Journal Article

Prediction of the Judd–Ofelt Parameters of Dy3+-Doped Lead Borosilicate Using Artificial Neural Network

Gaafar, Mohamed S., Mabrouk, Mai S., AlEisa, Hussah N., ElRashidy, Hussain, Alharbi, Sayer, Alhussan, Amel A., Marzouk, Samir Y., Samee, Nagwan Abdel, Alharbi, Mafawez

2022

Overview

Developments in the field of glass research necessitate the mimicking of the optical properties of glass materials before melting the raw materials, as they are very expensive nowadays. An artificial neural network (ANN) was utilized during this work to train and predict the Judd–Ofelt parameters of various glasses, such as Ω2, Ω4 and Ω6, and the radiative lifetimes of many different types of rare-earth-doped glasses. The optimized ANN architecture for forecasting the Judd–Ofelt parameters were found to be very near to the experimentally measured parameters. Then, the conferred ANN model was employed to predict the Judd–Ofelt parameters of some newly prepared borosilicate glasses. Therein, a new glass system of 0.25 PbO–0.2 SiO2–(0.55 − x) B2O3–x Dy2O3, was prepared in order to employ the melt-quenching technique. The parameter results of the Judd–Ofelt theory, as well as the Ω2, Ω4 and Ω6 and radiative lifetimes showed that the supplementation of Dy2O3 switched the BO4 units to BO3 units with oxygens that were non-bridging atoms, thus weakening the glass frameworks. Therefore, it is very important to use an ANN to predict the Judd–Ofelt parameters of several rare-earth-doped glasses as luminescent materials.

• Publisher: MDPI AG
• Subject: Aluminum / Artificial intelligence / Artificial neural networks / Boron oxides / Borosilicate glass / Genetic algorithms / Heat / Lead oxides / Neural networks / Optical properties / Ordinary differential equations / Parameters / Rare earth elements / Raw materials / Regression analysis / Silicon dioxide / Silicon nitride / Thermogenesis