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Abstract: In this contribution, we introduce a simple approach to quickly estimate the environment-induced crack velocity (CV) as a function of the calculated applied stress intensity factor (K) developed during the slow strain rate testing of aluminum alloys exposed to aqueous or humid air-type environments. The CV-K behavior for a commercial aluminum-magnesium alloy, AA5083-H131, sensitized and pre-exposed to a 0.6 m NaCl solution has been estimated from slow strain rate test data. The predicted threshold K and crack velocities match recently published data for the same alloy in similarly sensitized conditions where the CV-K data were obtained using state-of-the-art fracture mechanics-based testing.

Open Access

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Abstract: Bioglass® was the first material to form a stable chemical bond with human tissue. Since its discovery, a key goal was to produce three-dimensional (3D) porous scaffolds which can host and guide tissue repair, in particular, regeneration of long bone defects resulting from trauma or disease. Producing 3D scaffolds from bioactive glasses is challenging because of crystallization events that occur while the glass particles densify at high temperatures. Bioactive glasses such as the 13–93 composition can be sintered by viscous flow sintering at temperatures above the glass transition onset (Tg) and below the crystallization temperature (Tc). There is, however, very little literature on viscous flow sintering of bioactive glasses, and none of which focuses on the viscous flow sintering of glass scaffolds in four dimensions (4D) (3D + time). Here, high-resolution synchrotron-sourced X-ray computed tomography (sCT) was used to capture and quantify viscous flow sintering of an additively manufactured bioactive glass scaffold in 4D. In situ sCT allowed the simultaneous quantification of individual particle (local) structural changes and the scaffold's (global) dimensional changes during the sintering cycle. Densification, calculated as change in surface area, occurred in three distinct stages, confirming classical sintering theory. Importantly, our observations show for the first time that the local and global contributions to densification are significantly different at each of these stages: local sintering dominates stages 1 and 2, while global sintering is more prevalent in stage 3. During stage 1, small particles coalesced to larger particles because of their higher driving force for viscous flow at lower temperatures, while large angular particles became less faceted (angular regions had a local small radius of curvature). A transition in the rate of sintering was then observed in which significant viscous flow occurred, resulting in large reduction of surface area, total strut volume, and interparticle porosity because the majority of the printed particles coalesced to become continuous struts (stage 2). Transition from stage 2 to stage 3 was distinctly obvious when interparticle pores became isolated and closed, while the sintering rate significantly reduced. During stage 3, at the local scale, isolated pores either became more spherical or reduced in size and disappeared depending on their initial morphology. During stage 3, sintering of the scaffolds continued at the strut level, with interstrut porosity reducing, while globally the strut diameter increased in size, suggesting overall shrinkage of the scaffold with the flow of material via the strut contacts. This study provides novel insights into viscous flow in a complex non-idealized construct, where, locally, particles are not spherical and are of a range of sizes, leading to a random distribution of interparticle porosity, while globally, predesigned porosity between the struts exists to allow the construct to support tissue growth. This is the first time that the three stages of densification have been captured at the local and global scales simultaneously. The insights provided here should accelerate the development of 3D bioactive glass scaffolds.

Open Access

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Abstract: High Resolution X-Ray Computed Tomography (HRXCT) offers a powerful 3-dimensional, nondestructive and non-invasive diagnostic tool for imaging the external and internal structures of a range of specimens of interest including archaeobotanical remains. HRXCT offers new possibilities in terms of the research questions which may be asked of fragile and valuable archaeological and specifically archaeobotanical material. This technology, although currently somewhat limited in terms of time and access to beamtimes at National Synchrotrons, requires simple, non-destructive preparation of samples and produces exciting results. Based upon two rounds of successful work, we believe that this new methodology has wider implications and utility for advancing the field of imaging, and investigating aspects of plant domestication such as internal anatomical changes.

Open Access

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Abstract: X-ray computed tomography and, specifically, time-resolved volumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process. For this paper these highly sampled data are used to aid feature detection in the rapidly collected tomograms by assisting with the upsampling of their projections, which is equivalent to upscaling the θ-axis of the sinograms. In this paper, a super-resolution approach is proposed based on deep learning (termed an upscaling Deep Neural Network, or UDNN) that aims to upscale the sinogram space of individual tomograms in a 4D dataset of a sample. This is done using learned behaviour from a dataset containing a high number of projections, taken of the same sample and occurring at the beginning or the end of the data collection. The prior provided by the highly sampled tomogram allows the application of an upscaling process with better accuracy than existing interpolation techniques. This upscaling process subsequently permits an increase in the quality of the tomogram's reconstruction, especially in situations that require capture of only a limited number of projections, as is the case in high-frequency time-series capture. The increase in quality can prove very helpful for researchers, as downstream it enables easier segmentation of the tomograms in areas of interest, for example. The method itself comprises a convolutional neural network which through training learns an end-to-end mapping between sinograms with a low and a high number of projections. Since datasets can differ greatly between experiments, this approach specifically develops a lightweight network that can easily and quickly be retrained for different types of samples. As part of the evaluation of our technique, results with different hyperparameter settings are presented, and the method has been tested on both synthetic and real-world data. In addition, accompanying real-world experimental datasets have been released in the form of two 80 GB tomograms depicting a metallic pin that undergoes corruption from a droplet of salt water. Also a new engineering-based phantom dataset has been produced and released, inspired by the experimental datasets.

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Abstract: Osteoregenerative biomaterials for the treatment of bone defects are under much development, with the aim of favouring osteointegration up to complete bone regeneration. A detailed investigation of bone-biomaterial integration is vital to understanding and predicting the ability of such materials to promote bone formation, preventing further bone damage and supporting load-bearing regions. This study aims to characterise the ex vivo micromechanics and microdamage evolution of bone-biomaterial systems at the tissue level, combining high resolution synchrotron micro-computed tomography, in situ mechanics and digital volume correlation. Results showed that the main microfailure events were localised close to or within the newly formed bone tissue, in proximity to the bone-biomaterial interface. The apparent nominal compressive load applied to the composite structures resulted in a complex loading scenario, mainly due to the higher heterogeneity but also to the different biomaterial degradation mechanisms. The full-field strain distribution allowed characterisation of microdamage initiation and progression. The findings reported in this study provide a deeper insight into bone-biomaterial integration and micromechanics in relation to the osteoregeneration achieved in vivo, for a variety of biomaterials. This could ultimately be used to improve bone tissue regeneration strategies.