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Neutron capture cross section measurement for the 139La(n,γ)140La reaction at 0.0372 eV

  • Naima Belouadah EMAIL logo , Nadjet Osmani , Khelifa Boukeffoussa ORCID logo , Fatiha Kadem , Leila Yettou , Mohamed Belgaid , Nabila Taibouni ORCID logo and Mohamed Trari
Published/Copyright: October 11, 2024

Abstract

The thermal neutron capture cross section of the 139La(n,γ)140La nuclear reaction at 0.0372 eV energy was measured by the Neutron Diffraction Facility (NDF) in the Es-Salam Nuclear Research Reactor (Algeria). The 197Au(n,γ)198Au reaction was adopted as reference for the Neutron Activation Analysis (NAA). The induced activities in sample and control foils were measured non-destructively by a high-resolution HPGe γ-ray detector. The capture cross section of σ(∼7.5 ± 0.37) b was measured for the first time at this energy after correction for the γ attenuation effect. Our measured value at an energy of 0.0372 eV energy was compared with the evaluated cross sections reported in two different neutron databases, namely ENDF/B-VIII.0 and JENDL-5. The data measured at this energy was extrapolated to the energy 0.0253 eV, assuming 1/ν dependence in the thermal energy region, and the result was compared with the fully measured and evaluated values reported in the literature.


Corresponding author: Naima Belouadah, SNIRM Laboratory, Faculty of Physics (USTHB), P.O. Box 32, El-Alia, Algeria, E-mail:

Acknowledgments

The authors would like to express their gratitude to the staff of the Es-Salam Nuclear Research Reactor of Birine (Algeria) for their help in performing irradiations and their cordial help in carrying out experiment.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: NB: Methodology, Formal analysis, Investigation, Data curation, writing and original draft; NO: Software, Formal analysis, Validation; KB: Formal analysis, Validation; FK: Software, Validation; LY: Software, Validation; MB: Conceptualization, Supervision, NT: Software, Validation; MT: Conceptualization, Supervision.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All authors declare no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-05-01
Accepted: 2024-09-25
Published Online: 2024-10-11
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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