Titanium dioxide (TiO 2 ) is described as an established material to remove pollutants from water. However, TiO 2 is still not applied on a large scale due to issues concerning, for example, the form of use or low photocatalytic activity. We present an easily upscalable method to synthesize high active TiO 2 nanoparticles on a polyethersulfone microfiltration membrane to remove pollutants in a continuous way. For this purpose, titanium(IV) isopropoxide was mixed with water and hydrochloric acid and treated up to 210 • C. After cooling, the membrane was simply dip-coated into the TiO 2 nanoparticle dispersion. Standard characterization was undertaken (i.e., X-ray powder diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, water permeance, contact angle). Degradation of carbamazepine and methylene blue was executed. By increasing synthesis temperature crystallinity and photocatalytic activity elevates. Both ultrasound modification of nanoparticles and membrane pre-modification with carboxyl groups led to fine distribution of nanoparticles. The ultrasound-treated nanoparticles gave the highest photocatalytic activity in degrading carbamazepine and showed no decrease in degradation after nine times of repetition. The TiO 2 nanoparticles were strongly bound to the membrane. Photocatalytic TiO 2 nanoparticles with high activity were synthesized. The innovative method enables a fast and easy nanoparticle production, which could enable the use in large-scale water cleaning.Catalysts 2018, 8, 376 2 of 16 efficiently from water. The main challenge is to generate a low cost, long-term stable, and reusable system with a high activity to degrade organic contaminants.The mode of application of TiO 2 is a crucial parameter as it already defines the degradation rate and success. Theoretically, the photocatalytic activity is the highest when using TiO 2 as nanoparticles in a suspension as the overall surface area can be immense. However, nanoparticles tend to agglomerate to larger particles (i.e., surface area is diminished) and after final cleaning the nanoparticles have to be removed extensively [9,10]. Binding TiO 2 to a support (e.g., membrane [11][12][13][14]) decreases the overall surface area but the degradation of pollutants can be executed in one step. Designing a porous support can overcome the surface area issues of a supported system. Membranes are ideal support systems as they are highly porous and can be produced easily, are cost effective, and exist in many different forms according to the need of the consumer. The pollutant is directly transported through the membrane to the photocatalyst for degradation. Slow transport of the pollutant to the photocatalyst (e.g., diffusion) is avoided. To generate a high surface area compound, the porosity should be high and the pore size low. Nonetheless, decreasing the pore size will increase the energy needed to operate the membrane system. Microfiltration membranes with a pore size of 0.22 and 0.45 µm have been successfully utilized [13,14].Other param...

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Highly porous alumina monoliths can be fabricated by simultaneous hydrolysis of aluminum alkoxides and salts as homonuclear precursors. The use of carcinogenic epoxides can thus be avoided. In this novel approach, no water is added to the system but hydrolysis is induced by the crystal water of the aluminum salt. Mechanical stabilization and significantly increased porosity values can be achieved when the sol-gel synthesis is performed in an autoclave.

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.