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In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C 3 N 4 interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NO x) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C 3 N 4 was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C 3 N 4 is most suitable as a potential gas-sensitizing material for NO 2 gas sensors, followed by CO. Interestingly, Pd-modified g-C 3 N 4 is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C 3 N 4 remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C 3 N 4 as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases. [Display omitted] • The sensing properties of Pd-modified g-C 3 N 4 interface were evaluated for CO, NO x. • A new experiment for the prediction of humidity-influenced interface performance was designed. • This work enriches new schemes for the preparation of g–C 3 N 4 –based gas sensors. • This work provides new ideas for addressing hazardous gases generated in the electrical industry. [ABSTRACT FROM AUTHOR]

Insulation performance testing of gas-insulated switchgear (GIS) and treatment of SF 6 decomposition products under partial discharge are two crucial studies. In this paper, the adsorption behavior and sensor properties of five characteristic decomposition gases of SF 6 (HF, H 2 S, SO 2 , SOF 2 , SO 2 F 2) on intrinsic and CoO-doped SnSe monolayers are investigated in detail based on density-functional theory. The results showed that the adsorption behaviors of HF, H 2 S, SOF 2 and SO 2 F 2 on SnSe monolayers were all physisorption among which the best adsorption was SO 2 with an adsorption energy of −0.618 eV, which is a weak chemisorption. After doping with CoO, the adsorption capacity of SnSe monolayer for five gases was significantly enhanced, in which the adsorption energies for SO 2 and SOF 2 reached −1.356 eV and −1.175 eV, respectively. In addition, the conductivity of the system is greatly improved, with the bandgap changing from 1.129 eV to 0 eV. The microscopic mechanism of the interaction of gas molecules with CoO-SnSe monolayers has been revealed by energy band structure (E g), density of states (DOS), Milligan charge analysis (∆ Q) and electrostatic potential. Finally, the sensitivity (S) and desorption time (τ) of the five adsorption systems were calculated to illustrate the macroscopic gas-sensitive properties of the system. This work will help to explore the application of CoO-SnSe monolayers in SF 6 decomposition gas sensing detection and adsorption treatment. [Display omitted] • Doping greatly improves the conductivity of the system, and the adsorption effect of doped system on SO 2 and SOF 2 is significantly improved. • The change of electronic characteristics of doped system is expounded. • The sensing characteristics of adsorption system are calculated, and the possibility of SOF 2 as the target gas for CoO-SnSe sensing device to evaluate GIS insulation performance is explored. [ABSTRACT FROM AUTHOR]

Accurate and real-time prediction of gas concentrations is a critical component of intelligent prediction systems and holds significant importance for production safety and quality of life. However, due to the constraints of the network topology in gas sensor networks and the temporal nature of gas concentration variations, concentration prediction has always been considered a significant challenge. To simultaneously capture the spatiotemporal correlations in gas concentration data with spatiotemporal characteristics, this study proposes a fully-connected temporal multilayer graph convolutional network (FTM-GCN). This method combines multiple graph convolutional layers (MGC), fully connected layers, and gated recurrent units (GRU). FTM-GCN exhibits three prominent features: (1) It leverages MGC to learn the topology of gas sensor networks, enhancing the model's ability to graph data and capture spatial characteristics. MGC is incorporated to capture spatial features in the data. (2) GRU is employed to capture the dynamic changes in sensor network data and the temporal characteristics of the data. (3) Continuous fully-connected layers are introduced to enhance the model's performance. Experimental results demonstrate that FTM-GCN effectively captures the spatiotemporal correlations in spatiotemporal data across various prediction perspectives and outperforms GCN, GRU, T-GCN and STGCN. • Spatio-temporal prediction of graph-structured data using an electronic nose. • Successful prediction of ethanol concentrations in space-time. • A new deep learning model called FTM-GCN is proposed which is based on GCN. • The proposed FTM-GCN achieves outstanding regression prediction performance. [ABSTRACT FROM AUTHOR]