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Cited by 568

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

Large Magnetic Anisotropy of a Single Atomic Spin Embedded in a Surface Molecular Network

Hirjibehedin, Cyrus F., Lin, Chiung-Yuan, Otte, Alexander F., Ternes, Markus, Lutz, Christopher P., Jones, Barbara A., Heinrich, Andreas J.·2007·Science

Abstract

Magnetic anisotropy allows magnets to maintain their direction of magnetization over time. Using a scanning tunneling microscope to observe spin excitations, we determined the orientation and strength of the anisotropies of individual iron and manganese atoms on a thin layer of copper nitride. The relative intensities of the inelastic tunneling processes are consistent with dipolar interactions, as seen for inelastic neutron scattering. First-principles calculations indicate that the magnetic atoms become incorporated into a polar covalent surface molecular network in the copper nitride. These structures, which provide atom-by-atom accessibility via local probes, have the potential for engineering anisotropies large enough to produce stable magnetization at low temperatures for a single atomic spin.

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Metadata

Authors
Hirjibehedin, C. F., Lin, C.-Y., Otte, A. F., Ternes, M., Lutz, C. P., Jones, B. A., & Heinrich, A. J.
Venue
Science
Published
2007-08-31
Volume
317
Issue
5842
Pages
1199-1203
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2078333055
Cited by
568
References
1
FWCI
21.80
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Large Magnetic Anisotropy of a Single Atomic Spin Embedded in a Surface Molecular Network. https://app.cochat.ai/papers/10.1126/science.1146110
The paper
Hirjibehedin, C. F., Lin, C.-Y., Otte, A. F., Ternes, M., Lutz, C. P., Jones, B. A., & Heinrich, A. J. (2007). Large Magnetic Anisotropy of a Single Atomic Spin Embedded in a Surface Molecular Network. Science. https://doi.org/10.1126/science.1146110

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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.