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Reference type: Journal
Authors: Nezhadali A, Mojarrab M
Article Title: Fabrication of an electrochemical molecularly imprinted polymer triamterene sensor based on multivariate optimization using multi-walled carbon nanotubes.
Publication date: 2015
Journal: Journal of Electroanalytical Chemistry
Volume: 744
Page numbers: 85-94.
DOI: 10.1016/j.jelechem.2015.03.010
Alternative URL: http://www.sciencedirect.com/science/article/pii/S1572665715001216

Abstract: A novel electrochemical sensor using molecularly imprinted polymer (MIP) technique for the detection of triamterene (Tr) was developed. A computational approach was used for screening functional monomers and polymerization solvent in the rational design of MIPs. Based on computational results, the binding capacity showed that the highest binding specificity toward Tr could achieved when using pyrrole (PY) as the monomer in dimethyl sulfoxide (DMSO) solution to form a polymer matrix. The sensitive MIP sensor for Tr was electrochemically synthesized onto multi-walled carbon nanotubes immobilized pencil graphite electrode (MWCNTs/PGE) surface. The multivariate methods were used to optimize the voltammetric response of modified electrode for determination of Tr. A Plackett-Burman design (PBD) was chosen as a screening method to estimate the relative influence of the factors that could have an influence on the analytical response (current). The significant variables were optimized using central composite design (CCD). Under the optimized conditions, the calibration curve demonstrated linearity over a concentration range of 0.08-265 μM of Tr with R2 = 0.9909. The detection limit of Tr was obtained 3.35 nM (3sb, n = 7). The sensor showed a good reproducibility and regeneration capacity. The relative standard deviation (RSD%) of 3.99% was obtained to evaluate of electrode-to electrode reproducibility. This sensor was successfully applied to the quantification of Tr in some serum and pharmaceutical samples
Template and target information: triamterene, Tr
Author keywords: molecularly imprinted polymer, Multivariate optimization, Triamterene, computational design, multi-walled carbon nanotube, Electrochemical sensor

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