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Graphene field-effect transistors-based biosensors for Escherichia coli detection

Guangfu Wu, Meyya Meyyappan, King Wai Chiu Lai*

*Corresponding author for this work

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    Abstract

    Novel devices are desperately required for effective detection of bacteria because of its potential risks in the fields of food safety and public health. In this paper, we demonstrated graphene field-effect transistors (G-FETs) for Escherichia coli (E. coli) detection. Linker molecules and antibodies were employed to functionalize graphene. Raman spectroscopy and confocal microscopy were used to confirm the functionalization process of the linker molecules and antibodies. The linker molecule (1-pyrenebutanoic acid succinimidyl ester, PBASE) contains two parts: pyrene backbone and succinimidyl ester group. Pyrene attached onto the graphene surface via π-π stacking, and succinimidyl ester group covalently reacted with amino group of antibodies. Antibodies functionalized G-FETs enabled effective E. coli detection. The results showed that the Dirac point of the G-FETs shifted to right after detection of E. coli bacteria. Our G-FETs devices showed an obvious increase in the electrical current when the E. coli concentration was 5×103CFU/mL. The simple and label-free biosensor reported here possesses promising potential to serve as a platform for other bacteria, protein and small molecule detection.
    Original languageEnglish
    Title of host publication16th International Conference on Nanotechnology - IEEE NANO 2016
    PublisherIEEE
    Pages22-25
    ISBN (Print)9781509039142
    DOIs
    Publication statusPublished - 21 Nov 2016
    Event16th IEEE International Conference on Nanotechnology - IEEE NANO 2016 - Sendai, Japan
    Duration: 22 Aug 201625 Aug 2016

    Conference

    Conference16th IEEE International Conference on Nanotechnology - IEEE NANO 2016
    PlaceJapan
    CitySendai
    Period22/08/1625/08/16

    Funding

    This project is supported by the GRF grant from The Research Grant Council of the Hong Kong Special Administrative Region Government (CityU 11205514).

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