Electrochemical impedance biosensor array based on DNAzyme-functionalized single-walled carbon nanotubes using Gaussian process regression for Cu(II) and Hg(II) determination

Microchimica Acta(2020)

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摘要
RNA-cleaving DNAzyme is a very useful biomaterial for metal ions determination. However, parts of DNAzymes can be cleaved by several metal ions, which makes it difficult to distinguish the concentrations of different metal ions. A method was applied to determine the Cu(II) concentration by using electrochemical biosensors combined with a mathematical model. An electrochemical biosensor was fabricated using single carbon nanotubes/field-effect transistor (SWNTs/FET) functionalized with a DNAzyme named PSCu10 and its complementary DNA embedded phosphorothioate RNA (CS-DNA). The CS-DNA with amino groups at the 5′ end was immobilized on the SWNTs’ surface via the peptide bond and then combined with PSCu10 by identifying bases complementary pairing (Cuzyme/SWNTs/FET). The CS-DNA can be cleaved when Cu(II) bonded with the PSCu10 so that the structural change of Cuzyme improves the electrical conductivity of Cuzyme/SWNTs/FET. But CS-DNA also can be cut-off by the Hg(II) directly, which might interfere with the detection of the Cu(II) concentration using Cuzyme/SWNTs/FET. To solve this problem, Hgzyme/SWNTs/FET was employed to monitor the Hg(II) concentration at the same time, thus serving to determine the Cu(II) content through the Gaussian process regression. The biosensor array can determine the Cu(II) concentration varying from 0.01 to 10,000 nM when the Hg(II) concentration was ranging from 5 to 10,000 nM, and the limits of detection for Cu(II) and Hg(II) were 6.7 pM and 3.43 nM, respectively. Graphical abstract A biosensor array (Cuzyme/SWNTs/FET and Hgzyme/SWNTs/FET) was developed, and the detecting data were processed using Gaussian process regression. It allows determination of Cu(II) and Hg(II) concentrations with high accuracy.
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关键词
Impedance biosensor,Heavy metal,Field-effect transistor,Nanomaterial,Mathematical model
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