Porosity is an intrinsic property of many formulated products. The internal network of void spaces is a key enabler for the functioning of process and environmental control catalysts, gas sorbents, batteries, and electro-catalysts such as fuel cells, allowing the penetration of reactants, solvents and electrolytes. On the other hand, large void spaces reduce the amount of active material which can be added and reduce physical strength. The structure of the void space is also critical, with bottlenecks preventing effective use of valuable or scarce active material, or introducing performance limitations and energy wastage.
Despite this motivation, traditional, theoretically-based attempts to model and predict porous media performance have proved disappointing. This project will explore instead using data-driven approaches, such as multivariate statistical analysis and machine learning, to characterise and optimise porous structures.
The successful candidate will be based at Johnson Matthey, where this research can have maximum impact in generating more resource-efficient catalysts, higher performance fuel cells and batteries, and novel CO2 abatement processes and electrochemical synthesis routes.
In addition to the knowledge that we are working to make the world a cleaner, greener place, the student will gain experience in data analysis and interpretation, model development, discrimination and criticism for both theoretical and data-driven modelling approaches, and continued development as a chemical engineer.
To be eligible for EPSRC funding candidates must have at least a 2(1) in an Engineering or Scientific discipline or a 2(2) plus MSc and be a UK national. Please email your c.v. to [Email Address Removed]. For details on the Engineering Doctorate scheme visit the homepage. Deadline 31 May 2021
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