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Parameters Affecting Carbon Nanofiber Electrodes for Measurement of Cathodic Current in Electrochemical Sensors: An Investigation Using Artificial Neural Network Publisher



Adabi M1 ; Saber R1, 2 ; Naghibzadeh M3 ; Faridbod F4 ; Faridimajidi R1, 2
Authors
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Authors Affiliations
  1. 1. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Nanotechnology, Research and Clinical Center for Infertility, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  4. 4. Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran

Source: RSC Advances Published:2015


Abstract

The aim of this work was to investigate the effective parameters for predicting the cathodic current in a polyacrylonitrile-based carbon nanofiber (CNF) electrode using an artificial neural network (ANN) method. The various factors including CNF diameter, CNF layer thickness, electrodeposition time of Pt on the CNF electrode, and pH of a phosphate buffer solution (PBS) containing K3Fe (CN)6 were designed to investigate the cathodic current of the CNF electrode. The different samples of the electrodes were fabricated as training and testing data-sets for ANN modeling. The best network had one hidden layer with 10 nodes in the layer. The mean squared error (MSE) and linear regression (R) between the observed and predicted cathodic current were 0.0763 and 0.9563, respectively, confirming the performance of the ANN. The obtained results using cyclic voltammetry (CV) exhibited that the cathodic current improves with decreasing CNF diameter, CNF layer thickness, electrodeposition time of Pt on the CNF electrode and solution pH. © The Royal Society of Chemistry 2015.