ArticleScientific reports2026
Development of a model for design radiation shielding composite aprons using machine learning.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network (ANN). The ANN model achieved a high predictive accuracy for the mass attenuation coefficient, with a correlation coefficient of R = 0.997 and a mean absolute percentage error of 0.9-1.8%, closely matching benchmark Geant4 Monte Carlo simulations. The optimized Ba-Gd-I-Sb composite (73.9% Ba, 18.2% Gd, 7.65% I) exhibits superior shielding by exploiting synergistic multi-edge absorption. Key quantitative results show that a 1-mm thickness provides > 10⁵ attenuation at 50 keV, > 95% absorption at 1.0 mm thickness, and a favorable weight-to-performance ratio (1.08 kg for a 0.5 mm shield). The composite's attenuation of 99% at 80 keV exceeds that of commercial non-lead alternatives (97-98%), while its transmission matches that of lead at lower energies. These findings demonstrate the composite's high performance and potential as a sustainable, efficient solution for medical and industrial radiation protection.
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