Research on intelligent sensor for industrial hazardous gases monitoring based on Tc doped C 3 N using density functional theory

被引:1
|
作者
Zheng, Lijun [1 ]
Yin, Xiaomin [2 ]
机构
[1] ZhiJiang Coll ZheJiang Univ Technol, Informat Engn Coll, Coll Informat Engn, Hangzhou 312030, Zhejiang, Peoples R China
[2] Zhejiang Shuren Univ, Hangzhou 310015, Zhejiang, Peoples R China
关键词
Tc doped C3N; Hazardous gases; Adsorption; Density functional theory; ARTIFICIAL-INTELLIGENCE;
D O I
10.1016/j.diamond.2024.111238
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the ongoing global industrialization, a considerable amount of hazardous gases is emitted into the atmospheric environment. Achieving intelligent monitoring of industrial hazardous gases is a crucial pathway toward realizing the modernization of electronic information. In this study, the use of Tc doped C3N nanosheet for the intelligent online monitoring of industrial hazardous gases (CO, NO2, O-3) is firstly proposed. The doping and modification mechanism of Tc nanoparticle on the C3N surface are revealed. Simultaneously, the adsorption and sensing mechanisms of the three target gases on the surface of pure C3N and Tc doped C3N nanosheet is deeply elucidated through density of states, band structure, and differential charge density analysis. The results indicate that Tc particle can stably bind to the C3N surface, providing numerous active sites for the adsorption of target gases, enabling selective capture and scavenging of hazardous gases. Furthermore, the adsorption type for all three gases shifts from physical adsorption to physicochemical adsorption, with significant changes in the electronic signals during surface reactions, manifested by drastic alterations in the band structure and enhanced electron mobility. The adsorption capacity order for the three gases is O-3 > NO2 > CO. The achievements of this work not only provide a theoretical foundation for the design and preparation of Tc doped C3N nanosensors, but also offer a new direction for environmental protection and intelligent detection in industrial applications.
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页数:9
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