JOURNAL ARTICLE

Analysis of hysteresis loops and dielectric performance in the ovalene-like system: Monte Carlo study.

  • Published In: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2025, v. 39, n. 11. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Sabbah, Hussein; Fadil, Z.; El Fdil, R.; Ifseisi, Ahmad A.; Rosaiah, P.; Raorane, Chaitany Jayprakash 3 of 3

Abstract

This Monte Carlo study offers a comprehensive analysis of the ovalene-like system, emphasizing its dielectric behavior, thermal performance and hysteresis loops. The investigation reveals intricate dynamics influenced by diverse parameters, contributing to a nuanced understanding of the system's response. Additionally, the study scrutinizes the impact of various physical parameters on the thermal behavior of polarizations and susceptibilities. The examination thoroughly explores the influence of these parameters on loop characteristics, coercive field, and saturation behavior, unveiling the formation of multiple loops and polarization plateaus on the electric hysteresis. In summary, this study advances our understanding of the ovalene-like system's dielectric response, thermal characteristics and hysteresis dynamics, providing valuable insights for future research and applications, particularly in advanced electronic devices. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 2025/04, Vol. 39, Issue 11, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:0217-9792
  • DOI:10.1142/S0217979225500821
  • Accession Number:184452588
  • Copyright Statement:Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied 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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