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Artificial neural network analysis of optical measurements of glasses based on Sb2O3

O. BOŠÁK1, S. MINÁRIK1, V. LABAŠ1, Z. ANČÍKOVÁ2, P. KOŠTIAL2, O. ZIMNÝ2, M. KUBLIHA3, M. POULAIN4, M. T. SOLTANI5

Affiliation

  1. Faculty of Materials Science and Technology, Slovak University of Technology, Paulínska 16, 917 24 Trnava, Slovakia. ondrej.bosak@stuba.sk
  2. Faculty of Metallurgy and Materials Engineering, VŠB-Technical University of Ostrava, 17. listopadu 15/2172, 70833 Ostrava, Czech Republic. zora.jancikova@vsb.cz
  3. Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, 813 68 Bratislava 15, Slovakia. marian.kubliha@stuba.sk
  4. Laboratoire des Matériaux Photoniques, Centre d’ Étude des Matériaux Avancés, Université de Rennes, Campus de Beaulieu F – 35042 Rennes, France. marcel.poulain@univ_rennes1.fr
  5. Laboratoire de chimie appliqué, Departement de Chimie, Université de Biskra, BP 145, RP-Biskra 07000, Algeria

Abstract

In the paper we present application of artificial neural network (ANN) on relation between glass composition versus optical transmittance of the chosen glass systems of Sb2O3 - PbCl2 and Sb2O3 – PbO – M2O, where M was Na, K and Li, respectively. The excellent prediction ability of special ANN program developed for this study demonstrates the possibility to influence the glass composition to obtain asked optical properties. The measurements of the temperature dependencies of the direct electric conductivity show the strong influence of the concentration of the individual glass compounds of systems Sb2O3 - PbCl2 and Sb2O3 – PbO – M2O (M is Na, K, Li) on their electric and dielectric properties. Glasses own the same mechanism of the electric conductivity with activation energy, which goes to the value 3.75 eV when temperature is higher than 250 C. Similarly optical transmittance T of systems Sb2O3 - PbCl2 and Sb2O3 – PbO – M2O strongly depends on the glass composition and the amount of defects, too. The glass 70Sb2O3 – 30PbCl2 reached the highest value of T. The minimal content of defects in its volume makes these glasses very perspective for next searching. The measurements of the complex modulus M of mentioned glasses showed their high sensitivity to the changes of glass structure connected with the creation of different sort and the amount of defects. The sensibility of the used methods is comparable with the usual exploited methods (X-ray analysis, optical microscopy) and makes possible to assess partially the quantitative occurrence of defects in the glass volume. A model of neural network for prediction of the optical transmittance was created. Model enables to predict the transmittance with sufficiently small error..

Keywords

Heavy metal oxides glasses, Artificial neural networks, Transmittance, Dielectric properties.

Submitted at: Nov. 5, 2015
Accepted at: April 5, 2016

Citation

O. BOŠÁK, S. MINÁRIK, V. LABAŠ, Z. ANČÍKOVÁ, P. KOŠTIAL, O. ZIMNÝ, M. KUBLIHA, M. POULAIN, M. T. SOLTANI, Artificial neural network analysis of optical measurements of glasses based on Sb2O3, Journal of Optoelectronics and Advanced Materials Vol. 18, Iss. 3-4, pp. 240-247 (2016)