Thesis Examination Committee

Prof Xiaojun ZHANG, ISOM/HKUST (Chairperson)
Prof Amine BERMAK, ECE/HKUST (Thesis Supervisor)
Prof Zhiyong FAN, ECE/HKUST (Thesis Co-supervisor)
Prof Abdelkrim KHELIF, French National Centre for Scientific Research (External Examiner)
Prof Chi Ying TSUI, ECE/HKUST
Prof Johnny Kin On SIN, ECE/HKUST
Prof Yi-Kuen LEE, MAE/HKUST

Abstract

The electronic nose (E-nose), an imitation of mammals’ noses, could transform gas information into the electronic signal, thus it can meet the desire of “smell” the world. Typically, an E-nose system consists of a gas sensor array, a readout circuit, and a classification algorithm. Metal oxide semiconductor gas sensors have been applied for the E-nose because of their stability, low cost, sensitivity, and robustness. The developing nano-technology has provided a new approach for metal oxide based gas sensors to achieve room temperature gas detection with high performance. The readout circuit would transmit the information from the sensor array to the following classification algorithms, and the pre-trained algorithms would accurately distinguish detected gases or even the gas mixtures. 
 
In this thesis, we demonstrate multiple gas sensors and smart E-nose systems for room temperature gas detection and distinction. Firstly, we found the unique U-shape response curve of a single hierarchical ZnO gas sensor towards breath-level acetone with temperature modulation. That sensor presents the potential of gas sensors’ application in non-invasive diabetes monitoring. We also developed a sensor array using hybrid materials for gas sensing. Through modulating the combination ratios of the organic component and metal oxide nanoparticles in the hybrid materials, the sensor array presents the ability to identify drunk driving or to classify six different organic vapors with the classification accuracy of 99.2%. More importantly, we fabricated a high sensitivity monolithic ultra-low power tin oxide gas sensor array that based on the freestanding open-ended nanotube structure. The wireless sensor array module has reached state-of-art hydrogen and benzene detection capability. Combining with classification algorithms, the electronic nose system could identify multiple gases (H2, NO2, Benzene and organic vapors) and even gas mixtures. The aforementioned E-nose system has been successfully applied in the smart home for indoor gas detection.