A Magnetite Composite of Molecularly Imprinted Polymer and Reduced Graphene Oxide for Sensitive and Selective Electrochemical Detection of Catechol in Water and Milk Samples: An Artificial Neural Network (ANN) Application

Abstract

In the present study, a stable and more selective electrochemical sensor for catechol (CC) detection at magnetic molecularly imprinted polymer modified with green reduced graphene oxide modified glassy carbon electrode (MIP/rGO@Fe3O4/GCE). Two steps have been applied to achieve the imprinting process: (1) adsorption of CC on the surface of the polypyrrole (Ppyr) during the polymerization of pyrrole and (2) the green extraction of the template (CC) from the mass produced. Hence, the present paper doesn’t present the first use of MIP technology for CC identification but, it presents a new extraction process. The MIP/rGO@Fe3O4/GCE was characterized by voltammetry techniques and exhibited a wide linear range from1 50 μM of CC while the detection limits were estimated to be around 4.18 nM CC and limit of quantification in the range of 12.69 nM CC. Furthermore, the prepared MIP-based sensor provided outstanding electroanalytical performances including high selectivity, stability, repeatability, and reproducibility. For the accurate estimation of CC concentrations, an artificial neural network (ANN) was developed based on the findings of the study. The MIP/rGO@Fe3O4/GCE exhibits excellent stability with a very important selectivity and sensitivity. The analytical testing of the modified electrode has been analyzed in water and commercial milk samples and provided adequate recoveries.