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Materials

Notes on remanent magnetization measurements in superconductors and hard ferromagnets

Sergey L. Bud'ko, Mingyu Xu, Weiwei Xie, Chaowei Hu, Ni Ni, Paul C. Canfield

Featured July 3, 2026

Original Paper

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Simply

By carefully measuring how much magnetism stays in materials after a field is removed, the study shows clear, simple ways to tell if a material is a superconductor or a ferromagnet, especially useful for tiny samples under high pressure.

In depth
The paper presents a rigorous comparative study of remanent magnetization protocols to reliably distinguish between superconducting and ferromagnetic states. By analyzing temperature- and field-dependent magnetization, including zigzag temperature sweeps and relaxation, the authors identify distinct signatures that overcome ambiguities in high-pressure experiments, particularly in diamond anvil cells.

Key Takeaways

  • 1
    The study establishes specific experimental protocols for remanent magnetization measurements that reveal qualitative differences between superconductors and ferromagnets.
  • 2
    It provides clear criteria, such as the behavior of remanent magnetization during zigzag temperature sweeps and the ratio of saturation fields, to differentiate trapped flux from ferromagnetic hysteresis.
  • 3
    The findings are crucial for accurately identifying superconductivity in novel materials under extreme conditions, like those in diamond anvil cells, where conventional methods are challenging.

Conceptual Flow

HIGH LEVEL
1
Methodology: Comparing Magnetic Signatures

The scientists cool materials in different ways and measure their leftover magnetism to see if they act like a superconductor or a regular magnet.

Cool Material
Apply Field
Remove Field
Measure Leftover Magnetism
Superconductor Pattern
Ferromagnet Pattern
2
Results: Distinct Behaviors Revealed

They found that superconductors hold onto magnetism differently when heated and cooled in a special way compared to regular magnets, giving clear clues.

Superconductor Data
Ferromagnet Data
Compare Patterns
Clear Distinction
Reliable Identification