This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

Hydroxyapatite (HAp) has been attracting widespread interest in medical applications. In a form of coating, it enables to create a durable bond between an implant and surrounding bone tissues. With addition of silver nanoparticles HAp should also provide antibacterial activity. The aim of this research was to evaluate the composition of hydroxyapatite with silver nanoparticles in a non-destructive and non-contact way. For control measurements of HAp molecular composition and solvent evaporation efficiency the Raman spectroscopy has been chosen. In order to evaluate dispersion and concentration of the silver nanoparticles inside the hydroxyapatite matrix, the optical coherence tomography (OCT) has been used. Five samples were developed and examined ‒ a reference sample of pure HAp sol and four samples of HAp colloids with different silver nanoparticle solution volume ratios. The Raman spectra for each solution have been obtained and analyzed. Furthermore, a transverse-sectional visualization of every sample has been created and examined by means of OCT.