Bibliographic Details
| Title: |
Understanding dopant and defect effect on H2S sensing performances of graphene: A first-principles study |
| Authors: |
Zhang, Yong-Hui1 yonghuizhang05@gmail.com, Han, Li-Feng1, Xiao, Yuan-Hua1, Jia, Dian-Zeng2, Guo, Zhan-Hu3, Li, Feng1 lifeng696@yahoo.com |
| Source: |
Computational Materials Science. Mar2013, Vol. 69, p222-228. 7p. |
| Subjects: |
Graphene, Doping agents (Chemistry), Hydrogen, Density functionals, Green's functions, Nanostructures, Performance evaluation |
| Abstract: |
Abstract: The interaction between hydrogen sulfite (H2S) and graphene was investigated by density functional theory calculations and nonequilibrium Green’s function formalism. The structural and electronic properties of H2S–graphene systems were studied by tuning the geometries of H2S molecule toward 2D nanosheets of pristine, defective and doped graphene. It was found that Ca, Co and Fe doped and defective graphene nanosheets show much higher affinities to H2S molecule in comparison to pristine graphene. The strong interactions between H2S and graphene nanosheets modified with transition metals can lead to dramatic changes to the electronic and magnetic properties of graphene. The electronic transport behaviors of Fe-doped graphene nanosheets indicate that the chemical sensors constructed with the materials could exhibit much higher sensitivity for detecting H2S gas, in comparison with that of devices made with pristine graphene. It is possible to design H2S chemical sensors with highly improved performances, using graphene nanosheets as sensing materials with appropriate metal dopants or defects. In addition, the graphene doped with Si absorbs H2S molecule through forming Si–S bond, compared to the weak physisorption of H2S molecule onto pristine and the B, N doped graphene. [Copyright &y& Elsevier] |
|
Copyright of Computational Materials Science is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |