NDLI: Parameters affecting carbon nanofiber electrodes for measurement of cathodic current in electrochemical sensors: an investigation using artificial neural network

Content Provider	Royal Society of Chemistry (RSC)
Author	Faridi-Majidi, Reza Saber, Reza Faridbod, Farnoush Adabi, Mahdi Naghibzadeh, Majid
Copyright Year	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.
Starting Page	81243
Ending Page	81252
Page Count	10
File Format	HTM / HTML PDF
ISSN	20462069
Volume Number	5
Issue Number	99
Journal	RSC Advances
DOI	10.1039/c5ra15541j
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	PBS ANN Electrode Linear regression Buffer solution Carbon nanofiber Cyclic voltammetry Phosphate Partial current CNF Artificial neural network Electrophoretic deposition
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering

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