Voltammetric sensor array based on differently doped polypyrrole molecularly imprinted polymers for the simultaneous detection of acetaminophen, uric acid and ascorbic acid

Abstract

Molecularly imprinted polymers (MIPs) are synthetic receptors with complimentary cavities towards a chosen template molecule (the target analyte), able to rebind it with high affinity and specificity. Those interactions are similar to the ones between the antibodies and antigens, but with superior chemical, mechanical, thermal and pH stability, and reusability. Thus, with the goal of obtaining of low-cost artificial receptors with high selectivity towards a desired analyte, highly suitable for applications in diverse many fields, such as the environmental, medical or agro-alimentary. In this regard, MIPs have become a significant research hotspot in the development of electrochemical sensors given their ability to selectively rebind to the target analytes with high specificity even in the presence of complex matrix; thus simplifying the analysis process and improving chemosensors performance. In this work, MIP films are in-situ electro-synthesized from a monomer (pyrrole) solution, in the presence of the template molecule and different doping anions as a facile approach for the tuneability of the MIP morphology. A systematic evaluation on the effect of a series of anions as counter ion dopant integrated into the polypyrrole (PPy) backbone was carried out, including perchlorate (ClO4-), p-toluene sulfonate (pTS-), dodecyl sulfonate (DS-) and dodecyl benzene sulfonate (DBS-). The target compounds being evaluated were acetaminophen (AP), uric acid (UA) and ascorbic acid (AA), and the performance of the resulting MIPs modified electrodes was evaluated by means of cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Finally, combination of the different MIP modified electrodes as well as the NIP (non-imprinted polymer) into a sensor array will be evaluated to carry out the analysis of mixtures of the above-mentioned compounds, with the aid of chemometric methods such as principal component analysis (PCA) and artificial neural networks (ANNs).