Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

C3N monolayer is reported with comparable and even more desirable sensing behavior upon small gas molecules in comparison to graphene. In this work, we proposed Pt-doped C3N monolayer as a desirable 2D sensing nanomaterials for NOx detection by DFT method. The Pt atom tends to be doped on the BC-C site of C3N surface, making little effect on the bandgap but changing the indirect semiconducting property on the other hand. Pt-C3N monolayer behaves more admirable performance upon NO2 adsorption than NO, but both systems are identified as chemisorption with Ead of -2.25 and -1.99 eV, and QT of -0.344 and -0.083 e, respectively. The metallic property is determined in both systems given the calculated zero bandgap, which will increase the electrical conductivity of Pt-C3N monolayer largely after adsorption of NOx gases. This is the basic sensing mechanism for Pt-C3N monolayer upon NOx detection. In the meanwhile, the desirable changes of WF and dielectric function in NOx systems verify the potential of Pt-C3N monolayer for NOx detection through field effect transit or optical devices. Our calculations could be meaningful to exploit novel 2D nanomaterial for sensing NOx in order to monitor toxic gases in our environment.

Air discharge characteristic gas detection is considered as one of the most reliable methods in fault diagnosis of air switchgear. Based on first-principles of density functional theory, the adsorption and sensing properties of transition metal (Cr) modified MoS ₂ (001) monolayer on three air discharge characteristic gases (Co, NO, and NO ₂ ) are analyzed. Firstly, the Cr doped MoS ₂ (001) monolayer model is obtained by modeling and analyzing different doping sites, and the subsequent DFT calculation is done. Based on this model, many kinds of adsorption structures were constructed and optimized. The parameters of adsorption structure, adsorption energy and adsorption distance were obtained. Furthermore, the differential charge density, state density, response performance and adsorption performance of the system were analyzed. The results show that Cr-MoS ₂ (001) monolayer has good adsorption performance to three gases. CO gas molecules can be rapidly desorbed from Cr-MoS ₂ (001) monolayer surface at room temperature. Cr-MoS ₂ (001) monolayer has the potential as a low-power gas sensor material for detecting CO characteristic gases and a solid adsorbent material for cleaning NO and NO ₂ gases. This work provides theoretical guidance for the development of MoS ₂ -based sensors for detecting characteristic gases in air discharge.