Data-Driven design of Bio-inspired Photocatalysts

The goal of this project is to design efficient, non-toxic and sustainable photocatalysts for CO2 reduction, taking inspiration from natural systems, such as leaves. This would be carried out using an inverse design approach whereby computational modelling is utilised to design a material with a desired set of properties in-silico prior to synthesis. This narrows the area of chemical space needing to be explored experimentally and thus streamlines the process of material discovery significantly compared to more conventional methods. This project aims to tackle two problems simultaneously, that of CO2 in the atmosphere and that of disinfection of communal spaces. CO2 levels were 409.8 0.1 ppm in 2019 with the annual rate of concentration increase over the last 60 years about 100 times faster than previously observed naturally. Rising CO2 levels are contributing significantly towards global warming, ocean acidification and climate change. As such, methods for carbon capture and CO2 conversion have garnered significant interest in recent years. Likewise, in the wake of the COVID-19 pandemic increasing focus is being placed on the sanitisation of communal spaces to prevent the spread of pathogens. This project aims to address both issues by designing nature-inspired photocatalysts for the reduction of CO2 to ethanol, an effective disinfectant.