期刊
NANO RESEARCH
卷 15, 期 5, 页码 4000-4007出版社
TSINGHUA UNIV PRESS
DOI: 10.1007/s12274-022-4111-2
关键词
cellulose; carbon dots; hydrogen peroxide; machine learning; transient photovoltage; metal-free photocatalyst
类别
资金
- National Key R&D Program of China [2020YFA0406104, 2020YFA0406101, 2020YFA0406103]
- National MCF Energy R&D Program of China [2018YFE0306105]
- Innovative Research Group Project of the National Natural Science Foundation of China [51821002]
- National Natural Science Foundation of China [51725204, 21771132, 51972216, 52041202]
- Natural Science Foundation of Jiangsu Province [BK20190041]
- KeyArea Research and Development Program of GuangDong Province [2019B010933001]
- Collaborative Innovation Center of Suzhou Nano Science Technology
- 111 Project
- Suzhou Key Laboratory of Functional Nano Soft Materials
This study focuses on the development of a metal-free photocatalyst derived from biomass materials, which demonstrates high efficiency in the photocatalytic production of hydrogen peroxide. By utilizing machine learning and transient photovoltage tests, the synthesis and optimization of the photocatalyst were guided, presenting a novel approach for green photocatalyst design.
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 O-2 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 mu mol/(h center dot 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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