Date of Award

Fall 2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biomedical Engineering

First Advisor

Niel D. Crews

Abstract

Recent infectious disease outbreaks, such as Ebola in 2013, highlight the need for fast and accurate diagnostic tools to combat the global spread of the disease. Detection and identification of the disease-causing viruses and bacteria at the genetic level is required for accurate diagnosis of the disease. Nucleic acid analysis systems have shown promise in identifying diseases such as HIV, anthrax, and Ebola in the past. Conventional nucleic acid analysis systems are still time consuming, and are not suitable for point-ofcare applications. Miniaturized nucleic acid systems has shown great promise for rapid analysis, but they have not been commercialized due to several factors such as footprint, complexity, portability, and power consumption.

This dissertation presents the development of technologies and methods for a labon-a-chip nucleic acid analysis towards point-of-care applications. An oscillatory-flow PCR methodology in a thermal gradient is developed which provides real-time analysis of nucleic-acid samples. Oscillating flow PCR was performed in the microfluidic device under thermal gradient in 40 minutes. Reverse transcription PCR (RT-PCR) was achieved in the system without an additional heating element for incubation to perform reverse transcription step. A novel method is developed for the simultaneous pattering and bonding of all-glass microfluidic devices in a microwave oven. Glass microfluidic devices were fabricated in less than 4 minutes. Towards an integrated system for the detection of amplified products, a thermal sensing method is studied for the optimization of the sensor output. Calorimetric sensing method is characterized to identify design considerations and optimal parameters such as placement of the sensor, steady state response, and flow velocity for improved performance. An understanding of these developed technologies and methods will facilitate the development of lab-on-a-chip systems for point-of-care analysis.

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