Literature DB >> 21035606

Counting bacteria on a microfluidic chip.

Yongxin Song1, Hongpeng Zhang, Chan Hee Chon, Shu Chen, Xinxiang Pan, Dongqing Li.   

Abstract

This paper reports a lab-on-a-chip device that counts the number of bacteria flowing through a microchannel. The bacteria number counting is realized by a microfluidic differential Resistive Pulse Sensor (RPS). By using a single microfluidic channel with two detecting arm channels placed at the two ends of the sensing section, the microfluidic differential RPS can achieve a high signal-to-noise ratio. This method is applied to detect and count bacteria in aqueous solution. The detected RPS signals amplitude for Pseudomonas aeruginosa ranges from 0.05 V to 0.17 V and the signal-to-noise ratio is 5-17. The number rate of the bacteria flowing through the sensing gate per minute is a linear function of the sample concentration. Using this experimentally obtained correlation curve, the concentration of bacteria in the sample solution can be evaluated within several minutes by measuring the number rate of the bacteria flowing through the sensing gate of this microfluidic differential RPS chip. The method described in this paper is simple and automatic, and have wide applications in determining the bacteria and cell concentrations for microbiological and other biological applications.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 21035606     DOI: 10.1016/j.aca.2010.09.035

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  3 in total

1.  Impedimetric detection of bacteria by using a microfluidic chip and silver nanoparticle based signal enhancement.

Authors:  Renjie Wang; Yi Xu; Thomas Sors; Joseph Irudayaraj; Wen Ren; Rong Wang
Journal:  Mikrochim Acta       Date:  2018-02-19       Impact factor: 5.833

2.  One-Dimensional Flow of Bacteria on an Electrode Rail by Dielectrophoresis: Toward Single-Cell-Based Analysis.

Authors:  Yukihiro Yamaguchi; Takatoki Yamamoto
Journal:  Micromachines (Basel)       Date:  2021-01-24       Impact factor: 2.891

3.  Machine learning-driven electronic identifications of single pathogenic bacteria.

Authors:  Shota Hattori; Rintaro Sekido; Iat Wai Leong; Makusu Tsutsui; Akihide Arima; Masayoshi Tanaka; Kazumichi Yokota; Takashi Washio; Tomoji Kawai; Mina Okochi
Journal:  Sci Rep       Date:  2020-09-23       Impact factor: 4.379

  3 in total

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