ÌÇÐÄVlog

This is an exciting fully funded PhD studentship opportunity exploring the development of an automated electrochemical testing platform for data-driven catalyst discovery. In recent years, electrochemical C-N coupling reactions have emerged as a promising approach beyond water and CO2 electrolysis for sustainable chemical production.

Due to the reaction complexity, conventional trial-and-error catalyst searching strategy fails to push the frontier forward. Accelerating the catalyst discovery calls for high-throughput (HT) experimentation in not only material synthesis and performance evaluation, but also mechanistic study through operando interface characterisation.

Currently, there’s no such platform available. Herein, this project plans to address this gap by developing a HT screening platform coupling electrochemical and in-operando spectroscopic capabilities. We will use the CO₂-nitrate coupling for urea synthesis as the model reaction. Machine learning (ML) algorithms, such as Bayesian optimization with surrogate models, will be used to learn the structure–performance relationships of catalysts and accelerate the discovery of optimal C–N electrocatalysts.

As the PhD candidate in this project, you will acquire research skills in electrocatalysis, in operando spectroscopy, machine learning and automation. Specific activities include:

1. Build the HT screening platform and validate the electrochemistry and in-operando characterisation
2. Validate the platform using Raman and X-ray techniques
3. Develop ML algorithms for rapid data curation, analysis and catalyst optimisation
4. Validate learning, predict and design next-gen C-N coupling catalyst material.

HT screening and data-driven catalyst discovery is directly linked with the Industry 4.0 Evolution of digital transformation. Therefore, this interdisciplinary project has strong connections with  and the UK’s first-of-its-kind self-driving laboratory for energy research  at Imperial College London. Other supports including training opportunities, additional funding for travel, mentorship for personal and professional development are also available to help with career progression.

You will need to meet the minimum entry requirements for our PhD programme Sustainable Energy PhD research course.

Candidates must meet ÌÇÐÄVlog graduate entry requirements which include holding at least an upper second-class degree or equivalent qualifications in a relevant subject area such as physics, chemistry, materials science or chemical engineering. A Master’s degree in a relevant discipline and additional research experience would be an advantage.

We strongly encourage candidates from under-represented groups, including but not limited to Female, Black, Asian, or minoritised ethnic communities (sometimes referred to as BAME). Additional supports can be provided upon request.

Open to any UK or international candidates.