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A N-ary sorting system using an Integrated Droplet-Digital Microfluidics (ID2M)

Title:

A N-ary sorting system using an Integrated Droplet-Digital Microfluidics (ID2M)

Ahmadi, Fatemeh ORCID: https://orcid.org/0000-0002-0353-8856 (2018) A N-ary sorting system using an Integrated Droplet-Digital Microfluidics (ID2M). Masters thesis, Concordia University.

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Abstract

Droplet microfluidics has the ability to compartmentalize reactions in sub nano-liter (or pico) volumes that can potentially enable millions of distinct biological assays to be performed on individual cells. Typically, there are two main droplet operations that can be performed with these platforms: cell encapsulation and sorting. Droplet sorting has been a means to select a subset of reactions or activities that can be used for further manipulation and it has been used for single cell analysis and directed evolution. But one of the challenges with these techniques is that these are typically binary sorters – i.e. only relying on two sorting channels. This can limit the range of detecting rare events and to sort based on multiple conditions (i.e. more than two conditions). To alleviate this challenge, we have integrated droplet microfluidics with digital microfluidics to enable n-ary sorting techniques, which we call ‘Integrated Droplet-Digital Microfluidics’ (ID2M). Furthermore, our ‘ID2M’ microfluidic technique also allow on-demand droplet creation and droplet mixing which are two other operations that are difficult to perform in current droplet microfluidic platforms. ID2M is integrated to an automation system for on-demand manipulation of droplets and a spectrometer to detect droplet of interest. The ID2M platform has been validated as a robust on-demand screening system by sorting fluorescein droplets of different concentration with an efficiency of ~ 96 %. The device is further demonstrated for sorting tolerant wild-type and yeast mutants to ionic liquid. We propose that this system has the potential to be used for screening different types of cells and for performing directed evolution on chip.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Ahmadi, Fatemeh
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:14 August 2018
Thesis Supervisor(s):Shih, Steve
Keywords:Droplet microfluidics, Digital microfluidics, n-ary sorting, Biofuel, Directed evolution
ID Code:984623
Deposited By: Fatemeh Ahmadi
Deposited On:23 Jun 2021 15:46
Last Modified:24 Jun 2021 01:00

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