Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition

Title data

Rein, Valentina ; Gao, Hao ; Heenen, Hendrik H. ; Sghaier, Wissal ; Manikas, Anastasios C. ; Tsakonas, Christos ; Saedi, Mehdi ; Margraf, Johannes T. ; Galiotis, Costas ; Renaud, Gilles ; Konovalov, Oleg V. ; Groot, Irene M. N. ; Reuter, Karsten ; Jankowski, Maciej:
Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition.
In: ACS Nano. Vol. 18 (2024) Issue 19 . - pp. 12503-12511.
ISSN 1936-086X
DOI: https://doi.org/10.1021/acsnano.4c02070

Official URL:

Abstract in another language

In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor deposition on a liquid copper catalyst. By monitoring the growing graphene flakes in real time using in situ radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH4-to-H2 pressure ratios and deposition temperatures. Constant growth rates of the flakes’ radius indicate a growth mode limited by precursor attachment, whereas methane-flux-dependent flake shapes point to limited precursor availability. Large-scale free energy simulations enabled by an efficient machine-learning moment tensor potential trained to density functional theory data provide quantitative barriers for key atomic-scale growth processes. The wealth of experimental and theoretical data can be consistently combined into a microkinetic model that reveals mixed growth kinetics that, in contrast to the situation at solid Cu, is partly controlled by precursor attachment alongside precursor availability. Key mechanistic aspects that directly point toward the improved graphene quality are a largely suppressed carbon dimer attachment due to the facile incorporation of this precursor species into the liquid surface and a low-barrier ring-opening process that self-heals 5-membered rings resulting from remaining dimer attachments.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	graphene; chemical vapor deposition; liquid metal catalysts; growth kinetics; machine-learning potentials; biased molecular dynamics; free energy simulations
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT:	Yes
DDC Subjects:	500 Science > 540 Chemistry
Date Deposited:	13 Jan 2025 08:23
Last Modified:	13 Jan 2025 08:23
URI:	https://eref.uni-bayreuth.de/id/eprint/91542