Kinetic study of graphene growth: Temperature perspective on growth rate and film thickness by chemical vapor deposition

Xing, Sirui; Wu, Wei; Wang, Yanan; Bao, Jiming; Pei, S. S.

doi:10.1016/j.cplett.2013.06.047

Cited by 51 publications

(47 citation statements)

References 37 publications

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“…Several studies have also shown improvement in the graphene growth at higher temperature using quartz tube reactor. Xing et al, 25 attributed the temperature dependent graphene thickness to the difference in the amount of the consumed carbon concentration during the nucleation. At low temperatures, the amount of consumed carbon during the growth is higher compared to the amount consumed at higher temperatures which results in the formation of multilayer nucleation.…”

Section: Effect Of Growth Temperaturementioning

confidence: 99%

Toward fast growth of large area high quality graphene using a cold-wall CVD reactor

et al. 2017

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Section: Effect Of Growth Temperaturementioning

confidence: 99%

Toward fast growth of large area high quality graphene using a cold-wall CVD reactor

et al. 2017

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“…In the absence of significant surface roughness, the remaining pinholes likely originate via another mechanism. Previous studies by Xing et al, Loginova et al, and Li et al have suggested that the growth rate of graphene becomes significantly slower when the coverage nears completion, [37][38][39] in which case pinholes may arise from prematurely terminating growth before these pinholes can fully close. To evaluate this hypothesis, we next vary the growth duration on the Cu(111) films.…”

Section: G Cumentioning

confidence: 99%

Controlling the density of pinhole defects in monolayer graphene synthesized via chemical vapor deposition on copper

Roy

Jacobberger

Wan

et al. 2016

Carbon

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The chemical vapor deposition of monolayer graphene on metal foils and thin films typically yields graphene that appears to be continuous and complete but that actually contains a high density of pinhole defects. These pinhole defects can be present at densities greater than 1 µm-2 and are problematic for many applications, ranging from the implementation of graphene in electronics to its use as a diffusion barrier. Here, we characterize pinhole defects in graphene films that are grown on Cu foils and epitaxial Cu thin films using CH 4 as the precursor. On Cu foils, a relationship between surface roughness and the pinhole defect density is observed. A reduction in pinholes by a factor of ~40 is realized by decreasing the foil surface roughness via increasing the annealing temperature and duration. The pinhole density is further reduced by a factor of ~200 by using smoother epitaxial Cu thin films and additionally by extending the growth duration past the point of visual completion, as characterized by scanning electron microscopy. This study is expected to serve as a foundation for developing high quality substrates for graphene synthesis and realizing pinhole-free graphene.

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“…temperature and pressure), since faster growth kinetics will lead to larger area graphene. The mechanisms underlying graphene growth and their relationship to the process conditions have recently received much interest (Bhaviripudi et al ., ; Celebi et al ., ; Kim et al ., ; Xing et al ., ; Vlassiouk et al ., ), albeit through deterministic models. Recently, Wu and Huang () proposed a novel approach to model the growth kinetics of the graphene flakes deterministically, using the well‐studied confined exponential model.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Graphene Growth Kinetics Model

Sosina

Dasgupta

Huang

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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Graphene is an emerging nanomaterial for a wide variety of novel applications. Controlled synthesis of high quality graphene sheets requires analytical understanding of graphene growth kinetics. Graphene growth via chemical vapour deposition starts with randomly nucleated islands that gradually develop into complex shapes, grow in size and eventually connect together to form a graphene sheet. Models proposed for this stochastic process do not, in general, permit assessment of uncertainty. We develop a stochastic framework for the growth process and propose Bayesian inferential models, which account for the data collection mechanism and allow for uncertainty analyses, for learning about the kinetics from experimental data. Furthermore, we link the growth kinetics with controllable experimental factors, thus providing a framework for statistical design and analysis of future experiments.

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Kinetic study of graphene growth: Temperature perspective on growth rate and film thickness by chemical vapor deposition

Cited by 51 publications

References 37 publications

Toward fast growth of large area high quality graphene using a cold-wall CVD reactor

Toward fast growth of large area high quality graphene using a cold-wall CVD reactor

Controlling the density of pinhole defects in monolayer graphene synthesized via chemical vapor deposition on copper

A Stochastic Graphene Growth Kinetics Model

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