Deep learning prediction for gamma-ray attenuation behavior of the KNN-LMN based lead-free ceramics
AuthorMalidarre, Roya Boodaghi
Özhan Doğan, Serap
Malidarreh, Parisa Boodaghi
MetadataShow full item record
CitationMalidarre, R. B., Arslankaya, S., Nar, M., Kirelli, Y., Erdamar, I. Y. D., Karpuz, N., . . . Malidarreh, P. B. (2022). Deep learning prediction for gamma-ray attenuation behavior of the KNN-LMN based lead-free ceramics. Emerging Materials Research, doi:10.1680/jemmr.22.00012
The significance and novelty of the present work is the preparation of the non-lead ceramic by the general formula of (1-x) K0.5Na0.5NbO3-xLaMn0.5Ni0.5O3 with different x (0<x<20) (mol%) to examine the shielding qualities of KNN-LMN ceramic. This is done using PhyX/PSD calculation and predicts the attenuation behavior of the samples utilizing the Deep Learning (DL) algorithm. From the attained results it is seen that the higher the x (concentration of the LMN in KNN-LMN lead-free ceramics) the better shielding proficiency is observed in terms of gamma shielding performances for chosen KNN-LMN based lead-free ceramics. In all sections, a good agreement is observed between PhyX/PSD results and DL predictions. © 2022 ICE Publishing: All rights reserved.