Dissertation Defence: Development of Microfluidic Platforms for Low-Cost and High Precision Droplet Generation and Quantification of Agglutination Assays
December 9 at 1:00 pm - 5:00 pm
Abdul Basit Zia, supervised by Dr. Ian Foulds, will defend their dissertation titled “Development of Microfluidic Platforms for Low-Cost and High Precision Droplet Generation and Quantification of Agglutination Assays” in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering.
An abstract for Abdul Basit Zia’s dissertation is included below.
Examinations are open to all members of the campus community as well as the general public. Registration is not required for in-person exams.
ABSTRACT
This dissertation explores the development and calibration of a cost-effective, automated microfluidic droplet-on-demand generation system by adapting fused deposition modelling (FDM) 3D printer to overcome the high costs and complexities traditionally associated with droplet microfluidics. The developed systems can move the inlet of the microfluidic system in cartesian coordinates and generate droplets from various analytes, termed automated dynamic inlet microfluidic systems (ADIM systems).
A 3D printer (Prusa Mini+) is adapted with novel 3D printed modules into a droplet-on-demand system that generates combinatorial droplets from a standard 96-well plate in dual-phase microfluidics. The calibration methodology developed would allow any FDM printer to create monodisperse droplets (coefficient of variance (CV%) < 5%) from well plates or vials of any geometry submerged in oil. The system maintains precision across various volumes while maintaining a C.V. range of 0.81% to 3.61%. The cost of the system developed is 70% less than commercially available droplet generation packages.
The developed ADIM system is used to generate trains of droplets of analyte and quantified across a broad concentration range (0.0128 µg/mL to 5000 µg/mL). The analysis methodology utilizes MATLAB for video frame extraction and processing, and develops multiple metrics for the droplet images, each providing a different insight. This image analysis approach successfully demonstrates the system’s ability to differentiate between agglutinated and non-agglutinated droplets across the entire range of concentrations. Moreover, the system can classify the agglutination into three regions (Band 1: 0.0128 to 0.32 µg/mL, Band 2: 1.6 to 40 µg/mL, and Band 3: 200 to 5000 µg/mL) with a 97.50% accuracy for image analysis.
Building upon our previous model, the second ADIM system simplifies calibration methodology with an inverted inlet to produce mono-disperse droplets (CV% < 2% for a train of 100 droplets). The developed system is 30% more economical than the previous iteration. Additionally, the system’s utility in quantifying agglutination assays is highlighted using image analysis, which is capable of distinguishing between agglutinated and non-agglutinated samples. Image analysis metrics used in quantification include particle size and variance-based indicator (VBI).
