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

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...

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Bibliographic Details
Published in:Electronics (Basel) 2022-04, Vol.11 (7), p.1045
Main Authors: Alhussan, Amel A., Gaafar, Mohamed S., Alharbi, Mafawez, Marzouk, Samir Y., Alharbi, Sayer, ElRashidy, Hussain, Mabrouk, Mai S., AlEisa, Hussah N., Samee, Nagwan Abdel
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11071045