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Impedance measurement technique for high-sensitivity cell detection in microstructures with non-uniform conductivity distribution
Authors:Faenza Andrea  Bocchi Massimo  Pecorari Nicola  Franchi Eleonora  Guerrieri Roberto
Institution:ARCES-University of Bologna, Viale Pepoli 3/2, I-40123 Bologna, Italy. afaenza@arces.unibo.it
Abstract:Particle detection in microstructures is a key procedure required by modern lab-on-a-chip devices. Unfortunately, state of the art approaches to impedance measuring as applied to cell detection do not perform well in regions characterized by non-homogeneous physical parameters due, for example, to the presence of air-liquid interfaces or when the particle-electrode distance is relatively high. This paper presents a robust impedance measurement technique and a circuit for detecting cells flowing in microstructures such as microchannels and microwells. Our solution makes use of an innovative three-electrode measurement scheme with asymmetric polarization in order to increase cell detection ability in microstructures featuring large electrode distances of up to 100 μm as well as to limit signal loss due to cell position relative to the electrodes. Compared to standard techniques, numerical simulations show that, with the proposed approach, the cell detection sensitivity is increased by more than 40%. In addition, we propose a custom circuit based on division instead of difference between signals, as in standard differential circuits, so as to reduce the baseline signal drift induced by non-homogeneous conductivity. A simplified analytical model shows an increase in the signal-to-noise-ratio comprised in the range 3.9-5.9. Experimental results, carried out using an open-microwell device made with flexible printed circuit board technology, are in agreement with simulations, suggesting a six-fold increase of the signal-to-noise ratio compared to the differential measurement technique. We were thus able to successfully monitor the process of isolating K562 leukemia cells inside open-microwells determining all single-cell events with no false positive detection.
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