Thermal barrier coatings (TBCs) are multilayered and multifunctional coating system consisting of oxidation and corrosion resistant metallic bond coat of MCrAlY (M-Ni or Co or NiCo), thermally grown α-α-Al2O3 inter layer and insulating ceramic top coat. Enhanced performance of TBCs strongly depends on their high thermomechanical properties and also on the processing techniques. Understanding the top coat materials properties such as structural, mechanical, chemical and thermal can prevent the failure of the coatings. The talk will dwell upon the development of bi-layer coatings of 7YSZ and GZ and investigation on TBCs subjected to isothermal and cyclic test at 1273 K and 1473 K.

The features and migration mechanism of tilt boundaries with the misorientation axis 〈111〉, 〈100〉 and 〈110〉 in an fcc crystal using nickel as an example were studied by the method of molecular dynamics. The dependences of the boundaries energy and the rate of their migration at a temperature of 1700 K on the misorientation angle are obtained. It is shown that the migration rate of 〈110〉 tilt boundaries under the same conditions is an order of magnitude lower than the migration rate of 〈111〉 and 〈100〉 boundaries, which is primarily due to the relatively low energy of 〈110〉 boundaries. An analogy of migration mechanisms of low-angle 〈110〉 boundaries with the 〈111〉 and 〈100〉 boundaries was noted. During migration, in the grain towards which the migration took place, regions of the same shape orderly rotated through the angle of misorientation were formed, the size of which depended on the distance between neighboring grain boundary dislocations. In addition, the influence of carbon and oxygen impurity atoms and vacancy concentration on the migration rate of tilt boundaries with the 〈100〉 and 〈111〉 misorientation axes in fcc metals Ni, Ag and Al was studied by mean of molecular dynamics. It is shown that the introduction of impurity atoms of light elements led to a significant inhibition of the migration of grain boundaries. The dependence of the migration rate of grain boundaries on the vacancy concentration is nonmonotonic and has a maximum at a concentration of vacancies introduced at the initial stage of about 1%.

Biosensors are vital devices for healthcare, environmental monitoring, and food safety. Biorecognition layer where bioreceptors attach on a solid support plays a crucial role in determining the performance of biosensors like sensitivity, selectivity, and response time. Metal oxide thin films have good candidate for biorecognition layers in biosensors due to their unique properties. In our study, SiO₂-TiO₂ thin films, used fiber optic guiding layers of optical DNA biosensors, were fabricated by the sol-gel dip coating technique. Refractive index values of the films were measured using a Metricon 2010 prism coupler after immobilization of ssDNA (single strand DNA) and hybridization of target cDNA (complementary DNA). On the surface of each film, after immobilization and hybridization process, 12 different spots were taken for the measurement and calculation of the mean refractive index values. Increased refractive index values after each step showed the possibility that SiO₂-TiO₂ thin films can be used as a solid support in optical DNA biosensors. Additionally, our current studies on thin film biosensors will be outlined.

Ultra-thick coatings based on Carbon and Boron have been prepared by pulsed plasma enhanced chemical vapour deposition (PECVD). The plasma used for the deposition was characterized by Optical emission spectroscopy and Langmuir probe.

The deposited films were characterized by Raman spectroscopy, AFM/SEM surface topography, and nano-indentation hardness measurements. Further characterization of the prepared films was conducted using XDLVO based surface energy measurements and corrosion testing in brine solution using OCP and Tafel plots, as well as Electrochemical impedance spectroscopy (EIS) using Nyquist plots, Bode plots and Mott-Schottky analysis. The prepared coatings were found to hold substantial promise in application areas that include Oil and gas pipeline protection and for corrosion protection in aggressive geothermal brine containing environments.

It has been obvious through history that the evolution of technology has been controlled by the materials available. It is increasingly so today, and composite materials are among the most demanded. The increased use of such materials and structures in many engineering applications led to the need for a more accurate analysis and design optimization. While most of the major analytical theories were already developed in the last century, there were no reliable design methods for complex laminated composites with the use of an appropriate failure criterion until late 1980's . In fact, a five-layer anisotropic structure was probably a limit of accurate results. Thus, such problems fundamentally required new approaches and techniques. With the advent of evolutionary algorithms, it became possible to open up new multidimensional and complex problems for an accurate design optimization. Nature is striking in its complexity, despite its apparent chaotic appearance; it is well ordered and follows clear rules. Most of these rules can be explained by the theory of evolution through heredity, mutation and selection. The present study demonstrates how progress in modern evolutionary algorithms has revolutionized the design optimization of composite structures. The performance of such algorithms is shown by the example of the fibre–reinforced composite laminated pressure vessel. With proper tuning such algorithms like particle swarm optimization and the Big Bang – Big Crunch optimization can reach the optimum solution within a few seconds even in a highly complex twenty-dimensional and higher search space. It is obvious that similar results can be achieved for various other types of problems. Obviously, the use of the evolutionary algorithms is not always resulting in an efficient optimization. For instance, for some problems, the use of the genetic algorithms might require millions of possible designs to be analysed. Besides, the heterogeneous material and geometrical complexities can stand in the way of an efficient search in a large design space. An accurate representation of the design model would lead to a much longer chromosome string and, as a result, a poor exchange of the genetic material and the stagnation of the algorithm. At the same time, such methods like the Particle Swarm Optimization and the Big Bang – Big Crunch algorithm seem to be faster and much easier to use then the GA if the design parameters can be given in the form of the coordinate numbers. This requirement, however, prevents their use for topological and similar design problems. Using the design optimization problem for the search of an optimum fibre orientation in complex laminated structures as an example, the performance of the optimizing methods is demonstrated and subsequently discussed.

A deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions and compare with actual experimental data. A test rig is set up to provide the high gravity up to 11 g with the heat flux can be up to 15100 W/m² and the mass velocity range from 40 to 2000 kg m^-2 s^-1. In the current work, total 531 data samples have been used in the present ANN model. The proposed model was developed in Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The obtained results demonstrate that 8-512-512-64-2 neural network model has the best performance in predicting the helical coil characteristics with (R²=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136 , cosine proximity=1.000) in testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, especially achieved excellent performance in predicting the outputs having very large range of value differences.

Thermal spraying methods are commonly used in surface engineering area. Among them, plasma spraying exhibit many features and allows to obtain high quality coatings. In the last three decades, in the field of thermal spraying the finely grained structures are extensively investigated. There are many varieties, which make a possible to manufacture coatings build with submicrometric or even nanometric structure. The most perspective ones are: (i) suspension plasma spraying (SPS) and (ii) solution precursor plasma spraying (SPPS). In the presentation, the results of author’s investigations as well as from literature are presented in terms of microstructure analysis, mechanical and functional properties.

Surface engineering is a discipline that focuses on altering the properties of the parent material to reduce degradation over time. This is achieved through applying a ceramic coating to the base metal surface. The traditional methods include plasma and thermal spraying processes, Chemical Vapour Deposition (CVD), laser deposition and welding techniques, however our research focuses on using TIG (Tungsten Inert Gas) welding to embed the ceramic coating on the steels surface providing the same quality of coating as that offered by plasma and thermal spraying processes but less expensive.than these traditional techniques.

The outstanding properties of semiconductor nanowires (NWs) offer an exciting potential to develop novel and technological advances and new device concepts. For instance, their wide energy bandgap (~ 3.37 eV) and high exciton binding energy (60 meV) make zinc oxide (ZnO) NWs very attractive to develop highly efficient water splitting photoanodes. This presentation will discuss how a low-cost chemical bath deposition is advantaged to produce single-phase wurtzite ZnO NWs. We will show that the ZnO NWs can be doped to fine-tune their energy bandgap and subsequently conformally coated with metal organic framework to yield high-efficiency Photoanodes. The paper will also discuss the ZnO NWs growth, structural and optical characterisation properties and their potential for use as photoanodes for water splitting.

The presentation focuses on the metallurgical changes that associate with welding and heat treatments of different types of duplex stainless-steel alloys (2304, 2205 and 2507) and the role of austenitic stainless-steel alloy (316L) as a coating layer on Ti alloy as as antibacterial biomaterials applications. The first part aims to find out the optimum heat treatments to recover the microstructural changes of stainless-steel alloys, The alloys were heated to different temperatures, 750, 850, 950 and 1,050°C, for three different times, 10 min, 1 and 4 h. Findings The microstructural investigations showed that 2205 and 2507 behaved similarly in recovering their microstructures, especially in terms of the ferrite:austenite ratio within specific heat treatments and changing the hardness values. The results indicated that the microstructure of both alloys started to change above 750°C, the largest changes were shown at 850 and 950°C as the lowest ferrite content (FC%) was recorded at 850°C for both alloys. The second part aims to investigate the behaviour of austenitic stainless-steel layer on Ti substrate using a powder metallurgical technique. The Ti6Al4V alloy powder was placed as a substrate and stainless-steel type 316L powder layer was added on the substrate. The two layers were consolidated in-situ using a uniaxial hot press. The samples were metallographically prepared and their antibacterial properties were evaluated. A strong bonding was observed between the Ti6Al4V substrate and the 316L stainless steel layer, and no bacteria were observed on the stainless-steel surface.

The presentation describes the results of broad studies of the cavitation erosion behaviour and phenomena responsible for coatings and metallic materials resistance. Cavitation erosion is a unique wear process that still is not entirely understood. Even though the literature on the subject explains the general factors influencing cavitation erosion of materials, the continuous development of metal-based structures fabrication, processing, and treatment technology demands systematic reporting on the advances in the wear properties of metallic materials. From both the scientific and engineering points of view, the wear of metallic components must be minimized to improve their reliability. The engineering industry is demanding metal-based structures that perform well in terms of cavitation erosion. First, to manage that task, material wear mechanisms should be understood. To facilitate the selection and design of wear-resistant materials, computer simulation, numerical calculations, or artificial neural networks can be employed. The presentation is focused on the cavitation erosion testing of metal-based structures, hardfacings, thermally sprayed deposits, thin films, composites. The cavitation wear improvement via microstructural properties modification, surface layer treatment, and the deposition of wear-resistant coatings onto a metal-based substrate are described.