Beyond Bean's critical state model: On the origin of paramagnetic Meissner effect
Sangjun Oh, Dong Keun Oh, Won Nam Kang, Jung Ho Kim, Shi Xue Dou,, Dojun Youm, Dong Ho Kim

TL;DR
This paper introduces a new macroscopic model combining London theory and Bean's model to explain both reversible and irreversible magnetic properties in superconductors, including the paramagnetic Meissner effect.
Contribution
It proposes a unified macroscopic framework that extends Bean's model by incorporating flux shares and explains the paramagnetic Meissner effect.
Findings
Introduction of 'flux share' concept for magnetic flux penetration
Explanation of paramagnetic Meissner effect using the model
Analysis of reversible and irreversible magnetization behaviors
Abstract
Solving phenomenological macroscopic equations instead of microscopic Ginzburg-Landau equations for superconductors is much easier and can be advantageous in a variety of applications. However, till now, only Bean's critical state model is available for the description of irreversible properties. Here we propose a plausible overall macroscopic model for both reversible and irreversible properties, combining London theory and Bean's model together based on superposition principle. First, a simple case where there is no pinning is discussed, from which a microscopic basis for Bean's model is explored. It is shown that a new concept of 'flux share' is needed when the field is increased above the lower critical field. A portion of magnetic flux is completely shielded, named as 'Meissner share' and the rest penetrates through vortices, named as 'vortices share'. We argue that the flux shares…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMolecular spectroscopy and chirality · Origins and Evolution of Life · Nonlinear Dynamics and Pattern Formation
