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Title: Parameters affecting Carbon Nanofibers Electrode for Measurement of Cathodic Current in Electrochemical Sensors: An Investigation using Artificial Neural Network
Journal: RSC Advances
Author: 1. Mahdi Adabi, 1,2. Reza Saber, 3. Majid Naghibzadeh, 4. Farnoush Faridbod, 1,2. Reza Faridi-Majidi
Year: 2015
Address: 1. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran 2. Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran 3. Department of Nanotechnology, Research and Clinical Center for Infertility, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 4. Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran
Abstract: The aim of this work was to investigate the effective parameters for predicting of the cathodic current in the polyacrylonitrile-based carbon nanofibers (CNFs) electrode using artificial neural network (ANN) method. The various factors including CNFs diameter, CNFs layer thickness, electrodeposition time of Pt on CNFs electrode, and solution pH of a phosphate buffer solution (PBS) containing K3Fe (CN)6 was designed to investigate the cathodic current of CNFs 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 the CNFs diameter, CNFs layer thickness, electrodeposition time of Pt on CNFs electrode and solution pH.
Keywords: Carbon nanofiber, electrode, polyacrylonitrile, electrochemical sensor, artificial neural network
Application: Sensor
Product Model 1: Electroris
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