The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deriving Carbon Atomic Chains from Graphene
Abstract
Stable and rigid carbon atomic chains were experimentally realized by removing carbon atoms row by row from graphene through the controlled energetic electron irradiation inside a transmission electron microscope. The observed structural dynamics of carbon atomic chains such as formation, migration, and breakage were well explained by density-functional theory calculations. The method we reported here is promising to investigate all-carbon-based devices with the carbon atomic chains as the conducting channel, which can be regarded as the ultimate basic component of molecular devices.
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.
Integrity signals
Registry
Cites
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.
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
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732
Geim, A. K., & Novoselov, K. S. (2007) · Nature Materials · doi:10.1038/nmat1849 · Cited by 39,979
Cited by
Trung, T. Q., & Lee, N. (2016) · Advanced Materials · doi:10.1002/adma.201504244 · Cited by 2,160
Graphene-based smart materials
Yu, X., Cheng, H., Zhang, M., Zhao, Y., Qu, L., & Shi, G. (2017) · Nature Reviews Materials · doi:10.1038/natrevmats.2017.46 · Cited by 796
Extremely Elastic Wearable Carbon Nanotube Fiber Strain Sensor for Monitoring of Human Motion
Ryu, S., Lee, P., Chou, J. B., Xu, R., Zhao, R., Hart, A. J., & Kim, S.-G. (2015) · ACS Nano · doi:10.1021/acsnano.5b00599 · Cited by 770
Metadata
- Authors
- Jin, C., Lan, H., Peng, L., Suenaga, K., & Iijima, S.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2009-05-18
- Volume
- 102
- Issue
- 20
- Pages
- 205501-205501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W1986993716
- Cited by
- 629
- References
- 3
- FWCI
- 24.56
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deriving Carbon Atomic Chains from Graphene. https://app.cochat.ai/papers/10.1103/physrevlett.102.205501
- The paper
- Jin, C., Lan, H., Peng, L., Suenaga, K., & Iijima, S. (2009). Deriving Carbon Atomic Chains from Graphene. Physical Review Letters. https://doi.org/10.1103/physrevlett.102.205501
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.
