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Cited by 223

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Selective dielectrophoretic confinement of bioparticles in potential energy wells

Open access
Wang, X -B, Huang, Y, Burt, J P H, Markx, G H, Pethig, R·1993·Journal of Physics D: Applied Physics

Abstract

The potential energy surfaces generated by microelectrodes of polynomial and interdigitated castellated geometry have been calculated for particles experiencing both positive and negative dielectrophoretic forces. The resulting forms of particle collection at these electrodes are governed by the locations of the potential energy wells, and the theoretical predictions of the modes of collection for particles experiencing positive and negative dielectrophoretic forces are verified using mixtures of viable and non-viable yeast cells, as well as of bacteria and blood cells. An important result for the interdigitated electrodes is the finding that particles trapped in potential energy wells under the action of negative dielectrophoresis can be more easily removed from the electrode structure (e.g. by fluid flow or gravitational forces) than those trapped under positive dielectrophoresis. This was verified for mixtures of bacteria and blood cells, viable and non-viable yeast cells.

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Metadata

Authors
Wang, X. B., Huang, Y., Burt, J. P. H., Markx, G. H., & Pethig, R.
Venue
Journal of Physics D: Applied Physics
Published
1993-08-14
Volume
26
Issue
8
Pages
1278-1285
ISSN
  • 0022-3727 (ISSN-L)
  • 1361-6463
OpenAlex
W2006044860
Cited by
223
References
0
FWCI
10.52
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Selective dielectrophoretic confinement of bioparticles in potential energy wells. https://app.cochat.ai/papers/10.1088/0022-3727/26/8/019
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Wang, X. B., Huang, Y., Burt, J. P. H., Markx, G. H., & Pethig, R. (1993). Selective dielectrophoretic confinement of bioparticles in potential energy wells. Journal of Physics D: Applied Physics. https://doi.org/10.1088/0022-3727/26/8/019

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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.