Abstract

The growing threat of antimicrobial resistance is among the largest concerns in the world today. One method under development to combat this issue is the encapsulation of microbes in microfluidic droplets for single-cell testing. This method may be able to circumvent the need for a traditional positive cell culture which consumes the majority of the testing time using current diagnostic methods. This dissertation presents a method by which to deterministically encapsulate microbes using an artificial intelligence object detection algorithm and a Droplet-On-Demand microfluidic device. To accomplish this, the Droplet-On-Demand microfluidic device was first developed using a unique 3D-printing manufacturing method. An annular Channel-in-Channel droplet generator was developed which produced droplets within the hydrophobic 3D-printed polymeric microfluidic device. Supporting microfluidic unit operations were also developed including pumps, a 3-way flow-thru valve, and a detection window used for visualizing microfluidic particles. Control software was developed using python which controlled pneumatically-actuated membranes within the microfluidic device, the imaging system, and the object detection algorithm. 20-μm and 2-μm test particles were used as non-biological test particles while red blood cells and fluorescent E.coli baceria were used as biological test particles. All test particles were identified and encapsulated and show the flexibility of the system overall and the ability to identify a variety of particles of interest in microfluidic systems. Growth tests were conducted using E.coli bacteria encapsulated within microfluidic droplets with a fluorescent metabolic indicator. The fluorescence of droplets containing actively growing encapsulated bacteria was quantified using a unique first-principles model paired with an image processing protocol to provide relative concentration data to quantify the growth of the E.coli over time. These growth results indicated that bacterial growth in droplets could be detected and quickly quantified in 4 hours and thus provide practical results to clinicians on the susceptibility of bacteria to an antibiotic. This Droplet-On-Demand technology has the capability of providing clinically applicable data from the most basic and fundamental biological source, an individual cell; and that can be done with low concentrations and on any cell that can be visually identified.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Chemical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2022-12-08

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13098

Keywords

microfluidics, 3D print, droplet, computer vision, antimicrobial susceptibility assay

Language

english

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