Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 823

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Role of Kinetic Factors in Chemical Vapor Deposition Synthesis of Uniform Large Area Graphene Using Copper Catalyst

Bhaviripudi, Sreekar, Jia, Xiaoting, Dresselhaus, Mildred S., Kong, Jing·2010·Nano Letters

Abstract

In this article, the role of kinetics, in particular, the pressure of the reaction chamber in the chemical vapor deposition (CVD) synthesis of graphene using low carbon solid solubility catalysts (Cu), on both the large area thickness uniformity and the defect density are presented. Although the thermodynamics of the synthesis system remains the same, based on whether the process is performed at atmospheric pressure (AP), low pressure (LP) (0.1-1 Torr) or under ultrahigh vacuum (UHV) conditions, the kinetics of the growth phenomenon are different, leading to a variation in the uniformity of the resulting graphene growth over large areas (wafer scale). The kinetic models for APCVD and LPCVD are discussed, thereby providing insight for understanding the differences between APCVD vs LPCVD/UHVCVD graphene syntheses. Interestingly, graphene syntheses using a Cu catalyst in APCVD processes at higher methane concentrations revealed that the growth is not self-limiting, which is in contrast to previous observations for the LPCVD case. Additionally, nanoribbons and nanostrips with widths ranging from 20 to 100 nm were also observed on the APCVD grown graphene. Interactions between graphene nanofeatures (edges, folds) and the contaminant metal nanoparticles from the Cu etchant were observed, suggesting that these samples could potentially be employed to investigate the chemical reactivity of single molecules, DNA, and nanoparticles with monolayer graphene.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/nl102355e

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

Metadata

Authors
Bhaviripudi, S., Jia, X., Dresselhaus, M. S., & Kong, J.
Venue
Nano Letters
Published
2010-09-02
Volume
10
Issue
10
Pages
4128-4133
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W2061366494
Cited by
823
References
3
FWCI
30.89
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.