Sustainable separation processes involving solid selective materials, e.g. polymeric and nanostructured membranes, allow to consume less energy than solvent-based processes requiring thermal regeneration, or cryogenic ones.
The separation performance of separation materials can be evaluated with different simulation strategies, from the innovative macroscopic equations of state used for polymeric phases (e.g. SAFT and related ones) to atomistic methods (Molecular Dynamics and Montecarlo). Intermediate scale models are also available such as coarse grained and mesoscale techniques as well as computational fluid dynamics tools.
Multiple scale approaches can combine the computational efficiency of macroscopic methods with the accuracy and predictive power of atomistic ones, especially when complex materials such as those including crystalline structures and nanofillers with enhanced selective properties are concerned (e.g. graphene, MOFs etc.).
The separation of interest in this project is mainly CO2 capture and natural gas/biogas purification, but other processes such as water purification are not excluded a priori.
The modeling strategies developed within the project will be validated against experimental data.
This studentship will build a modeling platform which will allow to optimize the choice of materials and operative conditions for the separation, allowing to minimize the cost and maximize the lifetime of materials.
Eligibility:
To undertake this research, we are seeking a motivated candidate with an Honours degree at 2:1 or above (or international equivalent) in any of these areas, chemical engineering, chemistry, materials science, physics or a related discipline, possibly supported by an MSc degree.
Strong numerical modelling and coding (e.g. MATLAB) skills are essential; molecular simulation skills (e.g. LAMMPS or GROMACS) are important for this PhD project.
Further information:
Google scholar profile
https://scholar.google.com/citations?user=LquAwJ8AAAAJ&hl=it
Research Gate profile
https://www.researchgate.net/profile/Maria_Grazia_De_Angelis
Maria Grazia De Angelis School webpage
https://www.eng.ed.ac.uk/about/people/prof-maria-grazia-de-angelis
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