RESUMO
3D imaging via X-ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine-learning-based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of 'thick' cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput.
RESUMO
The fundamental chemical and structural diversity of metal-organic frameworks (MOFs) is vast, but there is a lack of industrial adoption of these extremely versatile compounds. To bridge the gap between basic research and industry, MOF powders must be formulated into more application-relevant shapes and/or composites. Successful incorporation of varying ratios of two different MOFs, CPO-27-Ni and CuBTTri, in a thin polymer film represents an important step toward the development of mixed MOF mixed-matrix membranes. To gain insight into the distribution of the two different MOFs in the polymer, we report their investigation by Cryo-scanning electron microscopy (Cryo-SEM) tomography, which minimizes surface charging and electron beam-induced damage. Because the MOFs are based on two different metal ions, Ni and Cu, the elemental maps of the MOF composite cross sections clearly identify the size and location of each MOF in the reconstructed 3D model. The tomography run was about six times faster than conventional focused ion beam (FIB)-SEM and the first insights to image segmentation combined with machine learning could be achieved. To verify that the MOF composites combined the benefits of rapid moisture-triggered release of nitric oxide (NO) from CPO-27-Ni with the continuous catalytic generation of NO from CuBTTri, we characterized their ability to deliver NO individually and simultaneously. These MOF composites show great promise to achieve optimal dual NO delivery in real-world medical applications.
