The concentration or the partial pressure of oxygen in an environment can be determined using different measuring principles. For high temperature measurements of oxygen, ceramic-based sensors are the most practical. They are simple in construction, exploration and maintenance. A typical oxygen potentiometric sensor consists of an oxygen ion conducting solid electrolyte and two electrodes deposited on the two sides of the electrolyte. In this paper different structures of potentiometric oxygen sensors with a solid state reference electrode were fabricated and investigated. The fabricated structures consisted of oxygen ion conducting solid electrolyte from yttria stabilized zirconia, a sensing platinum electrode and nickel-nickel oxide reference electrode. The mixture of nickel-nickel oxide was selected as the reference electrode because it provides reliable electrochemical potential in contact with oxygen conducting electrolyte. To avoid oxidation of nickel the reference electrode is sealed from ambient and the mixture of nickel-nickel oxide was formed electrochemically from nickel oxide after sealing. The effectiveness of the sealing quality and the effectiveness of nickel-nickel oxide mixture formation was investigated by impedance spectroscopy.
Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling