Machine learning-driven prediction of gamma-ray shielding performance in T e O 2 − B i 2 O 3 − Z n O − B a F 2 glasses

Radiation Physics and Chemistry 2026

View at publisher

Authors: Y. A. Tang

DOI: 10.1016/j.radphyschem.2025.113586

Cited by: 4

References: 48

Fields of Study & Subjects

Radiation Shielding Materials Analysis Nuclear materials and radiation effects Radioactivity and Radon Measurements

References 48