The unique properties of microporous zeolites, including ion-exchange properties, adsorption, molecular sieving, catalysis, conductivity have been exploited in improving the performance of gas sensors. Zeolites have been employed as physical and chemical filters to improve the sensitivity and selectivity of gas sensors. In addition, direct interaction of gas molecules with the extraframework cations in the nanoconfined space of zeolites has been explored as a basis for developing new impedance-type gas/vapor sensors. In this review, we summarize how these properties of zeolites have been used to develop new sensing paradigms. There is a considerable breadth of transduction processes that have been used for zeolite incorporated sensors, including frequency measurements, optical and the entire gamut of electrochemical measurements. It is clear from the published literature that zeolites provide a route to enhance sensor performance, and it is expected that commercial manifestation of some of the approaches discussed here will take place. The future of zeolite-based sensors will continue to exploit its unique properties and use of other microporous frameworks, including metal organic frameworks. Zeolite composites with electronic materials, including metals will lead to new paradigms in sensing. Use of nano-sized zeolite crystals and zeolite membranes will enhance sensor properties and make possible new routes of miniaturized sensors.

This paper aims to estimate the concentrations of volatile organic compounds (VOCs), such as ethanol, acetone, formaldehyde, and toluene, in a quaternary mixture. In order to develop an electronic nose for practical applications, the concentrations of VOCs and their combinations in the mixture are randomly assigned. The sensor array consists of five metal oxide sensors, which are produced in a laboratory. The algorithm for the estimation of concentration is realized using a back-propagation neural network (BPNN) with two hidden layers and decision tree learning. First, the data set of the VOC mixture is divided into four subclasses based on the concentration using classification and regression trees. Second, every subclass is classified and regressed using a corresponding BPNN; furthermore, its four output nodes provide a continuous prediction of the concentration of each VOC in the mixture. A single BPNN with two hidden layers is also constructed and evaluated for the purpose of comparison. The maximum error in the concentration estimation of each VOC using the proposed method is approximately 2 ppm, and the accuracy is better than the result obtained using the single BPNN. Moreover, the relative error is less than 5% when the predicted concentration is higher than 20 ppm. This paper reports some aspects of the potential of using neural networks for quantitatively analyzing the concentrations of a VOC mixture.

Microwave transduction is a novel research field in gas sensing owing to its simplicity, low cost, passive, and non-contact operations that indicate a huge potential in applications for gas sensing. At present, the study of microwave gas sensors is limited to testing the macroscopic performance of materials. Although the permittivity change of materials is the reason for microwave transduction, the knowledge of the fundamental mechanism is an urgent requirement for boosting the development of microwave gas sensors. In this review, we summarized the presented progresses of microwave gas sensors, including the performance of the different materials and propagative structures, as well as their sensitive signal. We have attempted to explain the sensitive mechanism of these materials, discuss the function of the propagative structure, and analyze the sensitive signal extracted from the parameters of electromagnetic waves. Finally, the challenges in the study of sensitive materials and propagative structures used in microwave gas sensors are analyzed. It is clear from the published literatures that microwave transduction provide a new route for gas detection, and it is expected that commercial manifestation will occur. The future research on microwave gas sensors will continue to exploit their sensitive materials and propagative structure for different applications. The understanding of the microscopic polarization interactions with gases will assist in and guide the fabrication of high-performance microwave gas sensors in the future.