Enhancing the electronic properties of TiO2 nanoparticles through carbon doping: An integrated DFTB and computer vision approach

KURBAN M., Polat C., Serpedin E., Kurban H.

Computational Materials Science, cilt.244, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 244
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.commatsci.2024.113248
  • Dergi Adı: Computational Materials Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: C-doped TiO2, Computer vision, DFTB, Electronic properties, Photocatalysis
  • Ankara Üniversitesi Adresli: Evet

Özet

In this study, an innovative approach is explored that combines Density Functional Tight Binding (DFTB) with Computer Vision (CV) techniques to analyze the electronic structure and enhance the photocatalytic capabilities of carbon-doped titanium oxide nanoparticles (C-doped TiO2 NPs). The findings reveal that C doping, in levels ranging from 0.1% to 0.6% progressively alters the material's electronic structure and photocatalytic activity. Specifically, the energy gap decreases significantly from 3.160 eV for undoped TiO2 to 0.565 eV at 0.6% doping, with no substantial changes observed beyond 0.6% doping. A notable correlation between increased C doping and a rise in total energy suggests a complex interaction between C incorporation and the energetic as well as structural dynamics of TiO2 NPs. This interaction could enhance photocatalytic efficiency, especially under visible light, by reducing the band gap through C doping. The use of CV methodologies improves computational efficiency and predictive accuracy. These techniques validate the DFTB results and accelerate the material discovery process via machine learning models. The ability of CV methods to accurately predict the properties of C-doped TiO2 NPs across various doping levels, combined with their computational advantages, represents a significant advancement in materials science.