Deep learning prediction of gamma-ray-attenuation behavior of KNN-LMN ceramics

Abstract

The significance and novelty of the present work is the preparation of non-lead ceramics with the general formula of (1 - x)K0.5Na0.5NbO3-xLaMn(0.5)Ni(0.5)O(3) (KNN-LMN) with different values of x (0 < x < 20) (mol%) to examine the shielding qualities of the KNN-LMN ceramics. This is done by carrying out Phy-X/PSD calculation and predicting the attenuation behavior of the samples by utilizing the deep learning (DL) algorithm. From the attained results, it is seen that the higher the x (concentration of LMN in the KNN-LMN lead-free ceramics), the better the shielding proficiency observed in terms of gamma-shielding performance for the chosen KNN-LMN-based lead-free ceramics. In all sections, good agreement is observed between Phy-X/PSD results and DL predictions.