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  4. Development of a nanofluidic preconcentrator with precise sample positioning and multi-channel preconcentration
 
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Development of a nanofluidic preconcentrator with precise sample positioning and multi-channel preconcentration

Journal
Microfluidics and Nanofluidics
Journal Volume
14
Journal Issue
3
Pages
645-655
Date Issued
2013-03
Author(s)
Kung-Bin Sung
Ke-Pan Liao
Yen-Lin Liu
Wei-Cheng Tian
KUNG-BIN SUNG  
WEI-CHENG TIAN 
DOI
10.1007/s10404-012-1084-6
URI
http://scholars.lib.ntu.edu.tw/handle/123456789/381778
Abstract
A nanofluidic preconcentrator with the capability of rapidly preconcentrating and precisely positioning protein bands in multiple microchannels has been developed for highly sensitive detection of biomolecules. A novel electrical resistive network model is developed to guide the design of the nanofluidic preconcentrator which consists of a PDMS slab bonded with a glass slide. In the prototype design, two microchannels (23 mm long, 25-50 μm wide, and 5-15 μm deep), one preconcentration microchannel and one ground microchannel are connected in the middle via 16 nanochannels (25-50 μm long, 25 μm wide, and 50-80 nm deep). With two sets of optimal voltage settings applied on the opposite ends of the nanofluidic chip, the ion depletion region and electrokinetic trapping were generated to carry out the preconcentration. With the optimal voltage settings (30-30 V) predicted by the model, the ionic current of the nanochannel in our optimized preconcentrator was adjusted to be greater than the threshold value (3.9 nA) needed for the occurrence of the preconcentration, and a preconcentration factor >105 was achieved in 5 min. The sample positioning capability of the preconcentrator was demonstrated by adjusting the applied voltages and moving the preconcentrated protein bands to multiple sites by a distance from several micrometers to several millimeters in the preconcentration channel. The multi-channel preconcentration capability was also demonstrated by preconcentrating two protein bands in two separate microchannels. In this work, the resistive network model was developed and validated to optimize nanofluidic preconcentrators for rapid, high throughput and highly sensitive sensing of low abundance analytes. © 2012 Springer-Verlag Berlin Heidelberg.
SDGs

[SDGs]SDG7

Type
journal article

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