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Computational Study of Double Nitrogen Doping on Graphene

Undergraduate #48
Discipline: Chemistry and Chemical Sciences
Subcategory: Nanoscience

Dinushka Herath - Clark Atlanta University
Co-Author(s): Tandabany Dinadayalane, Clark Atlanta University, Atlanta, GA



Graphene, which is a one-atom thick sheet, is the thinnest as well as the strongest known material. Each carbon atom in graphene is covalently bonded to three other carbon atoms with sp2 hybridization. Cost effective and efficient catalysts for oxygen reduction reaction are required for commercialization of the fuel cell technology. Recent study revealed that N-doped carbon nanomaterials could be used as catalysts for oxygen reduction reaction (ORR) in acidic fuel cells. Quantum mechanical calculations are useful to understand the N-doping induced charge transfer that has great impact on the design and development of metal-free heteroatom-doped graphene-based catalysts for fuel cell and many other applications. In our computational investigation, we have examined the structures and relative stabilities of double nitrogen doped graphene nanostructures using M06-2X/6-31G(d) and B3LYP/6-31G(d) levels. A finite-size graphene model of C82H24 was considered for doping of two nitrogen atoms at different positions. In our study of double nitrogen doping, two carbon atoms at various positions were replaced by two nitrogen atoms. Our computational study concludes that two nitrogen atoms prefer to be farther away rather than staying closer to each other within a single six-membered ring. We have analyzed the effect of nitrogen doping and the positions of two nitrogen atoms on the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO) energies and compared these results with respect to HOMO-LUMO energy gap of pristine graphene. Future research involves analyzing the election distribution changes when we change the positions of two nitrogen atoms in the finite-size graphene.

References: Shui, J.; Wang, M.; Du, F.; Dai, L. Sci. Adv. 2015, 1, e1400129. Reddy, A. L. M.; Srivastava, A.; Gowda, S. R.; Gullapalli, H.; Dubey, M.; Ajayan, P. M. ACS Nano 2010, 4, 6337-6342. Lee, C.; Wei, X. D.; Kysar, J. W.; Hone, J. Science, 2008, 321, 385-388.

Funder Acknowledgement(s): The National Science Foundation (NSF) and the state of Illinois are acknowledged for the financial support of Blue Waters sustained-petascale computing project (awards OCI-0725070 and ACI-1238993). NSF is acknowledged for the financial support through Georgia-Alabama Louis Stokes Alliance for Minority Participation (GA-AL LSAMP) Grant #1305041.

Faculty Advisor: Dinadayalane Tandabany,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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