Mechanistic study of CO₂ hydrogenation reactions using microkinetic modelling

The catalytic hydrogenation of CO₂ mediated by amines allows combining the selective capture of CO₂ with its conversion to methanol (see Figure). In this process, amines increase the concentration of CO₂ and facilitate the hydrogenation of formic acid by condensation into amides. The best catalysts for this reaction are bi-functional ruthenium catalysts (A). Most suitable catalysts based on non-precious metals, such as B-D, suffer from catalyst decomposition processes that hamper their activity. 

In this project, density functional theory (DFT) methods will be used to study the mechanism of CO₂ hydrogenation reactions, including catalyst decomposition processes, which are barely known. The mechanistic study will involve the application of automated reaction pathway search methods such as AFIR. Microkinetic models will be used to identify the critical reaction barriers and thermodynamics. Machine learning models will be built to find the base metal, ligands, and amine combinations yielding optimal catalytic performance. The implementation of the designed systems will be facilitated by the collaboration with experimental groups working within the NordCO2 consortium and the ITN-CO2PERATE. This project will include a collaboration with the ICIQ Research Institute (Spain) and the group of Prof. Maseras who has extensive experience in the modeling of homogeneous catalysis.

Requirements

• MSc in materials science, chemistry, or physics, preferably in computational chemistry, is required.
• Candidates with documented experience in density functional theory calculations applied to catalysts and catalysis will be prioritized.
• Experience with machine learning and microkinetics is beneficial.

Supervisors

Senior researcher Ainara Nova

Professor Mats Tilset

Call 2: Project start autumn 2022

This project is in call 2, starting autumn 2022.