論文

査読有り
2020年6月2日

Application of multiple γ-ray detection to long-lived radioactive nuclide determination in environmental samples

Journal of Nuclear Science and Technology
  • Masumi Oshima
  • ,
  • Jun Goto
  • ,
  • Tomoko Haraga
  • ,
  • Tadahiro Kin
  • ,
  • Yurie Ikebe
  • ,
  • Hirofumi Seto
  • ,
  • Shigeru Bamba
  • ,
  • Hirofumi Shinohara
  • ,
  • Takao Morimoto
  • ,
  • Keisuke Isogai

57
6
開始ページ
663
終了ページ
670
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1080/00223131.2019.1710614
出版者・発行元
Informa UK Limited

Gamma-gamma coincidence measurement utilized in gamma-ray spectroscopy experiments is well known to be effective for the improvement of signal-to-noise ratio in a gamma-ray spectrum. We study its applicability to the determination of long-lived radioactive nuclides in environmental samples. The gamma-ray simulation code Geant 4.10.2 was used. A conventional and effective detector system comprising five Ge detectors was assumed. We took up 38 nuclides which need to be determined for the evaluation of fission product leakage at the nuclear accident in the Fukushima nuclear power plants in Japan. Among them 12 nuclides emit gamma-rays and five nuclides of Co-60, Nb-94, Cs-134, Eu-152, and Eu-154 can be the objectives of the multiple gamma-ray detection methods. The simulation results indicate that the signal-to-noise ratio can be improved by a factor between 9.84 and 283, and the detection limit by a factor between 2.71 and 8.53 relative to the singles measurement, implying that the method can be well applied to the determination of the long-lived radioactive nuclides and will provide a quick and non-destructive analysis method.

リンク情報
DOI
https://doi.org/10.1080/00223131.2019.1710614
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000506117500001&DestApp=WOS_CPL
URL
https://www.tandfonline.com/doi/pdf/10.1080/00223131.2019.1710614
ID情報
  • DOI : 10.1080/00223131.2019.1710614
  • ISSN : 0022-3131
  • eISSN : 1881-1248
  • Web of Science ID : WOS:000506117500001

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