Journal
ANALYTICA CHIMICA ACTA
Volume 1095, Issue -, Pages 82-92Publisher
ELSEVIER
DOI: 10.1016/j.aca.2019.10.027
Keywords
Molecularly imprinted polymer; Electrochemical impedance sensor; Polydopamine; DDT
Categories
Funding
- National Key Research and Development Program of China [2016YFF0203703]
- National Natural Science Foundation of China [21505018]
- Fundamental Research Funds for the Central Universities [2242017k30001]
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Dichlorodiphenyltrichloroethane (DDT) is a kind of broad-spectrum insecticides, which is potentially toxic and persistently threatens the safety of environment and food, due to their stability in nature and difficulty to degrade. For the first time, a novel impedance chemical sensor based on magnetic Fe3O4 and polydopamine molecularly imprinted polymer magnetic nanoparticles (PDA@Fe3O4-MIP MNPs) was designed. Bisphenol A (BPA) and dopamine were used as virtual template molecules and functional monomer for MIP synthesis, respectively. Recognition cavities formed in PDA layers could specifically recognize and effectively adsorb DDT molecules, with the help of virtual templates that had similar molecular structure to DDTs. The as-prepared PDA@Fe3O4-MIP MNPs could be used for specific adsorption and efficient extraction of target molecules 4,4'-DDT from food samples. The electrochemical impedance of the PDA@Fe3O4-MIP MNPs increased sensitively with the adsorption of 4,4'-DDT, the correlationship between of the electrochemical impedance response and the concentration of 4,4'-DDT were applied in the construction of electrochemical impedance sensors for the determination of 4,4'-DDT. The sensor showed a good linear relationship between the charge transfer resistance (R d ) and the 4,4'-DDT concentration over a range from 1 x 10(-11) to 1 x 10(-3) mol L-1 with a detection limit of 6 x 10(-12) mol L-1. The sensor also exhibited excellent sensitivity and selectivity as well as high stability for the detection of pesticide residues and other environmentally harmful chemicals in various food samples. (C) 2019 Elsevier B.V. All rights reserved.
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