JOURNAL ARTICLE

Study of quantum Stirling heat engine in Tsallis formalism under Dzyaloshinskii-Moriya interaction.

  • Published In: International Journal of Modern Physics C: Computational Physics & Physical Computation, 2026, v. 37, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Khordad, R.; Rastegar Sedehi, H. R.; Ghaffaripour, A. 3 of 3

Abstract

The subject of quantum heat engine (QHE) is an interesting challenge in condensed matter physics. The Stirling QHE is a groundbreaking advancement in the field of quantum machines. The machine can be employed as an efficient heat engine and an innovative refrigerator. In this work, the property of the Stirling QHE is examined by treating its working substance as a two-qubit Heisenberg model, taking into account the Dzyaloshinskii-Moriya (DM) interaction, and a magnetic field within the framework of Tsallis formalism. The novelty of the work is to use nonextensive thermodynamics in studying the quantum Stirling engine. We investigate the influence of the nonextensive Tsallis parameter (q) , DM parameter and magnetic field on various aspects of the Stirling heat engine, including absorbed heat, released heat, work done and efficiency. The findings show that the nonextensive parameter has an important role in efficiency. The best value for the efficiency of this QHE can be obtained by setting sufficient values for the system parameters such as nonextensive parameter, DM parameter and magnetic field. Here, we obtained a maximum value of 0.4 for the efficiency of the QHE at q = 0. 9 6. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics C: Computational Physics & Physical Computation. 2026/04, Vol. 37, Issue 4, p1
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2026
  • ISSN:0129-1831
  • DOI:10.1142/S0129183125500883
  • Accession Number:189717632
  • Copyright Statement:Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation 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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