Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-19T17:26:18.316Z Has data issue: false hasContentIssue false
  • English
  • Français

On the sulfur doping of γ-graphdiyne: A Molecular Dynamics and DFT study

Published online by Cambridge University Press:  13 May 2020

Eliezer Fernando Oliveira ,
Augusto Batagin-Neto  and
Douglas Soares Galvao
Eliezer Fernando Oliveira
Affiliation:
Gleb Wataghin Institute of Physics, University of Campinas (UNICAMP), Campinas, SP, Brazil. Center for Computational Engineering & Sciences (CCES), University of Campinas (UNICAMP), Campinas, SP, Brazil. Department of Materials Science and Nanoengineering, Rice University, Houston, TX, United States.
Augusto Batagin-Neto
Affiliation:
São Paulo State University (UNESP). Campus of Itapeva, Itapeva18409-010, SP, Brazil.
Douglas Soares Galvao
Affiliation:
Gleb Wataghin Institute of Physics, University of Campinas (UNICAMP), Campinas, SP, Brazil. Center for Computational Engineering & Sciences (CCES), University of Campinas (UNICAMP), Campinas, SP, Brazil.
Get access

Abstract

Recently, an experimental study developed an efficient way to obtain sulfur-doped γ-graphdiyne. This study has shown that this new material could have promising applications in lithium-ion batteries, but the complete understanding of how the sulfur atoms are incorporated into the graphdiyne network is still missing. In this work, we have investigated the sulfur doping process through molecular dynamics and density functional theory simulations. Our results suggest that the doped induced distortions of the γ-graphdiyne pores prevent the incorporation of more than two sulfur atoms. The most common configuration is the incorporation of just one sulfur atom per the graphdiyne pore.

Type
Articles
Information
MRS Advances , Volume 5 , Issue 52-53: Nanoscale and Quantum Materials , 2020 , pp. 2701 - 2706
Check for updates
Copyright
Copyright © 2020 Materials Research Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References:

Sun, Y., Wu, Q., Shi, G., Energy Environ. Sci. 4, 1113 (2011).CrossRefGoogle Scholar
Lee, H., Paeng, K., Kim, I. S., Synth. Met. 244, 36 (2018).CrossRefGoogle Scholar
Yun, Y.S., Le, V.-D., Kim, H., Chang, S.-J., Baek, S.J., Park, S., Kim, B.H., Kim, Y.-H., Kang, K., Jin, H.-J., J. Power Sources 262, 79 (2014).CrossRefGoogle Scholar
Yang, Z., Cui, W., Wang, K., Song, Y., Zhao, F., Wang, N., Long, Y., Wang, H., Huang, C., Chem. Eur. J. 25, 5643 (2019).Google Scholar
van Duin, A. C. T., Dasgupta, S., Lorant, F., Goddard, W. A., J. Phys. Chem. A 105, 9396 (2001)CrossRefGoogle Scholar
Plimpton, S. J., Comput. Phys. 117, 1 (1995).CrossRefGoogle Scholar
Perdew, J. P., Burke, K., Ernzerhof, M., Phys. Rev. Lett. 77, 3865 (1996).CrossRefGoogle Scholar
Giannozzi, P., et al. , J. Phys.: Condens. Matter. 21, 395502 (2009).Google Scholar
Giannozzi, P., et al. , J. Phys.: Condens. Matter. 29, 465901 (2017).Google Scholar
Lide, D. R., CRC handbook of chemistry and physics. 88th ed. CRC Press: Boca Raton, 2007.Google Scholar
NIST Computational Chemistry Comparison and Benchmark Database: NIST Standard Reference Database Number 101. Release 20, August 2019, Editor: Russell D. Johnson III. https://cccbdb.nist.gov (accessed in 21/02/2020).Google Scholar