Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 420

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Chemical Vapor Deposition-Derived Graphene with Electrical Performance of Exfoliated Graphene

Petrone, Nicholas, Dean, Cory R., Meric, Inanc, van der Zande, Arend M., Huang, Pinshane Y., Wang, Lei, Muller, David, Shepard, Kenneth L., Hone, James·2012·Nano Letters

Abstract

While chemical vapor deposition (CVD) promises a scalable method to produce large-area graphene, CVD-grown graphene has heretofore exhibited inferior electronic properties in comparison with exfoliated samples. Here we test the electrical transport properties of CVD-grown graphene in which two important sources of disorder, namely grain boundaries and processing-induced contamination, are substantially reduced. We grow CVD graphene with grain sizes up to 250 μm to abate grain boundaries, and we transfer graphene utilizing a novel, dry-transfer method to minimize chemical contamination. We fabricate devices on both silicon dioxide and hexagonal boron nitride (h-BN) dielectrics to probe the effects of substrate-induced disorder. On both substrate types, the large-grain CVD graphene samples are comparable in quality to the best reported exfoliated samples, as determined by low-temperature electrical transport and magnetotransport measurements. Small-grain samples exhibit much greater variation in quality and inferior performance by multiple measures, even in samples exhibiting high field-effect mobility. These results confirm the possibility of achieving high-performance graphene devices based on a scalable synthesis process.

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/nl204481s

Cites

1 of 1 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

Cited by

See all 420

Metadata

Authors
Petrone, N., Dean, C. R., Meric, I., van der Zande, A. M., Huang, P. Y., Wang, L., Muller, D., Shepard, K. L., & Hone, J.
Venue
Nano Letters
Published
2012-05-29
Volume
12
Issue
6
Pages
2751-2756
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W1978679821
Cited by
420
References
1
FWCI
24.04
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.