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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Continuous flow separations in microfluidic devices

Pamme, Nicole·2007·Lab on a Chip

Abstract

Biochemical sample mixtures are commonly separated in batch processes, such as filtration, centrifugation, chromatography or electrophoresis. In recent years, however, many research groups have demonstrated continuous flow separation methods in microfluidic devices. Such separation methods are characterised by continuous injection, real-time monitoring, as well as continuous collection, which makes them ideal for combination with upstream and downstream applications. Importantly, in continuous flow separation the sample components are deflected from the main direction of flow, either by means of a force field (electric, magnetic, acoustic, optical etc.), or by intelligent positioning of obstacles in combination with laminar flow profiles. Sample components susceptible to deflection can be spatially separated. A large variety of methods has been reported, some of these are miniaturised versions of larger scale methods, others are only possible in microfluidic regimes. Researchers now have a diverse toolbox to choose from and it is likely that continuous flow methods will play an important role in future point-of-care or in-the-field analysis devices.

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Metadata

Authors
Pamme, N.
Venue
Lab on a Chip
Published
2007
Volume
7
Issue
12
Pages
1644
ISSN
  • 1473-0189 (ISSN-L)
  • 1473-0197
OpenAlex
W1978546199
Cited by
770
References
2
FWCI
35.89
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Continuous flow separations in microfluidic devices. https://app.cochat.ai/papers/10.1039/b712784g
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Pamme, N. (2007). Continuous flow separations in microfluidic devices. Lab on a Chip. https://doi.org/10.1039/b712784g

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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.