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Abstract: The addition of nano-fillers has been widely proposed as a method to enhance the dielectric properties of high voltage polymeric insulation, though there are mixed reports in the literature. Here the potential of silica nano-particles to extend the time to failure specifically through resistance to electrical tree growth in epoxy resin is determined. The benefit of silane treating the nano-particles before compounding is clearly established with regard to slowing tree growth and subsequent time to failure. The growth of trees in needle-plane samples is measured in the laboratory with loadings of 1, 3 and 5 wt% nano-filler. In all cases the average times to failure are extended, but silane treatment of the nano-particles prior to compounding yields much superior results. The emergence of a pronounced inception time before tree growth is also noted for the higher-filled, silane-treated cases. The average time to failure of silane-treated 5 wt% filled material was 28 times that of the unfilled resin. The improvement in performance between the nanocomposites with untreated and treated fillers is attributed to fewer agglomerations and improved dispersion of the filler in the treated cases. Measurements of Partial Discharge (PD) indicated significant differences in PD patterns during the growth of trees in the treated and untreated cases. This distinction may provide a quality control method for monitoring materials. In particular, long periods in which PDs were not measured were observed in the silane-treated cases. Visual imaging of tree growth in the unfilled material allowed the changing nature of the tree from fine to tree to dark tree to be observed as it grew. Corresponding PD measurements suggest the dark tree is gradually becoming conductive, and that growth of maximum PD measured is dependent on the relative rates of the growth of the tree and its carbonization. X-ray computer tomography identified significant differences in average tree channel diameters (a reduction from 2.8 µm to 2.0 µm for 1 wt% and 3 wt% cases). This implies that in addition to tree length changes, evaporated tree volumes also change and may explain the change in partial discharge characteristics observed.

Open Access

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Abstract: Colonic crypts are tubular glands that multiply through a symmetric branching process called crypt fission. During the early stages of colorectal cancer, the normal fission process is disturbed, leading to asymmetrical branching or budding. The challenging shapes of the budding crypts make it difficult to prepare paraffin sections for conventional histology, resulting in colonic cross sections with crypts that are only partially visible. To study crypt budding in situ and in three dimensions (3D), we employ X-ray micro-computed tomography to image intact colons, and a new method we developed (3D cyclorama) to digitally unroll them. Here, we present, verify and validate our ‘3D cyclorama’ method that digitally unrolls deformed tubes of non-uniform thickness. It employs principles from electrostatics to reform the tube into a series of onion-like surfaces, which are mapped onto planar panoramic views. This enables the study of features extending over several layers of the tube’s depth, demonstrated here by two case studies: (i) microvilli in the human placenta and (ii) 3D-printed adhesive films for drug delivery. Our 3D cyclorama method can provide novel insights into a wide spectrum of applications where digital unrolling or flattening is necessary, including long bones, teeth roots and ancient scrolls.

Open Access

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Abstract: Injection of CO2 into shale reservoirs to enhance gas recovery and simultaneously sequester greenhouse gases is a potential contributor towards the carbon-neutral target. It offers a low-carbon, low-cost, low-waste and large-scale solution during the energy transition period. A precondition to efficient gas storage and flow is a sound understanding of how the shale's micro-scale impacts on these phenomena. However, the heterogeneous and complex nature of shales limits the understanding of microstructure and pore systems, making feasibility analysis challenging. This study qualitatively and quantitatively investigates the Bowland shale microstructure in 3D at five length scales: artificial fractures at 10–100 μm scale, matrix fabric at 1–10 μm-scale, individual mineral grains and organic matter particles at 100 nm–1 μm scale, macropores and micro-cracks at 10–100 nm scale and organic matter and mineral pores at 1–10 nm-scale. For each feature, the volume fraction variations along the bedding normal orientation, the fractal dimensions and the degrees of anisotropy were analysed at all corresponding scales for a multi-scale heterogeneity analysis. The results are combined with other bulk laboratory measurements, including supercritical CO2 and CH4 adsorption at reservoir conditions, pressure-dependent permeability and nitrogen adsorption pore size distribution, to perform a comprehensive analysis on the storage space and flow pathways. A cross-scale pore size distribution, ranging from 2 nm to 3 μm, was calculated with quantified microstructure. The cumulative porosity is calculated to be 8%. The cumulative surface area is 17.6 m2 g−1. A model of CH4 and CO2 flow pathways and storage with quantified microstructure is presented and discussed. The feasibility of simultaneously enhanced gas recovery and subsurface CO2 storage in Bowland shale, the largest shale gas potential formation in the UK, was assessed based using multi-scale microstructure analysis. The potential is estimated to store 19.0–21.2 Gt CO2 as free molecules, together with 18.3–28.5 Gt CO2 adsorbed onto pore surfaces, implying a theoretical maximum of 47.5–49.5 Gt carbon storage in the current estimate of 38 trillion cubic metres (∼1300 trillion cubic feet) of Bowland shale. Simple estimates suggest 6.0–15.8 Gt CO2 may be stored in practice.

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Abstract: Digital volume correlation (DVC) in combination with high-resolution micro-computed tomography (microCT) imaging and in situ mechanical testing is gaining popularity for quantifying 3D full-field strains in bone and biomaterials. However, traditional in situ time-lapsed (i.e., interrupted) mechanical testing cannot fully capture the dynamic strain mechanisms in viscoelastic biological materials. The aim of this study was to investigate the time-resolved deformation of bone structures and analogues via continuous in situ synchrotron-radiation microCT (SR-microCT) compression and DVC to gain a better insight into their structure-function relationships. Fast SR-microCT imaging enabled the deformation behaviour to be captured with high temporal and spatial resolution. Time-resolved DVC highlighted the relationship between local strains and damage initiation and progression in the different biostructures undergoing plastic deformation, bending and/or buckling of their main microstructural elements. The results showed that SR-microCT continuous mechanical testing complemented and enhanced the information obtained from time-lapsed testing, which may underestimate the 3D strain magnitudes as a result of the stress relaxation occurring in between steps before image acquisition in porous biomaterials. Altogether, the findings of this study highlight the importance of time-resolved in situ experiments to fully characterise the time-dependent mechanical behaviour of biological tissues and biomaterials and to further explore their micromechanics under physiologically relevant conditions.

Open Access

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Abstract: Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.