This studentship will develop an automated electrochemical testing platform coupled with machine-learning for C-N coupling catalyst discovery. 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 is no such platform available. Herein, this project plans to address this gap by developing a HT screening platform coupling electrochemical and 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, operando spectroscopy, machine learning and automation.
Specific activities include:
* Build the HT screening platform and validate the electrochemistry and operando characterisation.
* Validate the platform using Raman and X-ray techniques.
* Develop ML algorithms for rapid data curation, analysis and catalyst optimisation
* 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 National Physical Laboratory and the UK’s first-of-its-kind self-driving laboratory for energy research DIGIBAT 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.