An alpha decay study of superheavy nuclei within a Coulomb and proximity potential model including a Q-value dependent surface diffuseness parameter.

  • Published In: International Journal of Modern Physics E: Nuclear Physics, 2024, v. 33, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Babu, Ananya; Damodaran, Lisha; Suresh, T. P.; Prathapan, K. 3 of 3

Abstract

Using the Coulomb and Proximity Potential Model (CPPM) and a Q-value dependent surface diffuseness parameter in the proximity potential, the alpha decay half-lives of 75 experimentally synthesized superheavy nuclei were determined. Comparisons are made between the obtained results and experimentally determined alpha decay half-lives, as well as the predictions of Yang et al.'s recent study [Nucl. Phys. A 1014 (2021) 122250], which is based on the Unified Fission Model (UFM) with a new preformation parameter. The lowest standard deviation of 0.621 is obtained for CPPM calculations with Q-value-dependent surface diffuseness parameter. The calculation is extended further to estimate the alpha decay half-lives of 2 8 4 − 3 1 5 119 and 2 8 7 − 3 1 6 120 superheavy isotopes and compared with the predictions of UFM. Finally, the results of the calculations are compared to the predictions of the UDL formula [Phys. Rev. Lett. 103 (2009) 072501] and the modified Hutsukawa formula [Nucl. Part. Phys. Proc. 339 (2023) 92]. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics E: Nuclear Physics. 2024/08, Vol. 33, Issue 8, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0218-3013
  • DOI:10.1142/S0218301324500319
  • Accession Number:179689783
  • Copyright Statement:Copyright of International Journal of Modern Physics E: Nuclear Physics is the property of World Scientific Publishing Company 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.