Synthetic jet fuel

Aviation accounts for approximately 2.5% of human-made global CO₂ emissions, and the road towards fully electrification of airplane transport seems particularly long. A significant intermediate step towards our aim to avoid fosil fuels would be to use synthetic jet fuels produced from more sustainable sources.

In the Computational Chemistry, we combine quantum chemical simulations with machine learning and generative AI models, such as language models and denoising diffusion models, to help with the design of improved materials and molecules. In this master project, we will focus on improving the chemical process of synthetic jet fuel production, which starts from conversion of CO₂ to methanol, which is then further converted into longer olefin molecules, and finally into synthetic kerosine. In particular, you will further develop, condition, and apply a molecular diffusion model to generate catalyst materials for olefin polymerisation with improved yield and selectivity, using data from quantum chemical calculations.

This research project is part of a collaborative research line with NERA / Maeve Aerospace, a startup company focused on sustainable aviation, and researchers from the TU Delft and TU Twente.

Supervision:

• Bernd Ensing (HIMS)