Tuning catalyst reaction environments towards photoreforming of wastewater. This project aims to combine high-throughput computation and machine learning to screen photocatalysts more thoroughly for p

Description

Tuning catalyst reaction environments towards photoreforming of wastewater. 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.. Scheme: Discovery Projects. Field: 4016 - Materials Engineering. Lead: Prof Zongyou Yin