DFT-based investigation on adsorption of methane on pristine and defected graphene
- 264 Downloads
The investigation of methane adsorption on surface of graphene is of high interest as graphene can be tuned as required, also the favorable adsorption properties may be employed to create cost-effective novel storage devices. In the present study, we investigate the adsorption characteristics of methane (CH4) on eight different kinds of hydrogen-capped graphene sheet with variable and high number of carbon atoms using density functional theory methods. Our results infer that the methane adsorption is high on defected graphene than that of pristine and less charge transfer between CH4 and graphene. The physisorbed methane molecule on graphene sheet has enhanced adsorption energy with a high number of carbon atoms and the value is −0.184 and −0.185 eV (pristine graphene) and −0.188 and −0.191 eV (defected graphene). Due to high barrier, the transfer of electrons from graphene to methane is hard rather than methane to graphene. Further, it is found that the energy gap value of the hydrogen-capped graphene sheets decreases upon adsorption of methane. The reduced density gradient (RDG) scatter graph shows the interaction to be weak van der Waals interaction with the steric repulsion in the graphene sheet.
KeywordsPristine and defected graphene Methane adsorption RDG analysis Physisorption Density of states (DOS)
One of the authors (SV) highly acknowledges the Department of Science and Technology (DST-SERB), Government of India, for the financial support in the form of a project under Grant SR/FTP/PS-115/2011.
- 1.Baxter J, Bian Z, Chen G, Danielson D, Dresselhaus MS, Fedorov AG, Fisher TS, Jones CW, Maginn E, Kortshagen U, Manthiram A, Nozik A, Rolison DR, Sands T, Shi L, Wu Y (2009) Energy Environ 2:559–588Google Scholar
- 17.Thrower PA (1969) In: Walker PL (ed) Chemistry and physics of carbon. Dekker, New YorkGoogle Scholar
- 29.Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA, Peralta Jr JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi Pomelli RC, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, Revision A.1. Gaussian, Inc, Wallingford Google Scholar
- 31.Umadevi D, Sastry GN (2014) Curr Sci 106:1224–1234Google Scholar
- 32.Lu T Multiwfn 2.1, http://multiwfn.codeplex.com/