纳米材料基催化剂
材料科学
光氧化
纳米颗粒
等离子体子
胶体金
纳米技术
单线态氧
光电子学
有机化学
化学
氧气
作者
Mohsen Tamtaji,Xuyun Guo,Abhishek Tyagi,Patrick Ryan Galligan,Zhenjing Liu,Alexander Perez Roxas,Hongwei Liu,Yuting Cai,Hoilun Wong,Lun Zeng,Jianbo Xie,Yucong Du,Zhigang Hu,Dong Liu,William A. Goddard,Ye Zhu,Zhengtang Luo
标识
DOI:10.1021/acsami.2c11101
摘要
We demonstrate the use of the machine learning (ML) tools to rapidly and accurately predict the electric field as a guide for designing core-shell Au-silica nanoparticles to enhance 1O2 sensitization and selectivity of organic synthesis. Based on the feature importance analysis, obtained from a deep neural network algorithm, we found a general and linear dependent descriptor (θ ∝ aD0.25t-1, where a, D, and t are the shape constant, size of metal nanoparticles, and distance from the metal surface) for the electric field around the core-shell plasmonic nanoparticle. Directed by the new descriptor, we synthesized gold-silica nanoparticles and validated their plasmonic intensity using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) mapping. The nanoparticles with θ = 0.40 demonstrate an ∼3-fold increase in the reaction rate of photooxygenation of anthracene and 4% increase in the selectivity of photooxygenation of dihydroartemisinic acid (DHAA), a long-standing goal in organic synthesis. In addition, the combination of ML and experimental investigations shows the synergetic effect of plasmonic enhancement and fluorescence quenching, leading to enhancement for 1O2 generation. Our results from time-dependent density functional theory (TD-DFT) calculations suggest that the presence of an electric field can favor intersystem crossing (ISC) of methylene blue to enhance 1O2 generation. The strategy reported here provides a data-driven catalyst preparation method that can significantly reduce experimental cost while paving the way for designing photocatalysts for organic drug synthesis.
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