Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NOx), and two odor gases, ammonia (NH3) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and hybridized SnO2-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.

Micro-electronic gas sensor devices were developed for the detection of carbon monoxide (CO), nitrogen oxides (NOx), ammonia (NH3), and formaldehyde (HCHO), as well as binary mixed-gas systems. Four gas sensing materials for different target gases, Pd-SnO2 for CO, In2O3 for NOx, Ru-WO3 for NH3, and SnO2-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were then fabricated using a micro sensor platform. The gas sensing behavior and sensor response to the gas mixture were examined for six mixed gas systems using the experimental data in MEMS gas sensor arrays in sole gases and their mixtures. The gas sensing behavior with the mixed gas system suggests that specific adsorption and selective activation of the adsorption sites might occur in gas mixtures, and allow selectivity for the adsorption of a particular gas. The careful pattern recognition of sensing data obtained by the sensor array made it possible to distinguish a gas species from a gas mixture and to measure its concentration.

Four types of metal oxide semiconductor gas sensor arrays were used to observe the aroma and spoilage odor emitted during the ripening process of plum & banana fruits. All gas sensors showed a high correlation (R=0.82~0.90) with the olfactory. The TGS 2603 sensor showed a high correlation of 0.90 between the odor generated and sensory perception of smell in the process of ripening and decaying fruits. In addition, it showed a very high correlation of 0.91 with the decay rate of the plum sample, and the significance probability through one-way ANOVA was also less than 0.05, which confirmed it as an optimal gas sensor (TGS 2603). Principal component analysis was performed using all the data. The cumulative variability was 99.54%, which could be explained only by two principal components, and the first principal component was 95.11%, which was related to the freshness of the fruit. It was analyzed as fresh fruit in the negative(-) direction and decayed fruit in the positive(+) direction.

In this study, we evaluated the photocatalytic oxidation efficiency of aromatic volatile hydrocarbons by using WO3–doped TiO2 nanotubes (WTNTs) under visible-light irradiation. One-dimensional WTNTs were synthesized by ultrasonic-assisted hydrothermal method and impregnation. XRD analysis revealed successful incorporation of WO3 into TiO2 nanotube (TNT) structures. UV-Vis spectra exhibited that the synthesized WTNT samples can be activated under visible light irradiation. FE-SEM and TEM images showed the one-dimensional structure of the prepared TNTs and WTNTs. The photocatalytic oxidation efficiencies of toluene, ethylbenzene, and o-xylene were higher using WTNT samples than undoped TNT. These results were explained based on the charge separation ability, adsorption capability, and light absorption of the sample photocatalysts. Among the different light sources, light-emitting-diodes (LEDs) are more highly energy-efficient than 8-W daylight used for the photocatalytic oxidation of toluene, ethylbenzene, and o-xylene, though the photocatalytic oxidation efficiency is higher for 8-W daylight.