Hybrid nanostructured materials for gas-sensor applications

Today, there are a variety of sensor technologies designed to detect almost any gas. These miniaturized sensory systems should be capable of precisely identifying different gaseous species and mixtures, need further significant improvements. In this regard, this thesis is carried out at Sensors group of Tor Vergata, is aimed at contributing to the advances in gas sensor technology by adopting solutions such as preparation of engineered hybrid nanostructures with a highly specific surface area, electrode geometry and sensing method, multisensory array, and data processing and projection tools; are demonstrated to be helpful approaches to improve the gas-sensing performance in terms of sensitivity and selectivity. The innovative hybrid nanostructures made of organic and inorganic materials are gaining interest in many technological domains ranging from light conversion to sensors. In this thesis the porphyrinoids as organic and ZnO and SiO2 as inorganic materials are considered. The investigated hybrid materials are prepared by the state-of-the-art approaches. The ZnO and SiO2 nanohybrids are prepared using one-pot growth and postgrowth methods respectively and duly characterized. These materials are then applied with transducers based on different working principles such as conductometric and capacitive type sensing and the resulting sensors show the interplay between chemical sensitivity and selectivity. The emphasis is given for investigating the sensing behaviour of an array to a wide range of VOCs and real samples. The porphyrinoids structure dictates the overall sensing properties of nanohybrids. Thus, these sensors show promise to be implemented as an e-nose that combines high sensitivity to high polar compounds in case of capacitive array and high selectivity to strong electron donors in case conductive array and broad-selectivity towards the other classes of chemicals with different sensitivity patterns. The large amount of the data has been analysed and the data projections are developed for efficiently mapping and thereby discriminating the responses of the sensor array using Euclidean and angular distances. A remarkable improvement in device response to VOCs is achieved by cluster a very large class of pure chemical vapors. Finally, real applications such as food and wine investigated by chemotopoic map with conductive array and COVID19 detection using linear discriminant classifier with capacitive array highlight the potentialities of these nanohybrids for the commercialization in food and health sectors.