Optimization of perovskite gas sensor performance: characterization, measurement and experimental design

Abstract

Eight different types of nanostructured perovskites based on YCoO3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo0.9Pd0.1O3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312°C for CO (284 ppm, from design) and response equal to −14.17% at 185°C for NO2 (16 ppm, from design). Analogously, for NO2 (16 ppm, from design), the material type YCo0.9O2.85 + 1%Pd (Mt8) allows for optimizing the response value at −15.39% with a working temperature at 181.0°C, whereas for YCo0.95Pd0.05O3 (Mt3), the best response value is achieved at −15.40% with the temperature equal to 204°C