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Sulfur hexafluoride (SF6) is widely employed in electrical power systems due to its superior insulating properties. However, prolonged use or equipment failure may cause SF6 gas leaks, leading to environmental contamination. While numerous 2-D materials have been proposed by academics for detecting SF6 decomposition gas, investigations into SF6 gas adsorption and the ability to modulate adsorption performance have been relatively limited. Given the strong adsorption of SF6 gas and the tunability of adsorption performance, this article proposes a methodology for achieving distinct adsorption properties by configuring solvents with varying dielectric constants based on CuO-doped SnSe (SnSe-CuO) monolayer. The SnSe-CuO monolayer is used as an adsorbent for SF6 leakage in solvents with different relative dielectric constants. The methodology employs computational analysis to obtain the solvent dielectric constant corresponding to the required adsorption energy from the fit function, which can serve as a solid theoretical basis for future chemisorption studies.

Gesture recognition using machine learning methods is valuable in the development of healthcare and human–computer interaction and typically relies on images or video. To improve recognition accuracy, these visual data can be combined with data from other sensors, but this approach, known as data fusion, is limited by the quality of sensor data and incompatible datasets. Here, we propose a flexible strain sensor based on laser-induced graphene (LIG) technology, which is easy to manufacture and has superior performance. The sensor is attached to the finger and effectively captures finger bending information as an additional gesture information channel, constructing a bimodal gesture interaction interface for strain and vision. In particular, a deep convolutional neural network (CNN) based on machine learning is used to acquire features in monocular camera image data. The data fusion method of feature layer fusion is selected to construct a bimodal gesture recognition system based on image data and strain sensor data. Experimental tests show that the strain and vision bimodal gesture interaction interface developed based on this fusion recognition model can still achieve more than 90% real-time gesture recognition accuracy under an illumination level of 20 lx, which effectively solves the problem of the traditional monocular vision’s gesture interaction interface with a significant decrease in recognition accuracy under dark light conditions and also further expands the application of LIG flexible sensor devices in human–computer interaction system.

Monitoring sulfur hexafluoride (SF6) is vital in the electrical insulation sector, where it serves as a key insulator. SF6 decomposition, caused by operational wear or aging, releases hazardous gases such as SO2, SOF2, SO2F2, and H2S, raising safety concerns. However, the effectiveness of current nanoscale gas sensors is compromised by SF6’s protective nature, struggling with multigas detection. This study utilizes density functional theory (DFT) calculations to assess the performance of a novel 2-D TiO2-doped HfSe2 (TiO2-HfSe2) monolayer. Through comprehensive analyses of electronic, band structure, and gas interaction, we found that titanium dioxide (TiO2) doping not only broadens the bandgap and enhances the structural stability ( ${E}_{b}$ = −2.934 eV) of HfSe2 monolayer but also increases its gas adsorption ability (197.142%–573.104%). The sensors can effectively distinguish SF6 gas and its by-products in the sequence: SO2F2 > SOF2 > SO $_{{2}} > \text{H}_{{2}}\text{S} \gg $ SF6. The detection of SF6 gas is efficient at room temperature (298 K), ensuring uninterrupted operation of gas-insulated switchgear. Furthermore, adsorption energy analysis with varying dielectric constant solvents showed that higher constants boost adsorption strength (16.907%–71.996%), improving gas differentiation. Finally, a literature comparison confirms the exceptional performance of the TiO2-HfSe2 monolayer, endorsing its application as a reliable sensor for SF6 decomposition gases.

The effective detection and maintenance of electrical equipment failures in circuit maintenance is crucial in reducing public property losses. Therefore, the research and development of a highly sensitive SF6 decomposition detection device is of great significance. Here, in this article, the adsorption behavior and sensing characteristics of TiO2, Ag $_{{2}}\text{O}$ doped GeTe monolayer for SF6 decomposition species ( $\text{H}_{{2}}\text{S}$ , SO2) have been studied through first-principles analysis. The results show that the doping of Ag $_{{2}}\text{O}$ and TiO2 enhances the conductivity of GeTe monolayer by 14.9% and 16.8%, respectively, and increases the adsorption energy of the doped GeTe monolayer for $\text{H}_{{2}}\text{S}$ and SO2 by 214% (TiO2–GeTe– $\text{H}_{{2}}\text{S}$ ), 184% (TiO2–GeTe–SO2), 361% (Ag $_{{2}}\text{O}$ –GeTe– $\text{H}_{{2}}\text{S}$ ), and 191% (Ag $_{{2}}\text{O}$ –GeTe–SO2), respectively. The adsorption energy with proper size makes both doped GeTe monolayers highly sensitive and selective with a rapid recovery time, which theoretically solves the problem of industrial recycling of sensing materials. The research results provide a theoretical basis for the material selection of a highly selective sensor for detecting SF6 decomposition products.