JOURNAL ARTICLE

Scattering of Electrons and Positrons by Nitrogen Dioxide.

  • Published In: International Journal of Quantum Chemistry, 2025, v. 125, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kumer, Tusher; Shorifuddoza, M.; Das, Pretam K.; Watabe, Hiroshi; Shahmohammadi Beni, Mehrdad; Haque, A. K. Fazlul; Uddin, M. Alfaz 3 of 3

Abstract

This study presents a comprehensive theoretical investigation into the scattering of electrons and positrons from nitrogen dioxide (NO2) molecules across a broad energy ranging from 1 eV to 1 MeV. The focus of the analysis encompasses a variety of cross‐sections, including differential, integrated elastic, inelastic, total ionization, total, momentum transfer and viscosity. Additionally, the study explores the spin polarization effects within electron/positron‐NO2 scattering events. Utilizing a combination of relativistic Dirac partial wave analysis, the independent atom model (IAM), and the screening adjusted independent atom model (IAMS), this research achieves a refined understanding of scattering mechanisms. Comparative assessments with prior theoretical and empirical findings reveal that the IAM approach yields lesser accuracy at lower energies, while maintaining commendable agreement with existing data at medium to high energies. The insights and methodologies developed herein are anticipated to contribute significantly to the advancement of future research in this domain. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Quantum Chemistry. 2025/01, Vol. 125, Issue 1, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2025
  • ISSN:0020-7608
  • DOI:10.1002/qua.27475
  • Accession Number:182048592
  • Copyright Statement:Copyright of International Journal of Quantum Chemistry 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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