Deep learning prediction of gamma-ray-attenuation behavior of KNN–LMN ceramics
| Autor: | Roya Boodaghi Malidarre, Seher Arslankaya, Melek Nar, Yasin Kirelli, Isık Yesim Dicle Erdamar, Nurdan Karpuz, Serap Ozhan Dogan, Parisa Boodaghi Malidarreh |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Emerging Materials Research. 11:276-282 |
| ISSN: | 2046-0155 2046-0147 |
| DOI: | 10.1680/jemmr.22.00012 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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