JOURNAL ARTICLE

Elemental behaviors of γ‐irradiated borosilicate glass as a vitrification model.

  • Published In: International Journal of Applied Glass Science, 2023, v. 14, n. 3. P. 380 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Jiandong; Xia, Xiaoyu; Zeng, Fanrong; Xi, Xiaochong; Zhang, Xiaoyang; Pan, Yuhe; Sun, Yuxi; Jia, Wenbao; Peng, Haibo 3 of 3

Abstract

Borosilicate glass has been extensively studied due to its unique properties of solidifying high‐level radioactive waste (HLW). However, the responses of borosilicate glass under γ irradiation are not fully understood. In this work, NBS9 and NBS10 glass were irradiated by γ‐rays at absorbed doses of 8 kGy and 800 kGy, respectively. Scanning electronic microscopy, energy dispersive X‐ray, and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) were used to observe the surface morphology and elemental distributions. The results show that the borosilicate glass remains stable until the absorbed dose was up to 800 kGy. At 800 kGy, the samples precipitate particles composed of Na and O on the surface. Na and B near the surface are significantly reduced under γ‐rays irradiation. The results indicate that the effects of γ irradiation on glass vitrification are obvious with certain accumulated doses. The changes of glass structures and elemental distributions by γ‐ray irradiation are also dependent on glass compositions. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Applied Glass Science. 2023/07, Vol. 14, Issue 3, p380
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:2041-1286
  • DOI:10.1111/ijag.16632
  • Accession Number:164064035
  • Copyright Statement:Copyright of International Journal of Applied Glass Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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