Magnetization Curve
Chart overview
Magnetization curves measured by SQUID or VSM magnetometry are the primary tool for characterizing long-range magnetic order in materials.
Key points
- The M-T curve reveals the Curie or Neel temperature from the sharp drop in magnetization at the ordering transition, and the divergence between zero-field-cooled (ZFC) and field-cooled (FC) curves identifies superparamagnetic, spin-glass, or cluster-glass behavior.
- The M-H curve at fixed temperature gives saturation magnetization, coercivity, and exchange bias, providing a complete picture of magnetic anisotropy and domain structure.
Example Visualization
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"Create a publication-quality magnetization curve from my SQUID or VSM data. For an M-T plot: plot magnetization (emu/g or uB/f.u.) on the y-axis versus temperature (K) on the x-axis, overlaying ZFC and FC branches in distinct colors with arrows indicating measurement direction. Mark the Curie or Neel temperature Tc with a vertical dashed line. For an M-H plot: plot magnetization versus applied field with annotated coercivity and saturation. Add axis labels with units, a legend, and a descriptive title. White background, professional styling."
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Python Code Example
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Common Use Cases
- 1Determining Curie temperature of ferromagnetic thin films and bulk alloys
- 2Identifying ZFC-FC bifurcation temperature in superparamagnetic nanoparticles
- 3Measuring saturation magnetization of rare-earth permanent magnet compounds
- 4Monitoring magnetic phase transitions under pressure in quantum materials
Pro Tips
Plot ZFC and FC branches in contrasting colors and annotate the bifurcation temperature
Mark the Curie or Neel temperature with a vertical dashed line and text annotation
Use a derivative dM/dT plot in an inset to precisely locate the transition temperature
Normalize magnetization to per formula unit (uB/f.u.) for comparison across compositions
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.