Artificial Neural Network Optimization of a Carbon Paste Electrode for the Detection of Zinc Ions

The present work focuses on the fabrication of a potentiometric sensor for the determination of zinc ions using 3,7,12,17-Tetramethyl-8,13-divinyl-2,18-porphinedipropionic acid disodium salt (protoporphyrin IX disodium) as the ionophore. The electrode is fabricated by various composition of protoporphyrin IX disodium, graphite, multiwalled carbon nanotube, paraffin oil, and sodium tetraphenyl borate. These factors are varied at 5 levels. The composition of the electrode is optimized using artificial neural network. The optimization of electrode composition was carried out using genetic algorithm, rotation inherit optimization and particle swarm optimization techniques. The genetic algorithm (GA) optimized electrode was prepared with the composition ionophore (9.71 mg), paraffin oil (drop) (7.17), NaTPB (5.31 mg), graphite (244.82 mg) and MWCNT (21.82 mg) showed better response with a a Nernstian slope of 29.69 mV/decade. It also showed a linear response in the concentration range of 1.0×10 M to 3.09×10 M and a detection limit of 1.9×10 M. The electrode showed a good selectivity for Zn relative to many studied cations. The GA optimized electrodes showed a stable response in the pH range of 2.7 to 7.0w with a response time less than 20 s for all the tested concentration ranges. Analytical application of the electrode was demonstrated by using the prepared electrode as an indicator electrode for the potentiometric titration of Zn against EDTA.