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

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.

Automated summary

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.