JOURNAL ARTICLE

α-particle clusterization: A reflection of shell closures.

  • Published In: International Journal of Modern Physics E: Nuclear Physics, 2024, v. 33, n. 11. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bharmoria, Shubham; Ramit, Ramit; Sharma, Mridula; Kaur, Sukhnandan; Singh, Anupinder; Kaur, Harjeet 3 of 3

Abstract

We employ a theoretical framework motivated by a microscopic phenomenological approach involving Skyrme force model to account for the interaction of α -particle with the nucleons of α -radioactive nucleus. The realistic calculations for α -particle preformation probabilities ( P α ) of even–even nuclei whose atomic numbers lie in the range 8 4 ≤ Z ≤ 1 1 8 are performed. Through the calculations of assault frequency possessed by α -particle, shell effects are naturally incorporated in this methodology. The obtained results are then subjected to fitting procedures, resulting in a comprehensive fit within N p N n I -scheme. These fitted α -particle preformation probabilities are utilized to predict the half-lives of unknown superheavy nuclei (SHN). This work not only refines our understanding about radioactive properties of these nuclei but also provides valuable insights into their stability against α -decay and spontaneous fission (SF). Twelve α -decay chains originating from unknown SHN have been identified in this work. The outcomes of this research would contribute to the advancement of predictive models to study the fundamental properties of SHN, offering an experimental verification as well. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics E: Nuclear Physics. 2024/11, Vol. 33, Issue 11, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0218-3013
  • DOI:10.1142/S0218301324500460
  • Accession Number:182904443
  • 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.)

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