Tuning catalyst reaction environments towards photoreforming of wastewater

本项目旨在将高通量计算和机器学习相结合，更深入地筛选用于废水光转化的光催化剂。反应环境对表面活性单元的影响将适用于无COX排放的选择性有机合成，在环境条件下由含有机废水生产氢气。该项目希望扩大我们在快速、可靠的筛选策略以及电场(或晶格应变)和反应路径之间的关系方面的知识。该项目将开发一个光转化系统，用于从废水中选择性地联合生产有机物和氢气，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.