EPSRC DTP Research Studentship: Developing a new electro-photosynthesis system for CO2 fixation

In the era of global warming the pursuit of sustainable, low or zero-carbon energy solutions has emerged as a pressing imperative. Yet, the realization of a truly sustainable future hinges not only on the successful decarbonization of energy production, but also on the simultaneous reduction of atmospheric CO2 levels and provision of net-zero or net-negative chemical production. These imperatives direct us towards electrosynthesis as a transformative technology, addressing the symbiotic challenges of decarbonization of production and atmospheric carbon removal, using solely clean electrical energy and/or sunlight. Microbial Electrosynthesis is a process by which bacteria convert low energy substrates into high energy products using microbes as a catalyst (reaction centre). When used for CO2 capture these systems have key inputs of: CO2, Water, and electricity (supplied by a solar panel) with potentially limitless potential outputs. By engineering a light harnessing pathway into the bacteria, we have already shown that we can increase cell metabolism, further driving the efficiency of an artificial photosynthetic system driven by microbial electrosynthesis powered by a solar panel.

This project primarily aims to answer the question of whether, "Microbial Electrochemical based Artificial Photosynthetic Systems" (MEBAPS) can be a scalable solution to the problem of Carbon capture and utilisation. We intend to do this by meeting the following key aims:
1. Assess the impact of key operational factors on system performance (e.g. Flow rates, Reactor Design parameters)
2. Develop a platform for rapid testing of Bio-Electrochemical Systems
3. Developed detailed engineering models to predict system bottlenecks and validate models empirically.
4. Use a combined experimental-modelling approach to rapidly assess solutions to system bottlenecks.
5. Apply computational methods (Quantum Modelling/Metabolic Modelling/Molecular Modelling/ML) to assess methods to enhance...