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Effect of Nanoscale Curvature of Single-Walled Carbon Nanotubes on Adsorption of Polycyclic Aromatic Hydrocarbons
Abstract
Liquid-phase adsorption of tetracene and phenanthrene on a single-walled carbon nanotube (SWCNT) was examined. Tetracene adsorption was more than six times greater than that of phenanthrene. X-ray photoelectron spectroscopic examination clearly showed that tetracene and phenanthrene molecules efficiently coated the SWCNT external surfaces. The remarkable difference between the adsorption amounts of tetracene and phenanthrene was caused by the nanoscale curvature effect of the tube surface, resulting in a difference in the amount of contact between the molecule and the tube surface. The adsorption of tetracene and phenanthrene caused a significant higher frequency shift in the radial breathing mode (RBM) of the Raman band of the SWCNT, indicating an intensive pi-pi interaction between these polycyclic aromatic hydrocarbons and the external SWCNT surface.
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Zhu, D., & Pignatello, J. J. (2005) · Environmental Science & Technology · doi:10.1021/es0491376 · Cited by 425
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Lin, D., & Xing, B. (2008) · Environmental Science & Technology · doi:10.1021/es801297u · Cited by 593
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Metadata
- Authors
- Gotovac, S., Honda, H., Hattori, Y., Takahashi, K., Kanoh, H., & Kaneko, K.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-02-23
- Volume
- 7
- Issue
- 3
- Pages
- 583-587
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2067377831
- Cited by
- 283
- References
- 1
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gotovac, S., Honda, H., Hattori, Y., Takahashi, K., Kanoh, H., & Kaneko, K. (2007). Effect of Nanoscale Curvature of Single-Walled Carbon Nanotubes on Adsorption of Polycyclic Aromatic Hydrocarbons. Nano Letters. https://doi.org/10.1021/nl0622597
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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.
