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Title: Effective Parameters on Conductivity of Mineralized Carbon Nanofibers: An Investigation Using Artificial Neural Networks
Journal: RSC Advances
Author: 1. Hadi Samadian, Mahdi Adabi, Reza Faridi-Majidi, 2. Seyed Salman Zakariaee, 3,4. Hamid Mobasheri, 5. Mahmoud Azami
Year: 2016
Address: 1. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran 2. Department of Medical Physics, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran 3. Laboratory of Membrane Biophysics and Macromolecules, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran 4. Biomaterials Research Center (BRC), University of Tehran, Tehran, Iran 5. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract: The aim of this study was to predict the effects of different parameters on the conductivity of mineralized PAN-based carbon nanofibers by artificial neural network (ANN) method. The conductivity of CNFs was investigated as a function of various parameters including; simulated body fluid (SBF) concentration, immersion time and CNFs diameter. In order to conduct ANN modeling, the considered parameters and experimental output were categorized into i) training, ii) validating and iii) testing datasets which were subsequently analyzed using three different training algorithms including, scaled conjugate gradient, Bayesian regularization, and Levenberg–Marquardt back-propagation. The comparison study between three artificial neural network models indicates that all back-propagation methods could be employed to estimate the cathodic current accurately. The results of cyclic voltammetry demonstrated that the cathodic current increased as a function of decreasing simulated body fluid concentration, immersion time and carbon nanofiber diameter. The Pearson correlation coefficients were significant at less than 0.01 % level for all prediction models. Among the studied algorithms, the scaled conjugate gradient back-propagation method produced the highest R-value at 0.92. Based on the promising results of current approach, the mineralized CNFs can be tailored in a way to construct electroconductive scaffolds capable to manipulate the activities of bone cells through electrical stimulation and utilized in bone tissue engineering.
Keywords: Artificial neural network, Carbon nanofibers, Simulated body fluid, Immersion time, Cathodic current
Application: Scaffold
Product Model 1: Electroris
Product Model 2:
URL: http://pubs.rsc.org/-/content/articlehtml/2016/ra/c6ra21596c#="http://pubs.rsc.org" & "/-/content/articlehtml/2016/ra/c6ra21596c"#