Numerical Investigation of a Novel Wiring Scheme Enabling Simple and Accurate Impedance Cytometry

Caselli, Federica; Reale, Riccardo; Nodargi, Nicola A.; Bisegna, Paolo

doi:10.3390/mi8090283

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…Experimental details, such as distribution of particle sizes, dielectric properties, inter-arrival times, velocities, and trajectories can be accounted, along with an allowance for the measurement noise. Hence, a virtual laboratory can be developed using finite-element-based approaches, providing synthetic data streams that closely mimic experimental conditions 148,149 . As such, finite element simulations are widely used for in silico device design, testing and optimization, thus allowing a reduction of time and resources needed to move from novel ideas to prototypes 62,77,86,93,[97][98][99]108,150 .…”

Section: Please Do Not Adjust Marginsmentioning

confidence: 99%

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

et al. 2021

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Section: Please Do Not Adjust Marginsmentioning

confidence: 99%

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

et al. 2021

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“…Unless methods for particle focusing are used, the accuracy of estimated particle properties (e.g., particle size) is reduced due to position blurring. To remove position blurring, we have recently introduced a novel wiring scheme applicable to standard chip layouts involving two electrode pairs (either coplanar or facing) [37,38]. That wiring scheme is used in this work for both the H-zone and the F-zone.…”

Section: Microfluidic Impedance Chip Designmentioning

confidence: 99%

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

et al. 2020

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Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and crosssectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting.

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A simple electrical approach to monitor dielectrophoretic focusing of particles flowing in a microchannel

et al. 2019

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This paper reports an impedance‐based system for the quantitative assessment of dielectrophoretic (DEP) focusing of single particles flowing in a microchannel. Particle lateral positions are detected in two electrical sensing zones placed before and after a DEP‐focusing region, respectively. In each sensing zone, particle lateral positions are estimated using the unbalance between the opposite pulses of a differential current signal obtained with a straightforward coplanar electrode configuration. The system is used to monitor the focusing of polystyrene beads of 7 or 10 μm diameter, under various conditions of DEP field intensities and flow rates that produce different degrees of focusing. This electrical approach represents a simple and valuable alternative to optical methods for monitoring of particle focusing systems.

show abstract

Numerical Investigation of a Novel Wiring Scheme Enabling Simple and Accurate Impedance Cytometry

Cited by 12 publications

References 28 publications

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry

A simple electrical approach to monitor dielectrophoretic focusing of particles flowing in a microchannel

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