In the paper, the Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) results of the surface layer formed on pure titanium after plasma electrolytic oxidation (micro arc oxidation) at the voltage of 450 V are shown. As an electrolyte, the mixture of copper nitrate Cu(NO₃)₂ (10–600 g/L) in concentrated phosphoric acid H₃PO₄ (98 g/mol) was used. The thickness of the obtained porous surface layer equals about 10 μm, and it consists mainly of titanium phosphates and oxygen with embedded copper ions as a bactericidal agent. The maximum percent of copper in the PEO surface layer was equal to 12.2 ± 0.7 wt % (7.6 ± 0.5 at %), which is the best result that the authors obtained. The top surface layer of all obtained plasma electrolytic oxidation (PEO) coatings consisted most likely mainly of Ti₃(PO₄)₄∙nH₃PO₄ and Cu₃(PO₄)₂∙nH₃PO₄ with a small addition of CuP₂, CuO and Cu₂O.

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life.

The results of the investigations into the technological formation of new wear-resistant polymer composites based on polytetrafluoroethylene (PTFE) filled with disperse synthetic and natural compounds are presented. The efficiency of using PTFE composites reinforced with carbon fibers depends on many factors, which influence the significant improvement of physicomechanical characteristics. The results of this research allow stating that interfacial and surface phenomena of the polymer–solid interface and composition play a decisive role in PTFE composites properties. Fillers hinder the relative movement of the PTFE molecules past one another and, in this way, reduce creep or deformation of the parts, reducing the wear rate of parts used in dynamic applications as well as the coefficient of thermal expansion. The necessary structural parameters of such polymer composites are provided by regimes of process equipment.

This paper from the field of environmental chemistry offers an innovative use of sorbents in the treatment of waste industrial water. Various industrial activities, especially the use of technological fluids in machining, surface treatment of materials, ore extraction, pesticide use in agriculture, etc., create wastewater containing dangerous metals that cause serious health problems. This paper presents the results of studies of the natural zeolite clinoptilolite as a sorbent of copper cations. These results provide the measurement of the sorption kinetics as well as the observed parameters of sorption of copper cations from the aquatic environment to the clinoptilolite from a promising Slovak site. The effectiveness of the natural sorbent is also compared with that of certain known synthetic sorbents.