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

Developments in the field of glass research necessitate mimicking optical properties materials before melting raw materials, as they are very expensive nowadays. An artificial neural network (ANN) was utilized during this work to train and predict Judd–Ofelt parameters various glasses, such ?2, ?4 ?6, radiative lifetimes many different types rare-earth-doped glasses. The optimized ANN architecture for forecasting were found be near experimentally measured parameters. Then, conferred model employed some newly prepared borosilicate Therein, a new system 0.25 PbO–0.2 SiO2–(0.55 ? x) B2O3–x Dy2O3, order employ melt-quenching technique. parameter results theory, well ?6 showed that supplementation Dy2O3 switched BO4 units BO3 with oxygens non-bridging atoms, thus weakening frameworks. Therefore, it is important use an several glasses luminescent materials.