The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Trapping Single Molecules by Dielectrophoresis
Abstract
We have trapped single protein molecules of R-phycoerythrin in an aqueous solution by an alternating electric field. A radio frequency voltage is applied to sharp nanoelectrodes and hence produces a strong electric field gradient. The resulting dielectrophoretic forces attract freely diffusing protein molecules. Trapping takes place at the electrode tips. Switching off the field immediately releases the molecules. The electric field distribution is computed, and from this the dielectrophoretic response of the molecules is calculated using a standard polarization model. The resulting forces are compared to the impact of Brownian motion. Finally, we discuss the experimental observations on the basis of the model calculations.
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Citations
Cites
Jones, T. B. (1995) · doi:10.1017/cbo9780511574498 · Cited by 2,786
Cited by
Dielectrophoresis for manipulation of micro/nano particles in microfluidic systems
Zhang, C., Khoshmanesh, K., Mitchell, A., & Kalantar-zadeh, K. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-009-2922-6 · Cited by 305
Metadata
- Authors
- Hölzel, R., Calander, N., Chiragwandi, Z., Willander, M., & Bier, F. F.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2005-09-13
- Volume
- 95
- Issue
- 12
- Pages
- 128102-128102
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2000350057
- Cited by
- 197
- References
- 1
- FWCI
- 8.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hölzel, R., Calander, N., Chiragwandi, Z., Willander, M., & Bier, F. F. (2005). Trapping Single Molecules by Dielectrophoresis. Physical Review Letters. https://doi.org/10.1103/physrevlett.95.128102
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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.
