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Cited by 375

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Organic light-emitting diodes: theoretical understanding of highly efficient materials and development of computational methodology

Open access
Shuai, Zhigang, Peng, Qian·2017·National Science Review

Abstract

Theoretical understanding of organic light-emitting diodes started from the quest to the nature of the primary excitation in organic molecular and polymeric materials. We found the electron correlation strength, bond-length alternation as well as the conjugation extent have strong influences on the orderings of the lowest lying excited states through the first application of density matrix renormalization group theory to quantum chemistry. The electro-injected free carriers (with spin 1/2) can form both singlet and triplet bound states. We found that the singlet exciton formation ratio can exceed the conventional 25% spin statistics limit. We proposed a vibration correlation function formalism to evaluate the excited-state decay rates, which is shown to not only give reasonable estimations for the quantum efficiency but also a quantitative account for the aggregation-induced emission (AIE). It is suggested to unravel the AIE mechanism through resonance Raman spectroscopy.

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Crossref · 10.1093/nsr/nww024

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Metadata

Authors
Shuai, Z., & Peng, Q.
Venue
National Science Review
Published
2016-04-19
Volume
4
Issue
2
Pages
224-239
ISSN
  • 2053-714X (ISSN-L)
  • 2095-5138
OpenAlex
W2341273646
Cited by
375
References
65
FWCI
5.79
Type
journal article
Language
en
Open access
hybrid

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Shuai, Z., & Peng, Q. (2017). Organic light-emitting diodes: theoretical understanding of highly efficient materials and development of computational methodology. National Science Review. https://doi.org/10.1093/nsr/nww024

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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.