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

作　　者：Yan Liu Xiao Wang Yajie Zhao Qingyao Wu Haodong Nie Honglin Si Hui Huang Yang Liu Mingwang Shao Zhenhui Kang 

机构地区：[1]Institute of Functional Nano and Soft Materials Laboratory(FUNSOM),Jiangsu Key Laboratory for Carbon-Based Functional Materials&Devices,Soochow University,Suzhou 215123,China [2]Macao Institute of Materials Science and Engineering,Macao University of Science and Technology,Taipa,Macao 999078,China

出　　处：《Nano Research》2022年第5期4000-4007,共8页纳米研究（英文版）

基　　金：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);KeyArea 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.

摘　　要：Great attention has been paid to green procedures and technologies for the design of environmental catalytic systems.Biomassderived catalysts represent one of the greener alternatives for green catalysis.Photocatalytic production of hydrogen peroxide(H_(2)O_(2))from O_(2) and H_(2)O 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 H_(2)O_(2) with the reaction rate up to 2,093μmol/(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.

关 键 词：cellulose carbon dots hydrogen peroxide machine learning transient photovoltage metal-free photocatalyst 

分 类 号：O64[理学—物理化学]