical shear stress, and so there is an increasing demand for measurements of these parameters in order to improve our predictive ability. However, we do not have established methods for measuring these parameters, nor do we know whether different methods provide consistent results. Here we present and compare new datasets of sediment structure metrics collected from eight locations in a small gravel-bed stream using three different methods: direct field-based measurements, terrestrial laser scanning (TLS), and computed tomography (CT) scanning. Using each method, we measure metrics including grain size distribution, grain protrusion and fine matrix content. We find that distributions of grain size are consistent between field-based and TLS data, but smaller in CT data. All three methods produce similar distributions of protrusion relative to grain size. There is also some consistency between field and CT measures of fine-grained matrix. However, the identification of similarity also depends on the type of analysis, and an alternative analysis shows less similarity in protrusion and fine-grained matrix between the different methods. Of the three methods, TLS-based approaches have potential to be most easily applied, and our analysis suggests that for grain-size and protrusion they perform as well as the alternative methods. However, they cannot currently be used for measuring fine-grained matrix content.

Open Access

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Abstract: Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently investigated using X-ray computed tomography (XCT). Image segmentation is an important data processing step for this type of research. However, it is often time consuming, computing resource-intensive, operator-dependent, and tailored for each XCT dataset due to differences in greyscale contrast. In this paper, an investigation is carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron XCT images of CH4-bearing sand during hydrate formation, and extract porosity and CH4 gas saturation. Three U-Net deployments previously untried for this task are assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, a 2D U-Net application with interactive corrections. U-Nets are trained using small, targeted hand-annotated datasets to reduce operator time. It was found that the segmentation accuracy of all three methods surpass mainstream watershed and thresholding techniques. Accuracy slightly reduces in low-contrast data, which affects volume fraction measurements, but errors are small compared with gravimetric methods. Moreover, U-Net models trained on low-contrast images can be used to segment higher-contrast datasets, without further training. This demonstrates model portability, which can expedite the segmentation of large datasets over short timespans.

Open Access

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Abstract: The DIAD beamline for Dual Imaging and Diffraction at Diamond Light Source has opted to use an industrial robot to position its Dectris Pilatus 2M CdTe diffraction detector. This setup was chosen to enable flexible positioning of the detector in a quarter-sphere around the sample position whilst reliably holding the large weight of 139 kg of detector, detector mount and cabling in a stable position. Metrology measurements showed that the detector can be positioned with a linear repeatability of <19.7 µm and a rotational repeatability of <16.3 µrad. The detector position stays stable for a 12 h period with <10.1 µm of movement for linear displacement and <3.8 µrad for rotational displacement. X-ray diffraction from calibration samples confirmed that the robot is sufficiently stable to resolve lattice d-spacings within the instrumental broadening given by detector position and beam divergence.