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Cited by 301

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Atom-by-atom nucleation and growth of graphene nanopores

Open access
Russo, Christopher J., Golovchenko, J. A.·2012·Proceedings of the National Academy of Sciences

Abstract

Graphene is an ideal thin membrane substrate for creating molecule-scale devices. Here we demonstrate a scalable method for creating extremely small structures in graphene with atomic precision. It consists of inducing defect nucleation centers with energetic ions, followed by edge-selective electron recoil sputtering. As a first application, we create graphene nanopores with radii as small as 3 Å, which corresponds to 10 atoms removed. We observe carbon atom removal from the nanopore edge in situ using an aberration-corrected electron microscope, measure the cross-section for the process, and obtain a mean edge atom displacement energy of 14.1 ± 0.1 eV. This approach does not require focused beams and allows scalable production of single nanopores and arrays of monodisperse nanopores for atomic-scale selectively permeable membranes.

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Crossref · 10.1073/pnas.1119827109

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Metadata

Authors
Russo, C. J., & Golovchenko, J. A.
Venue
Proceedings of the National Academy of Sciences
Published
2012-04-06
Volume
109
Issue
16
Pages
5953-5957
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1988345588
Cited by
301
References
21
FWCI
10.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Atom-by-atom nucleation and growth of graphene nanopores. https://app.cochat.ai/papers/10.1073/pnas.1119827109
The paper
Russo, C. J., & Golovchenko, J. A. (2012). Atom-by-atom nucleation and growth of graphene nanopores. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1119827109

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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.