Tuning catalyst reaction environments towards photoreforming of wastewater

该项目旨在结合高通量计算和机器学习，更彻底地筛选光催化剂，用于废水的光重整。反应环境对表面活性单元的影响将针对环境条件下含有机废水产氢的无cox排放选择性有机合成进行定制。该项目希望扩大我们对快速，可靠的筛选策略的了解，以及电场（或晶格应变）与反应途径之间的关系。该项目将开发一个光重整系统，用于从废水中选择性地联合生产有机物和氢，有利于化学合成和氢经济的可持续技术发展。

This project aims to combine high-throughput computation and machine learning to screen photocatalysts more thoroughly for photoreforming of wastewater. The reaction environments effects on surface active units will be tailored for COx-emission-free selective organic synthesis with hydrogen production from organic-contained wastewater at ambient conditions. The project expects to expand our knowledge on the fast, reliable screening strategies, and the relationship between electric field (or lattice strain) and reaction pathways. This project will develop a photoreforming system for selective co-production of organics and hydrogen from wastewater, benefiting sustainable technologies development for chemical synthesis and hydrogen economy.