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Cited by 499

The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.

Atomically Thin Boron Nitride: Unique Properties and Applications

Open access
Li, Lu Hua, Chen, Ying·2016·Advanced Functional Materials

Abstract

Atomically thin boron nitride (BN) is an important 2D nanomaterial, with many properties distinct from graphene. In this feature article, these unique properties and associated applications, often not feasible with graphene, are outlined. The article starts with characterization and identification of atomically thin BN. It is followed by demonstrating their strong oxidation resistance at high temperatures and applications in protecting metals from oxidation and corrosion. As flat insulators, BN nanosheets are ideal dielectric substrates for surface enhanced Raman spectroscopy (SERS) and electronic devices based on 2D heterostructures. The light emission of BN nanosheets in the deep ultraviolet (DUV) and ultraviolet (UV) regions is also included for its scientific and technological importance. The last part is dedicated to synthesis, characterization, and optical properties of BN nanoribbons, a special form of nanosheets.

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Crossref · 10.1002/adfm.201504606

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Metadata

Authors
Li, L. H., & Chen, Y.
Venue
Advanced Functional Materials
Publisher
Wiley
Published
2016-02-16
Volume
26
Issue
16
Pages
2594-2608
ISSN
  • 1616-301X (ISSN-L)
  • 1616-3028
OpenAlex
W2286419647
Cited by
499
References
62
FWCI
19.65
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Atomically Thin Boron Nitride: Unique Properties and Applications. https://app.cochat.ai/papers/10.1002/adfm.201504606
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Li, L. H., & Chen, Y. (2016). Atomically Thin Boron Nitride: Unique Properties and Applications. Advanced Functional Materials. https://doi.org/10.1002/adfm.201504606

Data sources

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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.