Multiscale Model of CVD Growth of Graphene on Cu(111) Surface.
CVD growth
density functional theory
graphene
kinetic Monte Carlo
multiscale modeling
Journal
International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791
Informations de publication
Date de publication:
10 May 2023
10 May 2023
Historique:
received:
30
03
2023
revised:
04
05
2023
accepted:
05
05
2023
medline:
29
5
2023
pubmed:
27
5
2023
entrez:
27
5
2023
Statut:
epublish
Résumé
Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments.
Identifiants
pubmed: 37239915
pii: ijms24108563
doi: 10.3390/ijms24108563
pmc: PMC10217890
pii:
doi:
Substances chimiques
Graphite
7782-42-5
Carbon
7440-44-0
Gases
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : GRK 2450 "Scale bridging methods of computational nanoscience"
Organisme : Ministry of Science, Research and Art of Baden-Württemberg, Germany
ID : Brigitte-Schlieben-Lange-Programm
Références
Science. 2004 Oct 22;306(5696):666-9
pubmed: 15499015
Nano Lett. 2008 Oct;8(10):3137-40
pubmed: 18763832
Nano Res. 2008;1(3):203-212
pubmed: 20216934
Acta Biomater. 2013 Dec;9(12):9243-57
pubmed: 23958782
J Mol Model. 2014 Jul;20(7):2260
pubmed: 24939464
Phys Rev Lett. 2015 May 29;114(21):216102
pubmed: 26066446
Nano Lett. 2010 Oct 13;10(10):4128-33
pubmed: 20812667
Nano Lett. 2009 Jan;9(1):422-6
pubmed: 19099364
Nature. 2009 Feb 5;457(7230):706-10
pubmed: 19145232
Nanomaterials (Basel). 2022 Aug 27;12(17):
pubmed: 36080001
Adv Mater. 2016 Jun;28(22):4184-202
pubmed: 26728114
Nanoscale. 2014 Dec 21;6(24):15255-61
pubmed: 25381813
Nano Lett. 2010 Nov 10;10(11):4328-34
pubmed: 20957985
Sci Rep. 2014 Jun 13;4:5278
pubmed: 24923290
Nano Lett. 2008 Mar;8(3):902-7
pubmed: 18284217
Nano Lett. 2012 Jun 13;12(6):2773-7
pubmed: 22563791
Nano Lett. 2009 Dec;9(12):4268-72
pubmed: 19711970
ACS Nano. 2022 Jan 25;16(1):285-294
pubmed: 34965103
Nano Lett. 2010 Aug 11;10(8):3163-7
pubmed: 20608744
Phys Chem Chem Phys. 2011 Dec 14;13(46):20760-5
pubmed: 21996683
J Chem Phys. 2010 Apr 21;132(15):154104
pubmed: 20423165
Science. 2010 Feb 5;327(5966):662
pubmed: 20133565
Nature. 2011 Apr 7;472(7341):74-8
pubmed: 21475197
Science. 2008 Jun 6;320(5881):1308
pubmed: 18388259
Chem Soc Rev. 2008 Oct;37(10):2163-71
pubmed: 18818819
Phys Rev B Condens Matter. 1996 Oct 15;54(16):11169-11186
pubmed: 9984901
J Chem Theory Comput. 2018 Mar 13;14(3):1583-1593
pubmed: 29357239
J Phys Condens Matter. 2020 Apr 10;32(15):155401
pubmed: 31846953
Nanotechnology. 2012 Jan 27;23(3):035603
pubmed: 22173552
J Am Chem Soc. 2011 Mar 9;133(9):2816-9
pubmed: 21309560
Science. 2012 Mar 16;335(6074):1326-30
pubmed: 22422977
Adv Mater. 2019 Mar;31(9):e1801583
pubmed: 30318816
Phys Rev B Condens Matter. 1994 Dec 15;50(24):17953-17979
pubmed: 9976227
Acc Chem Res. 2018 Mar 20;51(3):728-735
pubmed: 29493220
Molecules. 2020 Aug 25;25(17):
pubmed: 32854226
ACS Nano. 2009 Feb 24;3(2):301-6
pubmed: 19236064
J Am Chem Soc. 2013 Jun 19;135(24):9050-4
pubmed: 23701398
Nature. 2006 Jul 20;442(7100):282-6
pubmed: 16855586
Nat Nanotechnol. 2013 Dec;8(12):939-45
pubmed: 24240429
J Chem Phys. 2015 Aug 28;143(8):084109
pubmed: 26328820
Phys Rev B Condens Matter. 1992 Jun 15;45(23):13244-13249
pubmed: 10001404
Phys Rev Lett. 1996 Oct 28;77(18):3865-3868
pubmed: 10062328
Adv Mater. 2011 Aug 16;23(31):3522-5
pubmed: 21726004
ACS Omega. 2018 Jan 16;3(1):455-463
pubmed: 31457904
Science. 2008 Jul 18;321(5887):385-8
pubmed: 18635798
Adv Mater. 2013 Jul 12;25(26):3583-7
pubmed: 23703794
ACS Omega. 2020 Aug 26;5(35):22109-22118
pubmed: 32923769