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The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Electronic structure and magnetic properties of graphitic ribbons

Open access
Pisani, L., Chan, J. A., Montanari, B., Harrison, N. M.·2007·Physical Review B

Abstract

First-principles calculations are used to establish that the electronic structure of graphene ribbons with zigzag edges is unstable with respect to magnetic polarization of the edge states. The magnetic interaction between edge states is found to be remarkably long ranged and intimately connected to the electronic structure of the ribbon. Various treatments of electronic exchange and correlation are used to examine the sensitivity of this result to details of the electron-electron interactions, and the qualitative features are found to be independent of the details of the approximation. The possibility of other stablization mechanisms, such as charge ordering and a Peierls distortion, are explicitly considered and found to be unfavorable for ribbons of reasonable width. These results have direct implications for the control of the spin-dependent conductance in graphitic nanoribbons using suitably modulated magnetic fields.

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Crossref · 10.1103/physrevb.75.064418

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Metadata

Authors
Pisani, L., Chan, J. A., Montanari, B., & Harrison, N. M.
Venue
Physical Review B
Published
2007-02-16
Volume
75
Issue
6
ISSN
  • 1098-0121 (ISSN-L)
  • 1550-235X
OpenAlex
W2079468187
Cited by
649
References
35
FWCI
26.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Electronic structure and magnetic properties of graphitic ribbons. https://app.cochat.ai/papers/10.1103/physrevb.75.064418
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Pisani, L., Chan, J. A., Montanari, B., & Harrison, N. M. (2007). Electronic structure and magnetic properties of graphitic ribbons. Physical Review B. https://doi.org/10.1103/physrevb.75.064418

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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.