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<p>In this study, magnetic glass-ceramics (MGC) were synthesized using the sol-gel method, complemented by a machine learning regression model for predictive analysis. Bioglass 58S served as the glass matrix, while <i>M</i><i>g</i><i>F</i><i>e</i><sub>2</sub><i>O</i><sub>4</sub> nanoparticles were incorporated as the magnetic phase at weight percentages of 5, 10, 15, and 20%wt. The resultant MGC were characterized using X-ray diffraction (XRD), vibrating sample magnetometry (VSM), and transmission electron microscopy (TEM). XRD analysis confirmed the successful incorporation of the <i>M</i><i>g</i><i>F</i><i>e</i><sub>2</sub><i>O</i><sub>4</sub> phase within the glass-ceramic matrix. VSM measurements indicated that MGC exhibited soft magnetic material behavior with saturation magnetization value of 7.95 emu/g. TEM analysis revealed well defined crystalline structures corresponding to magnesium ferrite in specific regions, while other areas exhibited an amorphous structure. The average particle size was found to be 6.1 nm. The integration of an intelligent, machine learning-based approach facilitated a more efficient synthesis process for the MGC. Furthermore, the experimentally validated data corroborated the computational predictions, leading to the development of MGC with enhanced magnetic properties.</p> Graphical Abstract <p></p>

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An intelligent prediction model for the synthesis of magnetic glass-ceramic and experimental validation

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Diego H. de Hoyos-Sifuentes,
  • Perla J. Reséndiz-Hernández,
  • Edgar O. Reséndiz-Flores,
  • Luis A. García-Cerda,
  • Rocio M. Ochoa-Palacios

摘要

In this study, magnetic glass-ceramics (MGC) were synthesized using the sol-gel method, complemented by a machine learning regression model for predictive analysis. Bioglass 58S served as the glass matrix, while MgFe2O4 nanoparticles were incorporated as the magnetic phase at weight percentages of 5, 10, 15, and 20%wt. The resultant MGC were characterized using X-ray diffraction (XRD), vibrating sample magnetometry (VSM), and transmission electron microscopy (TEM). XRD analysis confirmed the successful incorporation of the MgFe2O4 phase within the glass-ceramic matrix. VSM measurements indicated that MGC exhibited soft magnetic material behavior with saturation magnetization value of 7.95 emu/g. TEM analysis revealed well defined crystalline structures corresponding to magnesium ferrite in specific regions, while other areas exhibited an amorphous structure. The average particle size was found to be 6.1 nm. The integration of an intelligent, machine learning-based approach facilitated a more efficient synthesis process for the MGC. Furthermore, the experimentally validated data corroborated the computational predictions, leading to the development of MGC with enhanced magnetic properties.

Graphical Abstract