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<p>Hexagonal boron nitride (h-BN) is attracting increased attention as a substrate material for functional nanoparticles (NPs) implemented in aerospace, energy, and catalytic applications. In this context, the oxidation resistance of h-BN and their defect structure become key properties. In this study, we investigate the oxidation behavior of h-BN micro- and nanoparticles through air annealing at 900&#xa0;°C for intervals ranging from 10&#xa0;min to 5&#xa0;h. To study the influence of defect structure on the oxidation process, the h-BN materials were characterized using XRD, EDX, XPS, and TGA. SEM and TEM results show the formation and structural evolution of boron oxide NPs during long-term annealing. Oxidation-induced defects in the h-BN structure, as well as their influence on the thermal stability and agglomeration of metallic NPs on h-BN surfaces, are studied in detail. The study combines experimental and theoretical approaches using a deep neural network method.</p> Graphical abstract <p></p>

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Oxidation peculiarities and defects formation in h-BN micro- and nanoparticles

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Anton Konopatsky,
  • Tatyana Teplyakova,
  • Ekaterina Sukhanova,
  • Viktor Baidyshev,
  • Zakhar Popov,
  • Dmitry Shtansky

摘要

Hexagonal boron nitride (h-BN) is attracting increased attention as a substrate material for functional nanoparticles (NPs) implemented in aerospace, energy, and catalytic applications. In this context, the oxidation resistance of h-BN and their defect structure become key properties. In this study, we investigate the oxidation behavior of h-BN micro- and nanoparticles through air annealing at 900 °C for intervals ranging from 10 min to 5 h. To study the influence of defect structure on the oxidation process, the h-BN materials were characterized using XRD, EDX, XPS, and TGA. SEM and TEM results show the formation and structural evolution of boron oxide NPs during long-term annealing. Oxidation-induced defects in the h-BN structure, as well as their influence on the thermal stability and agglomeration of metallic NPs on h-BN surfaces, are studied in detail. The study combines experimental and theoretical approaches using a deep neural network method.

Graphical abstract