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Charge Transfer from Ammonia Physisorbed on Nanotubes
Abstract
We report the use of nanotube field-effect transistor devices for chemical sensing in a conducting liquid environment. Detection of ammonia occurs through the shift of the gate voltage dependence of the source-drain current. We attribute this shift to charge transfer from adsorbed ammonia molecules, with the amount of charge estimated to be as small as 40 electrons for the smallest shift detected. Using the concentration dependence of the response as an adsorption isotherm, we are able to measure the amount of charge transfer to be 0.04 electron per ammonia molecule.
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Cited by
Detection of individual gas molecules adsorbed on graphene
Schedin, F., Geim, A. K., Morozov, S. V., Hill, E. W., Blake, P., Katsnelson, M. I., & Novoselov, K. S. (2007) · Nature Materials · doi:10.1038/nmat1967 · Cited by 8,028
Carbon nanotube films for transparent and plastic electronics
Gruner, G. (2006) · Journal of Materials Chemistry · doi:10.1039/b603821m · Cited by 567
Metadata
- Authors
- Bradley, K., Gabriel, J.-C. P., Briman, M., Star, A., & Grüner, G.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2003-11-17
- Volume
- 91
- Issue
- 21
- Pages
- 218301-218301
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2011022767
- Cited by
- 192
- References
- 0
- FWCI
- 13.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Charge Transfer from Ammonia Physisorbed on Nanotubes. https://app.cochat.ai/papers/10.1103/physrevlett.91.218301
- The paper
- Bradley, K., Gabriel, J.-C. P., Briman, M., Star, A., & Grüner, G. (2003). Charge Transfer from Ammonia Physisorbed on Nanotubes. Physical Review Letters. https://doi.org/10.1103/physrevlett.91.218301
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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.
