PhD Studentship: Mechanically Robust and Transport-optimized Thin-film Composite Mixed-matrix Membranes

This 3.5-year PhD project is fully funded; students who are eligible to pay tuition fees at the Home rate are eligible to apply (more details can be found here). The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026. 

We recommend that you apply early as the advert may be removed before the deadline.

This project is a collaborative initiative between SLB and the University of Manchester, aimed at developing a data-driven digital twin and predictive design framework for thin-film composite mixed-matrix membranes (TFC-MMMs). The primary objective is to design TFC-MMMs capable of efficient high-pressure gas separation for carbon capture applications.

Targeting industrial-scale deployment, the project addresses key barriers that currently limit the implementation of membrane-based gas separation technologies, including the permeability–selectivity trade-off, pressure-induced compaction, and the associated degradation of mechanical integrity and transport performance under realistic operating conditions.

The research combines advanced multiscale mechanical–transport modelling with experimental validation to quantify and predict the structural evolution of both the selective skin layer and the polymeric support layer under operating pressures of up to 50 bar during gas separation processes (e.g., CO₂/CH₄ separation). Under high-pressure conditions, skin layers may develop defects and microcracks, while polymeric supports undergo compaction, leading to changes in permeability, porosity, and long-term membrane stability.

We will investigate how nanoscale morphology, filler–polymer interfacial chemistry, and structural heterogeneity govern transport dynamics and degradation mechanisms during extended operation. A coupled mechanical–transport framework, accelerated through machine-learning surrogate models trained on multiscale simulation and experimental datasets, will establish a predictive digital twin that links pressure-driven gas flow with deformation-induced evolution of pore structure and membrane morphology.

By capturing this two-way coupling, the framework will enable accurate prediction of permeance decline, selectivity shifts, defect formation, and long-term mechanical stability. The model will be validated through advanced experimental studies conducted within the Department of Chemical Engineering at the University of Manchester and at the Diamond Light Source.

In addition, the successful applicant will undertake an internship at SLB (formerly Schlumberger), providing access to industrially relevant experimental data and practical insights that will directly support the project.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline such as chemical or mechanical engineering. The expertise in computer programming specially for solving partial differential equations is desired.

To apply please contact the main supervisor Masoud Babaei. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.