On-Chip Impedance for Quantifying Parasitic Voltages During AC Electrokinetic Trapping

Farmehini, Vahid; Varhue, Walter; Salahi, Armita; Hyler, Alexandra R.; Čemažar, Jaka; Davalos, Rafael V.

doi:10.1109/tbme.2019.2942572

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…[102] studied a multilayer cDEP device to detect several factors affecting device performance (e.g., resistance, resonance, and inductance). Later in 2020, another study [103] aimed at determining the amount of parasitic voltage drops in cDEP. They were able to do this by modeling the device with an equivalent RC circuit, determining the fraction of the total voltage that a cDEP device uses to achieve electrokinetic manipulation.…”

Section: Voltage Reduction Strategies In Insulator‐based Devicesmentioning

confidence: 99%

Toward low‐voltage dielectrophoresis‐based microfluidic systems: A review

2020

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Dielectrophoretically driven microfluidic devices have demonstrated great applicability in biomedical engineering, diagnostic medicine, and biological research. One of the potential fields of application for this technology is in point‐of‐care (POC) devices, ideally allowing for portable, fully integrated, easy to use, low‐cost diagnostic platforms. Two main approaches exist to induce dielectrophoresis (DEP) on suspended particles, that is, electrode‐based DEP and insulator‐based DEP, each featuring different advantages and disadvantages. However, a shared concern lies in the input voltage used to generate the electric field necessary for DEP to take place. Therefore, input voltage can determine portability of a microfluidic device. This review outlines the recent advances in reducing stimulation voltage requirements in DEP‐driven microfluidics.

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Section: Voltage Reduction Strategies In Insulator‐based Devicesmentioning

confidence: 99%

Toward low‐voltage dielectrophoresis‐based microfluidic systems: A review

2020

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“…(C) Negative dielectrophoresis (nDEP) and positive DEP (pDEP) of the collected red blood cells (RBCs) from the buffer swap stage flow rate of the collected sample are validated to support in-line negative dielectrophoresis (nDEP) at 30 kHz and positive dielectrophoresis (pDEP) at 1 MHz, by using a set of sequential field nonuniformities in the downstream microchannel for flowthrough DEP [37]. Based on this platform, we envision the ability for on-chip automation [38] and integration of sample preparation in-line with DEP sorting to reduce user intervention and stress on cells, as well as for monitoring of cell media properties, as well as their numbers, velocity, viability, and position in the microchannel, as may be required for tailoring DEP separations for different degrees of cellular heterogeneity within the biological sample of interest.…”

Section: Introductionmentioning

confidence: 99%

On‐chip microfluidic buffer swap of biological samples in‐line with downstream dielectrophoresis

et al. 2022

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Microfluidic cell enrichment by dielectrophoresis, based on biophysical and electrophysiology phenotypes, requires that cells be resuspended from their physiological media into a lower conductivity buffer for enhancing force fields and enabling the dielectric contrast needed for separation. To ensure that sensitive cells are not subject to centrifugation for resuspension and spend minimal time outside of their culture media, we present an on‐chip microfluidic strategy for swapping cells into media tailored for dielectrophoresis. This strategy transfers cells from physiological media into a 100‐fold lower conductivity media by using tangential flows of low media conductivity at 200‐fold higher flow rate versus sample flow to promote ion diffusion over the length of a straight channel architecture that maintains laminarity of the flow‐focused sample and minimizes cell dispersion across streamlines. Serpentine channels are used downstream from the flow‐focusing region to modulate hydrodynamic resistance of the central sample outlet versus flanking outlets that remove excess buffer, so that cell streamlines are collected in the exchanged buffer with minimal dilution in cell numbers and at flow rates that support dielectrophoresis. We envision integration of this on‐chip sample preparation platform prior to or post‐dielectrophoresis, in‐line with on‐chip monitoring of the outlet sample for metrics of media conductivity, cell velocity, cell viability, cell position, and collected cell numbers, so that the cell flow rate and streamlines can be tailored for enabling dielectrophoretic separations from heterogeneous samples.

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“…For microfluidic applications, such as active particle sorting and selective enrichment [19,20], there is a need for high-throughput real-time signal processing methodologies. Similarly, for distinction of particular cell phenotypes from complex samples with similar-sized cells [21], there is a need for routines that can be trained using model cell types to enable impedance-based signal recognition.…”

Section: Introductionmentioning

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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On-Chip Impedance for Quantifying Parasitic Voltages During AC Electrokinetic Trapping

Cited by 9 publications

References 32 publications

Toward low‐voltage dielectrophoresis‐based microfluidic systems: A review

Toward low‐voltage dielectrophoresis‐based microfluidic systems: A review

On‐chip microfluidic buffer swap of biological samples in‐line with downstream dielectrophoresis

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

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