JOURNAL ARTICLE
Dynamic behavior of a magnetic system driven by an oscillatory external temperature.
Published In: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2025, v. 39, n. 16. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Rubio Puzzo, M. Leticia 3 of 3
Abstract
The dynamic effects on a magnetic system exposed to a time-oscillating external temperature are studied using Monte-Carlo simulations on the classical 2D Ising Model. The time dependence of temperature is defined as T (t) = T 0 + A ⋅ sin (2 π t ∕ τ). Magnetization M (t) and period-averaged magnetization 〈 Q 〉 are analyzed to characterize out-of-equilibrium phenomena. Hysteresis-like loops in M (t) are observed as a function of T (t). The area of the loops is well-defined outside the critical Ising temperature ( T C ) but takes more time to close it when the system crosses the critical curve. Results show a power-law dependence of 〈 Q 〉 (the averaged area of loops) on both L and τ , with exponents α = 1. 0 (1) and β = 0. 7 0 (1) , respectively. Furthermore, the impact of shifting the initial temperature T 0 on 〈 Q 〉 is analyzed, suggesting the existence of a τ -dependent effective temperature T eff (τ). A scaling law behavior for 〈 Q 〉 is found on the base of this τ -dependent temperature. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 2025/06, Vol. 39, Issue 16, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:0217-9792
- DOI:10.1142/S0217979225501371
- Accession Number:184926238
- 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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