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Magnetic Transition Analysis in LaFeO₃ Samples

A more detailed discussion is found in the "analysis.ipynb" file

This repository presents the analysis of experimental magnetization data (M vs T) obtained from VSM measurements, with the goal of identifying and characterizing magnetic curves in LaFeO₃-based samples in the context of the analysis of these samples for my undergraduate thesis (Green precursor-assisted solid-state synthesis of LaFeO3 perovskite: role of tannic acid on structural and magnetic properties).

Overview

Two types of samples were studied: SS5: synthesized via solid-state method
ATSS5: synthesized using a tannic-acid-assisted route

Magnetic measurements were performed under: ZFC (Zero-Field-Cooling) FC (Field-Cooling)

The analysis focuses on understanding how synthesis conditions and microstructural differences affect the magnetic response.

Methodology

  1. Data loading and cleaning

    • Parsing .DAT files from VSM measurements
    • Extracting relevant columns (Temperature, Magnetization)
  2. Data processing

    • Normalization by sample mass and realization of the graph of the curves M vs T.
    image
  3. Feature extraction and peak fitting

    • Identification of transition temperatures from peaks in dM/dT
    • Lorentzian model using lmfit
    • Extraction of: μ (mu) → transition temperature
      σ (sigma) → peak width (transition broadening)
  4. Visualization

    • dM/dT curves for each sample and overlay of experimental data and fitted models.
image

Results

  • A magnetic transition is observed around 260 K (250 K) in all samples.
  • The transition in SS5 is sharper and better defined.
  • ATSS5 shows a broader and less defined transition.

These results are consistent with:

  • Presence of a hematite (Fe₂O₃) secondary phase (from XRD)
  • Differences in particle size distribution (from SEM)

The transition is attributed to the Morin transition of hematite, whose temperature depends on particle size and structural heterogeneity.

Tools & Libraries

  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • lmfit

About

Processed and analyzed experimental datasets for my undergraduate thesis using Python (Pandas, NumPy), including derivative computation and nonlinear fitting (lmfit). Extracted key features from magnetization vs temperature data and visualized results to support physical interpretation.

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