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    Development of a bulk acoustic resonator sensing platform for cancer biomarker detection

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    MOBLEY-DISSERTATION-2015.pdf (10.45Mb)
    Date
    2015-08-11
    Author
    Mobley, Stephen J.
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    Abstract
    Cancer is one of the leading causes of death for patients within the United States and throughout the world. When diagnosed in early growth stages, tumors (regions of uncontrolled cell division) can be more effectively treated and patients are more likely to survive. Therefore, the development of screening technology for early detection of cancer is essential to improve patient survival rates and serves as the motivation for this work. The objective of this dissertation is the design and implementation of a sensing platform for the detection of cancer biomarkers within aqueous patient samples. The system’s transducer is based on previously developed zinc oxide (ZnO) bulk acoustic wave (BAW) resonators that are capable of exciting multiple types of acoustic modes. Chapter 1 presents the motivation for this work along with a short review of gravimetric biosensors used in aqueous applications. Chapter 2 focuses on the history, theoretical derivation, and fabrication protocol for the system transducers and array configuration. In Chapter 3, the Universal Serial Bus (USB) is examined as a potential radio frequency bus for device characterization of MEMs devices. Chapter 4 presents the optimization of a module design for isolating the circuitry from the fluidics pathways for sample exposure. Combining the work of the previous chapters, Chapter 5 validates the ability of the designed system to serve as a biosensing platform. Each individual sensor is functionalized with antibodies selectively binding the desired biomarker. Lastly, Chapter 6 demonstrates a protocol for extracting features from raw sensor data to develop classification models. Thus, providing diagnostic information about the sample exposed to the device.
    URI
    http://hdl.handle.net/1853/56162
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Electrical and Computer Engineering Theses and Dissertations [3381]

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