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Journal of Chemistry
Volume 2013, Article ID 305713, 8 pages
http://dx.doi.org/10.1155/2013/305713
Research Article

Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

1Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
3Nanotechnology and Advance Materials Department, Materials & Energy Research Center, Alborz, Karaj 31787/316, Iran
4Department of Mathematics, Faculty of Science, University of Gilan, Rasht 1914, Iran

Received 14 December 2012; Revised 29 January 2013; Accepted 10 February 2013

Academic Editor: Elefterios Lidorikis

Copyright © 2013 Parvaneh Shabanzadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.