Highly Sensitive Gas Sensor for Detection of Air Decomposition Pollutant (CO, NOx): Popular Metal Oxide (ZnO, TiO2)-Doped MoS2 Surface.
ACS Appl Mater Interfaces
; 16(3): 3674-3684, 2024 Jan 24.
Article
em En
| MEDLINE
| ID: mdl-38198663
ABSTRACT
When partial discharges occur in air-insulated equipment, the air decomposes to produce a variety of contamination products, resulting in a reduction in the insulation performance of the insulated equipment. By monitoring the concentration of typical decomposition products (CO, NO, and NO2) within the insulated equipment, potential insulation faults can be diagnosed. MoS2 has shown promising applications as a gas-sensitive semiconductor material, and doping metal oxides can improve the gas-sensitive properties of the material. Therefore, in this work, MoS2 has been doped using the popular metal oxides (ZnO, TiO2) of the day, and its gas-sensitive properties to the typical decomposition products of air have been analyzed and compared using density functional theory (DFT) calculations. The stability of the doped system was investigated using molecular dynamics methods. The related adsorption mechanism was analyzed by adsorption configuration, energy band structure, density of states (DOS) analysis, total electron density (TED) analysis, and differential charge density (DCD) analysis. Finally, the practical application of related sensing performance is evaluated. The results show that the doping of metal oxide nanoparticles greatly improves the conductivity, gas sensitivity, and adsorption selectivity of MoS2 monolayer to air decomposition products. The sensing response of ZnO-MoS2 for CO at room temperature (25 °C) reaches 161.86 with a good recovery time (0.046 s). TiO2-MoS2 sensing response to NO2 reaches 3.5 × 106 at 25 °C with a good recovery time (0.108 s). This study theoretically solves the industrial challenge of recycling sensing materials and provides theoretical value for the application of resistive chemical sensors in air-insulated equipment.
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01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
ACS Appl Mater Interfaces
Ano de publicação:
2024
Tipo de documento:
Article