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Impedance microbiology-on-a-chip: microfluidic bioprocessor for rapid detection of bacterial metabolism

Gomez-Sjoberg, R., Morisette, D.T., Bashir, R.·2005·Journal of Microelectromechanical Systems

Abstract

Detection of a few live bacterial cells in many industrial or clinical samples is a very important technological problem. We have developed a microscale technique for concentrating bacterial cells from a dilute sample, by factors on the order of 10/sup 4/ to 10/sup 5/, and detecting their metabolic activity by purely electrical means. The technique was implemented on a silicon-based microfluidic chip where the cells are concentrated and incubated in a chamber with a volume of 400 pL. Concentration and capture are obtained by the use of dielectrophoresis on the bacterial cells, and metabolism detection is achieved by means of impedance measurements of the medium in which the bacteria are incubated. Performing impedance-based detection at the microscale results in drastically reduced detection times for dilute bacterial samples, thanks to the ability to efficiently concentrate and capture the cells in an extremely small volume. Such concentration eliminates the need to amplify the bacterial population by long culture steps. This detection technique can be used for a wide variety of applications.

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Metadata

Authors
Gomez-Sjoberg, R., Morisette, D. T., & Bashir, R.
Venue
Journal of Microelectromechanical Systems
Published
2005-08
Volume
14
Issue
4
Pages
829-838
ISSN
1057-7157 (ISSN-L)
OpenAlex
W2096578849
Cited by
174
References
0
FWCI
9.40
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Impedance microbiology-on-a-chip: microfluidic bioprocessor for rapid detection of bacterial metabolism. https://app.cochat.ai/papers/10.1109/jmems.2005.845444
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Gomez-Sjoberg, R., Morisette, D. T., & Bashir, R. (2005). Impedance microbiology-on-a-chip: microfluidic bioprocessor for rapid detection of bacterial metabolism. Journal of Microelectromechanical Systems. https://doi.org/10.1109/jmems.2005.845444

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.