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In this work, we demonstrate that highly oriented c-axis aluminium scandium nitride (AlScN) piezoelectric thin films can be deposited via microwave plasma-assisted reactive high power impulse magnetron sputtering (MAR-HiPIMS), without the necessity of substrate heating. A combination of in situ plasma diagnostics, i.e. time-of-flight mass spectrometry (ToF-MS), modified quartz crystal microbalance (m-QCM), and magnetic field measurements allowed to optimise the deposition conditions, in turn maximising the nitrogen supply and ionic flux at the substrate region, while maintaining stable discharge conditions. The AlScN thin films synthesised in this study were deposited as chemically gradient coatings with varying levels of scandium doping, and were characterised using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). Obtaining highly textured films was made possible with the addition of microwave plasma to the optimised HiPIMS discharge, where the wurtzite AlScN films (with up to 20 at. % Sc) exhibited a stronger texture in the (0002) orientation compared to films prepared without microwave plasma. Additionally, the use of a microwave plasma led to a significant decrease in oxygen content in the films and increase in nitrogen content, ensuring stoichiometric compositions. Based on the results mentioned above, it is expected that the AlScN thin films fabricated via MAR-HiPIMS would exhibit a strong piezoelectric response.

discrimination and quantification of volatile organic compounds (VOCs) using a non-selective sensor requires a combination of sensors followed by pattern recognition methods. Based on this concept, this paper deals with the discrimination of gas from the responses of several gas sensors coated with different type of polymer. Quartz crystal microbalance (QCM) electrodes were coated from hexamethyldisiloxane (HMDSO), hexamethyldisilazane (HMDSN) and tetraethoxysilane (TEOS) for the elaboration of gas sensors with different chemical affinity towards VOC molecules. The sensitivity of the elaborated QCM-based sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds, such as; ethanol, benzene and chloroform. The sensors responses data have been used for the identification and quantification of VOCs. The principal component analysis (PCA) and the neural-network (NNs) pattern recognition analysis were used for the discrimination of gas species and concentrations. Good separation among gases has been obtained using the principal component analysis. The feed-forward multilayer neural network (MLNNs) with a hidden layer and trained by Broyden Fletcher Goldfarb Shanno (BFGS) Quasi Newton algorithm has been implemented in order to identify and quantify the VOCs. By increasing the number of the neuron in the hidden layer, the precision of the estimate concentration increases. The approach is standard, however its application on the elaborated sensors have not been studied in depth so far.