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Nano Research 2022, 15(5): 4000-4007 https://doi.org/10.1007/s12274-022-4111-2
Research Article | Issue | Published: 21 February 2022

Highly efficient metal-free catalyst from cellulose for hydrogen peroxide photoproduction instructed by machine learning and transient photovoltage technology

Show Author's Information Yan Liu1,  Xiao Wang1,  Yajie Zhao1,  Qingyao Wu1,  Haodong Nie1,  Honglin Si1,  Hui Huang1( ),  Yang Liu1( ),  Mingwang Shao1( ),  Zhenhui Kang1,2( )
Institute of Functional Nano and Soft Materials Laboratory (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou 215123, China
Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
Keywords:
machine learning, carbon dots, hydrogen peroxide, cellulose, transient photovoltage, metal-free photocatalyst
Topics:
Catalysis
Cite this article:
Liu Y, Wang X, Zhao Y, et al. Highly efficient metal-free catalyst from cellulose for hydrogen peroxide photoproduction instructed by machine learning and transient photovoltage technology. Nano Research, 2022, 15(5): 4000-4007. https://doi.org/10.1007/s12274-022-4111-2
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Abstract Full text Electronic supplementary material About this article

Great attention has been paid to green procedures and technologies for the design of environmental catalytic systems. Biomass-derived catalysts represent one of the greener alternatives for green catalysis. Photocatalytic production of hydrogen peroxide (H2O2) from O2 and H2O is an ideal green way and has attracted widespread attention. Here, we show a metal-free photocatalyst from cellulose, which has a high photocatalytic activity for the photoproduction of H2O2 with the reaction rate up to 2,093 μmol/(h·g) and the apparent quantum efficiency of 2.33%. Importantly, a machine learning model was constructed to guide the synthesis of this metal-free photocatalyst. With the help of transient photovoltage (TPV) tests, we optimized their fabrication and catalytic activity, and clearly showed that the formation of carbon dots (CDs) facilitates the generation, separation, and transfer of photo-induced charges on the catalyst surface. This work provides a green way for the highly efficient metal-free photocatalyst design and study from biomass materials with the machine learning and TPV technology.


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Electronic supplementary material
About this article

Highly efficient metal-free catalyst from cellulose for hydrogen peroxide photoproduction instructed by machine learning and transient photovoltage technology

Show Author's information Yan Liu1, Xiao Wang1, Yajie Zhao1, Qingyao Wu1, Haodong Nie1, Honglin Si1, Hui Huang1( ), Yang Liu1( ), Mingwang Shao1( ), Zhenhui Kang1,2( )
Institute of Functional Nano and Soft Materials Laboratory (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou 215123, China
Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China

Abstract

Great attention has been paid to green procedures and technologies for the design of environmental catalytic systems. Biomass-derived catalysts represent one of the greener alternatives for green catalysis. Photocatalytic production of hydrogen peroxide (H2O2) from O2 and H2O is an ideal green way and has attracted widespread attention. Here, we show a metal-free photocatalyst from cellulose, which has a high photocatalytic activity for the photoproduction of H2O2 with the reaction rate up to 2,093 μmol/(h·g) and the apparent quantum efficiency of 2.33%. Importantly, a machine learning model was constructed to guide the synthesis of this metal-free photocatalyst. With the help of transient photovoltage (TPV) tests, we optimized their fabrication and catalytic activity, and clearly showed that the formation of carbon dots (CDs) facilitates the generation, separation, and transfer of photo-induced charges on the catalyst surface. This work provides a green way for the highly efficient metal-free photocatalyst design and study from biomass materials with the machine learning and TPV technology.

Keywords: machine learning, carbon dots, hydrogen peroxide, cellulose, transient photovoltage, metal-free photocatalyst

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Publication history
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Acknowledgements

Publication history

Received: 23 November 2021
Revised: 23 December 2021
Accepted: 27 December 2021
Published: 21 February 2022
Issue date: May 2022

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work is supported by National Key R&D Program of China (Nos. 2020YFA0406104, 2020YFA0406101, and 2020YFA0406103), National MCF Energy R&D Program of China (No. 2018YFE0306105), Innovative Research Group Project of the National Natural Science Foundation of China (No. 51821002), the National Natural Science Foundation of China (Nos. 51725204, 21771132, 51972216, and 52041202), Natural Science Foundation of Jiangsu Province (No. BK20190041), Key-Area Research and Development Program of GuangDong Province (No. 2019B010933001), Collaborative Innovation Center of Suzhou Nano Science & Technology, the 111 Project, and Suzhou Key Laboratory of Functional Nano & Soft Materials.

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