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    Electrolyte screening is well known for its detrimental impact on the sensitivity of liquid-gated field-effect transistor (FET) molecular sensors and is mostly described by the linearized Debye-Hückel model. However, charged and pH-sensitive FET sensing surfaces can limit the FET molecular sensitivity beyond the Debye-Hückel screening formalism. Pre-existing surface charges can lead to the breakdown of Debye-Hückel screening and induce enhanced nonlinear Poisson-Boltzmann screening. Moreover, the charging of the pH-sensitive surface groups interferes with biomolecule sensing resulting in a pH interference mechanism. With analytical equations and TCAD simulations, we highlight that the Debye-Hückel approximation can underestimate screening and overestimate FET molecular sensitivity by more than an order of magnitude. Screening strengthens significantly beyond Debye-Hückel in the proximity of even moderately charged surfaces and biomolecule charge densities (≥1 × 1012 q/cm2). We experimentally show the strong impact of both nonlinear screening and the pH interference effect on charge-based biomolecular sensing using a model system based on the covalent binding of single-stranded DNA on silicon FET sensors. The DNA signal increases from 24 mV at pH 7 to 96 mV at pH 3 in 1.5 mM PBS for a DNA density of 7 × 1012 DNA/cm2. Our model quantitatively explains the signal's pH dependence with roughly equal nonlinear screening and pH interference contributions. This work shows the importance of reducing the net charge and the pH sensitivity of the sensing surface to improve molecular sensing. Therefore, tailoring the gate dielectric and functional layer of FET sensors is a promising route to strong silicon FET molecular sensitivity boosts.

    Citation

    Sybren Santermans, Franz Schanovsky, Mihir Gupta, Geert Hellings, Marc Heyns, Willem Van Roy, Koen Martens. The Significance of Nonlinear Screening and the pH Interference Mechanism in Field-Effect Transistor Molecular Sensors. ACS sensors. 2021 Mar 26;6(3):1049-1056

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    PMID: 33496586

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