Experimental investigation for the impacts of Er2O3 content on enhancing the physical and radiation shielding capabilities of composites based-epoxy
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Mohammed I. Sayyed
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Taha Yaseen Wais
Abstract
By employing erbium oxide (Er2O3) as fillers, the current study seeks to improve the physical and radiation shielding performance of epoxy composites. Therefore, a series of epoxy-based composites were fabricated according to the chemical formula of (90−x) epoxy + 10 Eu2O3 + x Er2O3; x ≈ 0, 5, 10, and 15 wt%. The impact of Er2O3 fillers on the physical and radiation shielding properties of the prepared composites was investigated experimentally. The change in density of prepared epoxy-based Er2O3 composites was measured according to the Archimedes method. The recorded data confirms an increase in the prepared composites’ density between 1.188 ± 0.018 g/cm3 and 1.375 ± 0.021 g/cm3 when the Er2O3 concentration raised throughout a concentration range of 0–15 wt%. The impact of Er2O3 on the gamma-ray shielding performance of epoxy-based composites was evaluated experimentally using the narrow beam transmission method. During the application of the narrow beam transmission method, a 5.08 × 5.08 cm NaI (Tl) crystal and two radioactive sources (Cs-137 and Co-60) were utilized. The measured data showed that the increase in Er2O3 concentrations between 0 wt% and 15 wt% enhances the linear attenuation coefficient of the prepared composites between 0.0959 ± 0.0051 cm−1 and 0.1093 ± 0.0069 cm−1, respectively, at 0.662 MeV. The investigation shows that the increase in the Er2O3 concentration improves the linear attenuation coefficient by 14.04 %, 11.62 %, and 15.73 % at 0.662 MeV, 1.173 MeV, and 1.332 MeV, respectively.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Laith Ahmed Najam and Taha Yaseen Wais: Material preparation, data collection, analysis; Karem A. Mahmoud and M.I. Sayyed: writing the draft manuscript and the final version, revision, software, investigations. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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