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<p>Doping metal oxides with transition and alkaline earth metals is a well-established strategy to tailor their structural and optical properties for advanced functional applications. However, the effects of different dopant ions on host lattices like aluminium oxide (Al₂O₃) are often inconsistent due to variations in ionic radii, valence states, and defect formation. To address these challenges, this paper presents a systematic investigation of Al₂O₃ nanoparticles doped with 2 wt.% magnesium (Mg), zinc (Zn), and iron (Fe), synthesized using a co-precipitation method. The Verifiable Convolutional Neural Network (VCNN) with the Frilled Lizard Optimization (FLO) is used as a novel approach. The primary objective of the proposed method is to enhance the efficiency and achieve a bandgap in Al₂O₃ nanoparticles through doping, thereby improving blue photoluminescence for optoelectronic applications. The synthesized nanoparticles were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. The proposed method was implemented using the MATLAB platform and compared with existing techniques, including Multi-layer Perceptron (MLP), Graph Neural Network (GNN), and Deep Neural Network (DNN). The results indicate that doping Al₂O₃ with Mg leads to enhanced optical properties, with an energy bandgap of 2.7&#xa0;eV for the Mg-doped sample, compared to 3.03&#xa0;eV for pure Al₂O₃. The proposed method demonstrates the potential for improving the semiconductor behavior and optical characteristics of Al₂O₃ nanoparticles, which are valuable for various applications in material design and optoelectronics.</p>

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Enhancement of structural and functional properties of Al2O3 nanoparticles through magnesium, zinc, and iron doping for advanced technological applications

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • T. R. Jeena,
  • S. Antony Dominic Christopher,
  • V. Beena,
  • P. M. Shajin Shinu,
  • D. Shiney Manoj,
  • S. S. Bidhu,
  • T. R. Beena,
  • B. Queen Sheeba,
  • M. Amalanathan,
  • N. Sheen Kumar,
  • Lekshmi S,
  • A. Seema,
  • D. M. Suresh,
  • V. S. Shali

摘要

Doping metal oxides with transition and alkaline earth metals is a well-established strategy to tailor their structural and optical properties for advanced functional applications. However, the effects of different dopant ions on host lattices like aluminium oxide (Al₂O₃) are often inconsistent due to variations in ionic radii, valence states, and defect formation. To address these challenges, this paper presents a systematic investigation of Al₂O₃ nanoparticles doped with 2 wt.% magnesium (Mg), zinc (Zn), and iron (Fe), synthesized using a co-precipitation method. The Verifiable Convolutional Neural Network (VCNN) with the Frilled Lizard Optimization (FLO) is used as a novel approach. The primary objective of the proposed method is to enhance the efficiency and achieve a bandgap in Al₂O₃ nanoparticles through doping, thereby improving blue photoluminescence for optoelectronic applications. The synthesized nanoparticles were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and photoluminescence (PL) spectroscopy. The proposed method was implemented using the MATLAB platform and compared with existing techniques, including Multi-layer Perceptron (MLP), Graph Neural Network (GNN), and Deep Neural Network (DNN). The results indicate that doping Al₂O₃ with Mg leads to enhanced optical properties, with an energy bandgap of 2.7 eV for the Mg-doped sample, compared to 3.03 eV for pure Al₂O₃. The proposed method demonstrates the potential for improving the semiconductor behavior and optical characteristics of Al₂O₃ nanoparticles, which are valuable for various applications in material design and optoelectronics.