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<p>Zinc oxide (ZnO), a wide-bandgap semiconductor, exhibits tunable dielectric and electrical properties when doped with transition (e.g., Cu, Ni, Co) or alkaline-earth metals (e.g., Ca, Mg). This review examines recent studies on sol–gel synthesized doped ZnO nanoparticles, focusing on how dopant type, concentration, and synthesis conditions influence dielectric constant (ranging from ~12 to 85), dielectric loss, and AC conductivity (improved by up to 100× in some doped samples). Frequency- and temperature-dependent behaviors are discussed, with attention to space charge polarization and Maxwell–Wagner relaxation as key mechanisms. Structural factors such as crystallite size, porosity, and grain boundary effects are linked to electrical performance. The review also identifies key challenges, including dopant segregation and poor reproducibility in sol–gel synthesis, which limit scalability and device integration. Finally, it outlines data-driven approaches such as machine learning for guiding the future design of ZnO-based dielectric materials for nanoelectronics and energy storage applications.</p><p></p>

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Sol–gel derived ZnO nanoparticles doped with transition and alkaline-earth metals: a comprehensive review on dielectric and electrical properties

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Mokhtar Hjiri,
  • N. Mustapha

摘要

Zinc oxide (ZnO), a wide-bandgap semiconductor, exhibits tunable dielectric and electrical properties when doped with transition (e.g., Cu, Ni, Co) or alkaline-earth metals (e.g., Ca, Mg). This review examines recent studies on sol–gel synthesized doped ZnO nanoparticles, focusing on how dopant type, concentration, and synthesis conditions influence dielectric constant (ranging from ~12 to 85), dielectric loss, and AC conductivity (improved by up to 100× in some doped samples). Frequency- and temperature-dependent behaviors are discussed, with attention to space charge polarization and Maxwell–Wagner relaxation as key mechanisms. Structural factors such as crystallite size, porosity, and grain boundary effects are linked to electrical performance. The review also identifies key challenges, including dopant segregation and poor reproducibility in sol–gel synthesis, which limit scalability and device integration. Finally, it outlines data-driven approaches such as machine learning for guiding the future design of ZnO-based dielectric materials for nanoelectronics and energy storage applications.