Projects

Metal organic framework (MOF) materials represent an exciting new development in porous materials science, with myriad applications in fields as diverse as carbon capture, catalysis, and water harvesting. However, with tens of thousands of MOF syntheses identified, reproducing a reported framework or identifying the best synthesis route is a complex and often frustrating task.

Screening adsorption behaviour of porous materials is highly valuable to select candidates for scale-up and direct novel materials discovery. However, current methods are limited to adsorption of either very small ro simple molecules, unable to describe complex adsorption phenomena like water capture or arene isomer separation.

Mixed matrix membranes (MMMs) are promising materials for membrane gas separations. The idea of an MMM is to combine excellent separation performance of a filler material (common choices include Metal Organic Frameworks, zeolites, and carbon nanotubes/nanosheets) with the good processability and stability of the continuous phase (which is often a polymer material).

Most of the state-of-the-art approaches to computational materials discovery of MOFs via machine learning (ML), can be summarized in terms of three key components. A way to sample the phase space of MOFs (databases, material generators etc.

Pressure Swing Adsorption (PSA) processes are used extensively for many important gas separations. One of the advantages of PSA is routed in it’s cyclic nature: one can configure a variety of ‘cycles’ with different sequences and steps to achieve the best separation performance using a given adsorbent material.

This project will employ a broad range of multi-scale modelling methods, including Grand Canonical Monte Carlo simulations and process simulations, to explore adsorption processes in porous materials. The primary focus will be on the materials exhibiting unusual or advanced effects, such as cation-exchanged zeolites, metal-organic frameworks with open metal sites and flexible metal-organic materials.