{"title":"Enhancing the electronic properties of TiO2 nanoparticles through carbon doping: An integrated DFTB and computer vision approach","authors":"","doi":"10.1016/j.commatsci.2024.113248","DOIUrl":null,"url":null,"abstract":"<div><p>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 TiO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> 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 TiO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> 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 TiO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> 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 TiO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> NPs across various doping levels, combined with their computational advantages, represents a significant advancement in materials science.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927025624004695","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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 TiO 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 TiO 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 TiO 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 TiO NPs across various doping levels, combined with their computational advantages, represents a significant advancement in materials science.
期刊介绍:
The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.