Synthesis, structure, radiation attenuation efficacy as well as prediction of density using artificial intelligence techniques of lead borate lithium zinc strontium glasses

Abstract

Glass systems (6-glasses) of chemical formula (60-x)B2O3-10Li2O-20ZnO-10PbO-xSrO: 0 (Sr-0) ≤ x ≤ 25 (Sr-25) mol% have been manufactured. Density was measured experimentally and predicted via artificial intelligence (AI) and machine learning (ML) techniques. As well as radiation attenuation efficacy has been evaluated. Experimentally, density was varied from 3.2824 ± 0.0001 to 3.8717 ± 0.0001 g/cm3. Sample densities were predicated via the artificial neural network (ANN), Tanh and sigmoid activation functions showed the best density prediction with a maximum R2 of highest 0.8900. The best density prediction was also obtained by the random forest regression (RFR) model with R2 = 0.920 which is observed to be the highest compared to the polynomial. The sample encoded as Sr-25 had the greatest linear attenuation coefficient (LAC) values out of all the samples that were examined. The transmission factor (TF%) is simultaneously dependent on the samples' density and the energy of the incoming photons for example the sample of a density of 3.65 g/cm3 (Sr-15) and thickness of x = 1.0 cm. For instance, the TF values are 2.5%, 51.7%, 79.5%, 88.7%, 89.3%, 89.3%, and 88.9% for the energies 0.08, 0.3, 1.0, 5.0, 8.0, 10.0, and 15.0 MeV, respectively. In terms of the half value layer (HVL), the sample encoded as Sr-0 has the greatest value while the sample Sr-25 possessed the lowest HVL value among the studied glass mixtures.