Degree Type

Dissertation

Date of Award

2017

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

Major

Electrical Engineering

First Advisor

Meng Lu

Abstract

The early diagnosis of some chronic and severe diseases such as cancer, tuberculosis, etc. has been a long-sought goal of the medicine community. Traditional diagnostic tools such as X-ray and fecal blood tests cannot detect the disease before the focus or tumor have grown to an appreciable size or before the number of pathogens or tumor cells has reached a considerable amount in body fluids. These drawbacks could significantly delay the diagnosis. To detect and diagnose such diseases at an early stage, people have sought to detect the biomarkers related to certain physical conditions so that the anomalies caused by the diseases can be detected before a significant tumor has developed or the onset of symptoms. Driven by the needs to detect and quantify biomarkers, immunoassays have been developed. Two representative formats of immunoassays are enzyme-linked immunosorbent assay and lateral flow assay. They have been widely used for medical and research purposes, yet they still have drawbacks such as costly instruments and lack of sensitivity. To improve their performance, I have developed photoacoustic-based detection schemes that can be easily integrated with commercial immunoassay formats and can increase the sensitivity as well as lower the costs. For both assay formats, limit of detection has been lowered by two orders of magnitude with low-cost and portable instruments. As a follow up of the photoacoustic detection schemes, a technique based on photothermal lens is also developed. In this work, one-dimensional photonic crystal substrates have also been exploited to enhance the photoacoustic and photothermal signals. Due to the guided-mode resonance, the photonic crystal substrate can enhance the photoacoustic or photothermal signals by 10 to 40 times, making it a promising tool for biomarker detection.

Copyright Owner

Yunfei Zhao

Language

en

File Format

application/pdf

File Size

107 pages

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