Selective sensing and mechanism of patterned graphene-based sensors: Experiments and DFT calculations. (16th January 2022)

• Record Type: Journal Article
• Authors: Ye, Xiaohui; Qi, Ming; Yang, Houyong; Mediko, Francis Sona; Qiang, Hao; Yang, Yanling; He, Chaozheng
• Is Part Of: Chemical engineering science. Volume 247(2022)
• Publication Date: 2022-01-16
• Subjects: Selective sensing -- Patterned graphene -- Femtosecond laser -- DFT calculation -- Sensing mechanism; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.117017
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: Selective sensitivity of CO/NO/H2 O-mixed gas was derived from topologic graphene. The given topology can precisely detect one specific gas from mixed gases. The topologic graphene had superb sensing performance with high GF of ~10 4 . DFT was carried out to further understand the mechanism of selective sensing. Abstract: Graphene with antidots shows great potential in wearable devices and highly sensitive sensors. In this paper, we fabricated the patterned graphenes of circle, square, and triangle by ultrafast laser processing. The given patterned graphene can precisely detect one specific gas from the CO/NO/H2 O-mixed gases. The sensing performance results demonstrated that the circle patterned graphene (cir-graphene) was most sensitive to CO with the highest sensitivity with the gauge factor (GF) of 3.9 × 10 4 . For NO sensing, the triangle patterned graphene (tri-graphene) conducted the best results. The square patterned graphene (squ-graphene) was the most sensitive to H2 O. The DFT calculations revealed that CO was chemical adsorbed on cir-graphene with the most negative adsorption energy (−3.638 eV). For NO sensing, tri-graphene performed best. While, the patterned graphene was not available to H2 O sensing. This work offers a novel solution to next-generation wearable multifunctional sensors, and a new insight for the selective sensing mechanism.