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Cited by 750

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Evidence for Majorana bound states in an iron-based superconductor

Open access
Wang, Dongfei, Kong, Lingyuan, Fan, Peng, Chen, Hui, Zhu, Shiyu, Liu, Wenyao, Cao, Lu, Sun, Yujie, Du, Shixuan, Schneeloch, John, Zhong, Ruidan, Gu, Genda, Fu, Liang, Ding, Hong, Gao, Hong-Jun·2018·Science

Abstract

An iron home for Majoranas: The surface of the iron-based superconductor FeTe 0.55 Se 0.45 has been identified as a potential topological superconductor and is expected to host exotic quasiparticles called the Majorana bound states (MBSs). Wang et al. looked for signatures of MBSs in this material by using scanning tunneling spectroscopy on the vortex cores formed by the application of a magnetic field. In addition to conventional states, they observed the characteristic zero-bias peaks associated with MBSs and were able to distinguish between the two, owing to the favorable ratios of energy scales in the system. Science , this issue p. 333

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Crossref · 10.1126/science.aao1797

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Metadata

Authors
Wang, D., Kong, L., Fan, P., Chen, H., Zhu, S., Liu, W., Cao, L., Sun, Y., Du, S., Schneeloch, J., Zhong, R., Gu, G., Fu, L., Ding, H., & Gao, H.-J.
Venue
Science
Published
2018-10-19
Volume
362
Issue
6412
Pages
333-335
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2887552177
Cited by
750
References
3
FWCI
40.78
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Evidence for Majorana bound states in an iron-based superconductor. https://app.cochat.ai/papers/10.1126/science.aao1797
The paper
Wang, D., Kong, L., Fan, P., Chen, H., Zhu, S., Liu, W., Cao, L., Sun, Y., Du, S., Schneeloch, J., Zhong, R., Gu, G., Fu, L., Ding, H., & Gao, H.-J. (2018). Evidence for Majorana bound states in an iron-based superconductor. Science. https://doi.org/10.1126/science.aao1797

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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.