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Machine Learning-Aided Design of Gold Core–Shell Nanocatalysts toward Enhanced and Selective Photooxygenation

Tamtaji, Mohsen and Guo, Xuyun and Tyagi, Abhishek and Galligan, Patrick Ryan and Liu, Zhenjing and Roxas, Alexander and Liu, Hongwei and Cai, Yuting and Wong, Hoilun and Zeng, Lun and Xie, Jianbo and Du, Yucong and Hu, Zhigang and Lu, Dong and Goddard, William A., III and Zhu, Ye and Luo, Zhengtang (2022) Machine Learning-Aided Design of Gold Core–Shell Nanocatalysts toward Enhanced and Selective Photooxygenation. ACS Applied Materials & Interfaces, 14 (41). pp. 46471-46480. ISSN 1944-8244. doi:10.1021/acsami.2c11101.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription information
Tamtaji, Mohsen0000-0001-9118-5474
Guo, Xuyun0000-0003-0365-7545
Tyagi, Abhishek0000-0003-3522-703X
Galligan, Patrick Ryan0000-0002-3094-4067
Liu, Zhenjing0000-0003-4020-1774
Roxas, Alexander0000-0002-8723-5747
Liu, Hongwei0000-0001-7566-5224
Cai, Yuting0000-0002-1758-0086
Wong, Hoilun0000-0002-2343-849X
Goddard, William A., III0000-0003-0097-5716
Luo, Zhengtang0000-0002-5134-9240
Additional Information:Z.L. acknowledges the support by the NSFC-RGC Joint Research Scheme (N_HKUST607/17), the IER foundation (HT-JD-CXY-201907), “International science and technology cooperation projects” of Science and Technological Bureau of Guangzhou Huangpu District (2019GH06), Guangdong Science and Technology Department (Project#:2020A0505090003), Research Fund of GuangdongHong Kong-Macao Joint Laboratory for Intelligent MicroNano Optoelectronic Technology (No. 2020B1212030010), and Shenzhen Special Fund for Central Guiding the Local Science and Technology Development (2021Szvup136). Y.Z. acknowledges the support by the Research Grants Council of Hong Kong (N_PolyU531/18) and the Hong Kong Polytechnic University grant (No. ZVRP). Technical assistance from the Materials Characterization and Preparation Facilities of HKUST is greatly appreciated.
Funding AgencyGrant Number
Guangdong Science and Technology Department2020A0505090003
Research Grants Council, University Grants CommitteeN_HKUST607/17
Research Grants Council, University Grants CommitteeN_PolyU531/18
National Natural Science Foundation of ChinaN_HKUST607/17
Hong Kong Polytechnic UniversityZVRP
Science and Technological Bureau of Guangzhou Huangpu District2019GH06
IER FoundationHT-JD-CXY-201907
Shenzhen Special Fund for Central Guiding the Local Science and Technology Development2021Szvup136
Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology2020B1212030010
Subject Keywords:deep neural networks, electric field, singlet dioxygen, photooxygenation reaction, STEM-EELS, Carbene compounds, Electric fields, Metal nanoparticles, Nanoparticles, Plasmonic nanoparticles
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Issue or Number:41
Record Number:CaltechAUTHORS:20221011-972181000.1
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117366
Deposited By: Donna Wrublewski
Deposited On:12 Oct 2022 17:14
Last Modified:17 Dec 2022 01:39

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