Er2O3-Doped Lead Borate Glasses: Advanced Optical and Radiation Shielding Performance

Abstract

A new glass system with the composition 60B₂O₃ + 30PbF₂ + (10−x)K₂O + x Er₂O₃ (x = 0 to 3 mol%) were synthesized using the melt-quenching technique and comprehensively analyzed to evaluate their structural, optical, mechanical, and radiation shielding properties. Increasing Er₂O₃ concentration enhanced the density (from 4.260 to 4.89 g/cm³) and reduced the molar volume (from 29.28 to 28.98 cm³/mol), indicating a denser and more compact glass matrix. Optical studies revealed increased UV absorbance, a red shift in the cutoff wavelength, and a reduction in the optical energy gap from 3.487 to 3.335 eV (direct transitions). Urbach energy values increased from 0.722 to 1.083 eV, signifying heightened structural disorder. Enhanced refractive index and extinction coefficients further underscored the glasses’ potential for optical applications. Mechanical analyses demonstrated a significant increase in all elastic moduli, including Young’s, bulk, and shear moduli, with Er₂O₃ incorporation, indicating improved rigidity and mechanical stability. The radiation shielding performance of the glasses was assessed across photon energies of 0.015–15 MeV, incorporating both experimental data and machine learning (ML)-based predictions of mass attenuation coefficients (MAC). The ML model, developed using a neural network architecture, successfully predicted MAC values with high accuracy, demonstrating excellent agreement with XCOM-calculated results. Key shielding parameters, including half-value layer (HVL), effective atomic number (Z_eff), and buildup factors (EABF and EBF), improved significantly with higher Er₂O₃ content. BPKE3 glass, with the highest Er₂O₃ concentration, exhibited the best shielding efficiency, outperforming conventional shielding materials in terms of lower HVL and buildup factors, coupled with higher MAC and Z_eff values. This study highlights the dual role of Er₂O₃-doped lead borate glasses as efficient optical and radiation shielding materials. Machine learning effectively predicts shielding parameters, aiding material optimization for applications in nuclear, medical, and industrial fields.