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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.

The deterioration of concrete over time is the result of various mechanical, physical, chemical and biological processes, with the corrosion of reinforcement being the most serious problem of durability of reinforced concrete structures. Over the last 50 years, a tremendous effort has been spent by the international scientific community with laboratory research and experimental field studies in order to increase the resistance of concrete over corrosion. This paper presents an experimental study of the corrosion behaviour of reinforced concrete beams with simultaneous sustained flexural loading. For this purpose, 40 reinforced concrete beams of 5 different compositions were constructed and exposed in simulated harmful environmental conditions in 3 different stress ratios for a total period of 42 months. Their behavior against corrosion was determined via regular measurements of the electrical resistance of concrete (according to ASTM G57) and the corrosion potential of the steel-reinforced bars with the use of copper sulphate (CSE) as reference electrode (according to ASTM C876). A theoretical calculation of the corrosion rate was conducted based on the electrochemical measurements of the beams. The results indicate that the corrosion potential of steel decreased in time and more rapidly after the initiation of the corrosion process; the electrical resistance firstly increased, remained stable for a short period and then decreased with the corrosion development, as expected. An inversely proportional relationship of the water/cement ratio of a composition with its corrosion behaviour as well as an analogous relationship between the cement content of a composition and its corrosion behaviour was observed. Also, the corrosion rate of steel is increased gradually with increasing load.

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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.