Segmentability evaluation of back-scattered SEM images of multiphase materials.

Segmentation methods are very useful tools in the Electron Microscopy inspection of materials, enabling the extraction of quantitative results from microscopy images. Back-Scattered Electron (BSE) images carry information of the mean atomic number in the interaction volume and hence can be used to quantify the phase composition in multiphase materials. Since phase composition and proportion affects the material properties and hence its applications, the segmentation accuracy of such images rendered of critical importance for material science. In this work, the notion of segmentability for BSE images is proposed to define the ability of an image to be segmented accurately. This notion can be used to guide the image acquisition process so that segmentability is maximized and segmentation accuracy is ensured. An index is devised to quantify segmentability based on a combination of the modified Fisher Discrimination Ratio and of the second Minkowski functional capturing intensity and spatial aspects of BSE images respectively. The suggested Segmentability Index (SI) is validated in synthetic BSE images which are generated with a novel algorithm allowing the independent control of spatial distribution of phases and their grayscale intensity histograms. Additionally, SI is applied in real-synthetic BSE images, where the real greyscale distributions of Ordinary Portland Cement (OPC) clinker crystallographic phases are used, to demonstrate the ability of SI to indicate the optimum choice of critical image acquisition settings leading to the more accurate segmentation output.

Segmentation of SEM images of multiphase materials: When Gaussian mixture models are accurate?

Scanning electron microscopy has been a powerful technique to investigate the structural and chemical properties of multiphase materials on micro and nanoscale due to its high-resolution capabilities. One of the main outcomes of the SEM-based analysis is the calculation of the fractions of material components constituting the multiphase material by means of the segmentation of their back scattered electron SEM images. In order to segment multiphase images, Gaussian mixture models (GMMs) are commonly used based on the deconvolution of the image pixel histogram. Despite its extensive use, the accuracy of GMM predictions has not been validated yet. In this paper, we proceed to a systematic study of the evaluation of the accuracy and the limitations of the GMM method when applied to the segmentation of a four-phase material. To this end, first, we build a modelling framework and propose an index to quantify the accuracy of GMM predictions for all phases. Then we apply this framework to calculate the impact of collective parameters of image histogram on the accuracy of GMM predictions. Finally, some rules of thumb are concluded to guide SEM users about the suitability of using GMM for the segmentation of their SEM images based only on the inspection of the image histogram. A suitable histogram for GMM is a histogram with number of peaks equal to the number of Gaussian components, and if that is not the case, kurtosis and skewness should be smaller than 2.35 and 0.1, respectively.

Engineering Commercial TiO2 Powder into Tailored Beads for Efficient Water Purification.

In this study, efficient commercial photocatalyst (Degussa P25) nanoparticles were effectively dispersed and stabilized in alginate, a metal binding biopolymer. Taking advantage of alginate's superior metal chelating properties, copper nanoparticle-decorated photocatalysts were developed after a pyrolytic or calcination-sintering procedure, yielding ceramic beads with enhanced photocatalytic and mechanical properties, excellent resistance to attrition, and optimized handling compared to powdered photocatalysts. The morphological and structural characteristics were studied using LN2 porosimetry, SEM, and XRD. The abatement of an organic pollutant (Methyl Orange, MO) was explored in the dark and under UV irradiation via batch experiments. The final properties of the photocatalytic beads were defined by both the synthesis procedure and the heat treatment conditions, allowing for their further optimization. It was found that the pyrolytic carbon residuals enabled the adhesion of the TiO2 nanoparticles, acting as binder, and increased the MO adsorption capacity, leading to increased local concentration in the photocatalyst vicinity. Well dispersed Cu nanoparticles were also found to enhance photocatalytic activity. The prepared photocatalysts exhibited increased MO adsorption capacity (up to 3.0 mg/g) and also high photocatalytic efficiency of about 50% MO removal from water solutions, reaching an overall MO rejection of about 80%, at short contact times (3 h). Finally, the prepared photocatalysts kept their efficiency for at least four successive photocatalytic cycles.

Structuring efficient photocatalysts into bespoke fiber shaped systems for applied water treatment.

In this study, structured photocatalytic systems were successfully developed by a facile method based on Alginate molds and a wet-spinning/cross-linking technique, yielding commercial photocatalyst (Degussa P25) in the form of all-ceramic hollow fibers (HFs). Taking advantage of alginate's exceptional sorption properties, copper augmented HFs were also developed. The structured photocatalysts were thoroughly characterised by a variety of techniques, including nitrogen adsorption, SEM/EDS, XRD, XPS and Raman. Synthesis and heat treatment parameters were found to affect the fibers' properties, allowing their optimization. Treatment at 600 °C under Ar was found to produce the best performing photocatalysts in terms mechanical stability, resistance to attrition and photocatalytic performance. Ca-Alginate precursors led to structures with increased mechanical stability, while Cu-Alginate decorated the surface of the photocatalyst with highly dispersed copper nanoparticles, in the state of metallic and CuO. The developed materials were photo-catalytically active, while the copper decorated ceramic HFs exhibited the highest MO adsorption and photocatalytic degradation performance, reaching a MO removal of 73.4%. The synergestic effect of adsorption on the MO degradation performance was also noticed. Moreover, the copper addition facilitated the photocatalytic process by improving the electron-hole separation and inhibiting the recombination effects. The presence of carbon residue was also beneficial, enhancing the MO sorption on the photocatalysts. It is noteworthy that the structured photocatalysts retained their efficiency for at least four photocatalytic cycles. The prepared ceramic HFs exhibited enhanced mechanical properties and excellent resistance to attrition after subsequent cycles, rendering them excellent candidates for application in industrial wastewater processes.