University of Illinois at Chicago
Browse
HAJIZADEGAN-DISSERTATION-2020.pdf (4.78 MB)

Self-Powered Multi-Function Harmonics-Based Wireless Sensing System Using Graphene Bioelectronics

Download (4.78 MB)
thesis
posted on 2020-05-01, 00:00 authored by Mehdi Hajizadegan
We introduce a new paradigm of low-noise and low-interface wireless sensing system, which receives a radio signal at the fundamental frequency f0 and retransmits high harmonics (e.g. second harmonic 2f0) with the conversion gain being modulated by the targeted agent. Specifically, the harmonic-transponder sensor (or harmonic sensor) is based on all-graphene radio-frequency (RF) circuits. Thanks to unique properties in graphene field-effect transistors (GFETs), such as the ambipolar carrier transport and the shiftable charge neutral point, the frequency modulation and chemical/molecular sensing functions can be combined into a single RF component (i.e., chemically-sensitive modulator). By transmitting and interrogating RF signals with orthogonal frequencies, the backscattered signal can be free from severe background clutters, jamming, multipath-scattering and background electromagnetic interfaces, regardless of the sensor’s scattering cross-section. Moreover, a GFET-based RF modulator circuit may enable dual/multi-functional sensing by employing the machine learning approach to interpret output harmonics. The proposed graphene-based harmonic sensor may be used to a variety of sensing applications, including, but not limited to, real-time monitoring of chemical and gas exposures, as well as biological agents. Further development of such technique may have an impact on wearable and implantable devices, internet of things (IoTs), industry 4.0, and smart city.

History

Advisor

Chen, Pai-Yen

Chair

Chen, Pai-Yen

Department

Electrical and Computer Engineering

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Uslenghi, Piergiorgio Erricolo, Danilo Metlushko, Vitaly Xu, Jie

Submitted date

May 2020

Thesis type

application/pdf

Language

  • en

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC