How interfacial electron-donating defects influence the structure and charge of gold nanoparticles on TiO2 support
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The reduction degree of TiO2 support is critical to the performances of metal catalysts. In many previous theoretical calculations, only the bridge oxygen vacancy (Ov) was considered as the electron-donating defect on reduced rutile TiO2 (r-TiO2−x) supports. However, titanium adatoms (Tiad.), oxidized titanium islands (Tiad.On), and acid hydroxyls (ObrH) also exist at the metal/support interface. By conducting density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations, we compared r-TiO2−x surfaces with Ov, Tiad., Tiad.On, and ObrH sites loaded with Au nanoparticles (NPs). The results showed the Au NPs were oxygen-phobic but titanium-philic, resulting in wetting of Ov and Tiad. but short contact with Tiad.On and ObrH. The Bader charges of Au NPs (QM) showed a good linear relationship with the ideal number of donating electrons (Ne) from the defective sites (QM = −KeNe + QM,S), demonstrating the intrinsic electron allocation at the interface. The Ov, Tiad., and Tiad.On exhibited similar slopes (Ke), relatively steeper than that of ObrH. That means in the scope of Au NP charge state, the Tiad. and Tiad.On have a close electron-donating ability with Ov, but the ObrH donates relatively fewer electrons. This linear relationship can be extended approximately to other metals. The higher the metal work function, the steeper the Ke for easier electron donation from defective sites. The stronger the metal oxygen affinity, the more positive the intercept (QM,S). That explains the easy generation of metallic or negative Pt and Au NPs on r-TiO2−x, but hard for Cu and Zn in experiment. That provides theoretical guidance for regulating the charge of metal NPs over TiO2−x supports.
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How interfacial electron-donating defects influence the structure and charge of gold nanoparticles on TiO2 support
)
Abstract
The reduction degree of TiO2 support is critical to the performances of metal catalysts. In many previous theoretical calculations, only the bridge oxygen vacancy (Ov) was considered as the electron-donating defect on reduced rutile TiO2 (r-TiO2−x) supports. However, titanium adatoms (Tiad.), oxidized titanium islands (Tiad.On), and acid hydroxyls (ObrH) also exist at the metal/support interface. By conducting density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations, we compared r-TiO2−x surfaces with Ov, Tiad., Tiad.On, and ObrH sites loaded with Au nanoparticles (NPs). The results showed the Au NPs were oxygen-phobic but titanium-philic, resulting in wetting of Ov and Tiad. but short contact with Tiad.On and ObrH. The Bader charges of Au NPs (QM) showed a good linear relationship with the ideal number of donating electrons (Ne) from the defective sites (QM = −KeNe + QM,S), demonstrating the intrinsic electron allocation at the interface. The Ov, Tiad., and Tiad.On exhibited similar slopes (Ke), relatively steeper than that of ObrH. That means in the scope of Au NP charge state, the Tiad. and Tiad.On have a close electron-donating ability with Ov, but the ObrH donates relatively fewer electrons. This linear relationship can be extended approximately to other metals. The higher the metal work function, the steeper the Ke for easier electron donation from defective sites. The stronger the metal oxygen affinity, the more positive the intercept (QM,S). That explains the easy generation of metallic or negative Pt and Au NPs on r-TiO2−x, but hard for Cu and Zn in experiment. That provides theoretical guidance for regulating the charge of metal NPs over TiO2−x supports.
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Acknowledgements
This work was financially supported by the National Key Research and Development Program of China (No. 2022YFA1503102), the National Natural Science Foundation of China (Nos. 22022504, 22003022, and 22203041), Guangdong Basic and Applied Basic Research Foundation, China (No. 2021A1515110406), Guangdong “Pearl River” Talent Plan (No. 2019QN01L353), and Guangdong Provincial Key Laboratory of Catalysis (No. 2020B121201002). Most calculations were performed on the CHEM high-performance computing cluster (CHEM-HPC) located at the Department of Chemistry, SUSTech. The computational resources were also supported by the Center for Computational Science and Engineering at the Southern University of Science and Technology (SUSTech).
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