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    Additively manufactured reconfigurable microwave components based on microfluidics for wireless sensing and Internet-of-Things applications

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    SU-DISSERTATION-2018.pdf (32.64Mb)
    Date
    2018-03-29
    Author
    Su, Wenjing
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    Abstract
    This research combines additive manufacturing and microfluidics with microwave and radio frequency (RF) electronics to provide a novel low-cost flexible and reconfigurable solution for Internet-of-Things (IoT). Microfluidics, an emerging technology which allows the precise control of an extremely small amount of liquid within tiny channels, can be used in IoT applications to achieve Lab-on-Chip (LoC) functionality and an extraordinary reconfigurability. Microwave structures are very sensitive to the surrounding environment and thus excellent sensors, while passive radio frequency identification (RFIDs) provide low-cost zero-power solutions for wireless liquid sensing. This work has developed various proof-of-concept disposable wireless liquid sensors and RFID-based sensing platforms for LoC applications. To realize excellent reconfigurability low-costly and compactly, this research also has studied new reconfigurable RF integration topologies by integrating microfluidic channels and dielectric or conducting liquids. In order to significantly decrease the production time and cost, and thus to enhance the ubiquitous smart items, this research studied additive manufacturing (AM) in IoT applications. This research has been exploring the possibility of replacing the conventional electronics and microfluidics fabrication methods with cost-effective additive manufacturing methods such as inkjet-printing and 3D printing. This work presents first-of-its-kind, cost-effective, rapid, low-temperature, and environmental-friendly AM fabrication methods for various reconfigurable antennas or microwave components, wearable sensors, and sensing platforms. In summary, this research focuses on utilizing new AM fabrication techniques and novel microfluidics topologies to provide a low-cost, flexible, and scalable solution for wireless sensing and IoT applications.
    URI
    http://hdl.handle.net/1853/61134
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Electrical and Computer Engineering Theses and Dissertations [3381]

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