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Quantitative Determination of Organic Semiconductor Microstructure from the Molecular to Device Scale

Rivnay, Jonathan, Mannsfeld, Stefan C. B., Miller, Chad E., Salleo, Alberto, Toney, Michael F.·2012·Chemical Reviews

Abstract

A study was conducted to demonstrate quantitative determination of organic semiconductor microstructure from the molecular to device scale. The quantitative determination of organic semiconductor microstructure from the molecular to device scale was key to obtaining precise description of the molecular structure and microstructure of the materials of interest. This information combined with electrical characterization and modeling allowed for the establishment of general design rules to guide future rational design of materials and devices. Investigations revealed that a number and variety of defects were the largest contributors to the existence of disorder within a lattice, as organic semiconductor crystals were dominated by weak van der Waals bonding. Crystallite size, texture, and variations in structure due to spatial confinement and interfaces were also found to be relevant for transport of free charge carriers and bound excitonic species over distances that were important for device operation.

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Metadata

Authors
Rivnay, J., Mannsfeld, S. C. B., Miller, C. E., Salleo, A., & Toney, M. F.
Venue
Chemical Reviews
Published
2012-08-09
Volume
112
Issue
10
Pages
5488-5519
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
OpenAlex
W2317506299
Cited by
1,419
References
0
FWCI
6.77
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Quantitative Determination of Organic Semiconductor Microstructure from the Molecular to Device Scale. https://app.cochat.ai/papers/10.1021/cr3001109
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Rivnay, J., Mannsfeld, S. C. B., Miller, C. E., Salleo, A., & Toney, M. F. (2012). Quantitative Determination of Organic Semiconductor Microstructure from the Molecular to Device Scale. Chemical Reviews. https://doi.org/10.1021/cr3001109

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.