@article{Liu2022, 
author = {Yan Liu and Xiao Wang and Yajie Zhao and Qingyao Wu and Haodong Nie and Honglin Si and Hui Huang and Yang Liu and Mingwang Shao and Zhenhui Kang},
title = {Highly efficient metal-free catalyst from cellulose for hydrogen peroxide photoproduction instructed by machine learning and transient photovoltage technology},
year = {2022},
journal = {Nano Research},
volume = {15},
number = {5},
pages = {4000-4007},
keywords = {machine learning, carbon dots, hydrogen peroxide, cellulose, transient photovoltage, metal-free photocatalyst},
url = {https://www.sciopen.com/article/10.1007/s12274-022-4111-2},
doi = {10.1007/s12274-022-4111-2},
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.}
}