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    Predictive simulations and optimization of nanowire field-effect PSA sensors including screening

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    Type
    Article
    Authors
    Baumgartner, Stefan
    Heitzinger, Clemens
    Vacic, Aleksandar
    Reed, Mark A
    KAUST Grant Number
    KUK-I1-007-43
    Date
    2013-05-03
    Online Publication Date
    2013-05-03
    Print Publication Date
    2013-06-07
    Permanent link to this record
    http://hdl.handle.net/10754/599386
    
    Metadata
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    Abstract
    We apply our self-consistent PDE model for the electrical response of field-effect sensors to the 3D simulation of nanowire PSA (prostate-specific antigen) sensors. The charge concentration in the biofunctionalized boundary layer at the semiconductor-electrolyte interface is calculated using the propka algorithm, and the screening of the biomolecules by the free ions in the liquid is modeled by a sensitivity factor. This comprehensive approach yields excellent agreement with experimental current-voltage characteristics without any fitting parameters. Having verified the numerical model in this manner, we study the sensitivity of nanowire PSA sensors by changing device parameters, making it possible to optimize the devices and revealing the attributes of the optimal field-effect sensor. © 2013 IOP Publishing Ltd.
    Citation
    Baumgartner S, Heitzinger C, Vacic A, Reed MA (2013) Predictive simulations and optimization of nanowire field-effect PSA sensors including screening. Nanotechnology 24: 225503. Available: http://dx.doi.org/10.1088/0957-4484/24/22/225503.
    Sponsors
    SB and CH acknowledge support by the FWF (Austrian Science Fund) project no. P20871-N13 and by the WWTF (Vienna Science and Technology Fund) project no. MA09-028. CH acknowledges support by award no. KUK-I1-007-43 funded by the King Abdullah University of Science and Technology (KAUST). AV and MR acknowledge support by the DTRA project no. HDTRA1-10-1-0037.
    Publisher
    IOP Publishing
    Journal
    Nanotechnology
    DOI
    10.1088/0957-4484/24/22/225503
    PubMed ID
    23644739
    ae974a485f413a2113503eed53cd6c53
    10.1088/0957-4484/24/22/225503
    Scopus Count
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