Work

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

Year: 2022

Type: article

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–xLaMn0.5Ni0.5O3(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.

Source: Emerging Materials Research

DOI: 10.1680/jemmr.22.00012

Authors: Roya Boodaghi Malidarre, Seher Arslankaya, Melek Nar, Yasin Kirelli, Isık Yesim Dicle Erdamar, Nurdan Karpuz, Serap Ozhan Dogan, Parisa Boodaghi Malidarreh

Institutions: Payame Noor University, Sakarya University, Istinye University, Dicle University, Amasya Üniversitesi, Beykent University, Iran University of Science and Technology

Language: English

Topic: Radiation Shielding Materials Analysis

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4286424741

FWCI: 0.987

Cites: 57

Cited by: 16

Related to: 10