Designing a Novel Microfluidic Channel for Improved Sensing of Bio-Molecules

Abstract

Effective pathogen detection is an essential requisite for preventing and treating infectious diseases. Despite recent advances in biosensors, infectious diseases remain a major cause of illnesses and mortality worldwide. For instance, in developing countries, infectious diseases account for over half the mortality rate. There is a growing demand to integrate biosensors with microfluidics to provide miniaturised platforms with many favourable properties, such as reduced sample volume, decreased processing time, low-cost analysis, low reagent consumption, and multiple sample detection in parallel, portability and versatility in design. Translating bio-molecules and analyses into a functional device is a primary challenge in microfluidics-integrated biosensors. This PhD project aimed to extend the engineering understanding of microfluidic integrated biosensors to develop a new design to increase the translation rate of molecules onto the surface of a biosensor for point-of-care (POC) diagnostics. More specifically, this project used numerical techniques to calculate the convective-diffusive transport of bio-species inside a microfluidic channel. Results are validated against relevant experiments provided by previous literature for microfluidic channel design and fabrication. In the next stage, machine learning-aided molecular dynamics (M.D.) simulation has been applied to reduce the need for experimental input in numerical simulation. This method provides reliable and valuable insight into the biosensors reactions on the functionalised surface. The outcome of this project leads to enhanced analytical capability and nature-inspired novel microfluidic-integrated biosensors, which will widen the possibilities for applications in clinical settings, with particular emphasis on adopting these platforms for POC diagnostics in high mortality rate diseases (e.g., cancer).

Date of Award	6 Jan 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Masoud Jabbari (Co Supervisor) & Amir Keshmiri (Main Supervisor)