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Quantitative modeling of dielectrophoretic traps

Authors: Adam, Rosenthal; Brian M, Taff; Joel, Voldman;

Quantitative modeling of dielectrophoretic traps

Abstract

We present quantitative modeling software for simulating multiple forces acting on a single particle in a microsystem. In this paper, we focus on dielectrophoretic (DEP) trapping of single cells against fluid flow. The software effectively models the trapping behavior for a range of particles including beads, mammalian cells, viruses, and bacteria. In addition, the software can be used to reveal useful information about the DEP traps - such as multipolar DEP force effects, trap size-selectivity, and effects from varying the flow chamber height. Our modeling software thus serves as a predictive tool, enabling the design of novel DEP traps with superior performance over existing trap geometries. In addition, the software can evaluate a range of trap dimensions to determine the effects on trapping behavior, thus optimizing the trap geometry before it is even fabricated. The software is freely available to the scientific community at: .

Related Organizations
Keywords

Electrophoresis, Computer Simulation, Models, Theoretical, Software

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Average
Top 10%
Top 10%
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