Issue 60, 2017

Gap maximum of graphene nanoflakes: a first-principles study combined with the Monte Carlo tree search method

Abstract

The energy gap of graphene nanoflakes is important for their potential application in nano-devices; however, it is still a challenge to perform a systemic search of systems with large gaps due to the presence of numerous candidates. Herein, we showed an ideal feasible approach that involved structural recognition, simplified effective evaluation, and successive optimization strategy. Considering the local bonding environment of carbon atoms, we first proposed a tight-binding model with the parameters fitted from the first-principles calculations of possible GNFs; this model provided an ideal avenue to screen the candidates with high accuracy and efficiency. Via combining the Monte Carlo tree search method and the congruence check, we determined the correlation between structures and the gap distributions according to the carbon numbers, and the results were confirmed via the first-principles calculations. The structural stabilities of the candidates with different numbers of hydrogen atoms might be modulated by the chemical potential of hydrogen, whereas the candidates with larger gaps might be more stable for the isomers with the same number of C and H atoms. Note that the gap variation is dominated by the structural features despite the quantum confinement effect since the gap maximum fluctuates rather than gradually decreasing with the increase in size. Our finding shows the gap variety of GNFs due to the configuration diversity, which may help explore the potential application of GNFs in nano-devices and fluorescence labeling in biomedicine.

Graphical abstract: Gap maximum of graphene nanoflakes: a first-principles study combined with the Monte Carlo tree search method

Supplementary files

Article information

Article type
Paper
Submitted
20 Jun 2017
Accepted
18 Jul 2017
First published
08 Aug 2017
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2017,7, 37881-37886

Gap maximum of graphene nanoflakes: a first-principles study combined with the Monte Carlo tree search method

Z. Cao, Y. Zhao, J. Liao and X. Yang, RSC Adv., 2017, 7, 37881 DOI: 10.1039/C7RA06891C

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements