Optimization of stripping voltammetric sensor by mixture design-artificial neural network-genetic algorithm for determination of trace copper(II) based on iodoquinol-carbon nanotube modified carbon paste electrode

• Multivariate optimization of electrode composition and pH of measurements.
• Application of ANN-GA for optimization of sensor response.
• Proposing an easy to prepare and cheap sensor to determine trace copper(II).
• Selective sensor to determine copper(II) in presence of some heavy metals.
• Solvent free method of trace copper(II) determination in some real samples.

In this work, differential pulse cathodic stripping voltammetry method has been developed for determination of Cu (II) ions in aqueous solutions using a carbon paste electrode (CPE) modified with iodoquinol and multi-walled carbon nanotubes. The factors affecting the performance of the modified carbon paste electrode (MCPE) including the electrode composition and pH were optimized. The optimization process was performed using experimental design, artificial neural network (ANN) and genetic algorithm (GA). At first, experiments were performed using mixture design with pH factor as the process variable. The data from mixture design including experimental conditions and results were used to train the ANN using Bayesian regularization. The ANN was trained over 211 iterations with mean square error (MSE) of 1.60 and regression ocoefficient of r = 0.9328. The predicted model obtained from the trained ANN was introduced to GA as the fitness function to be optimized. GA optimized the fitness function using the defined constraints. Under optimal conditions a linear calibration graph in the range 0.01–5 μM was obtained. The square of regression coefficient was obtained 0.9880. The limit of detection (3S/N) was obtained 0.005 μM. The real samples analysis was performed to determine trace copper (II) in some oil and water specimens. The repeatability and reproducibility of method (n = 3) were obtained 3.31 and 4.12%, respectively. The shelf life of the sensor was obtained at least 4 months.

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