Associate Professor - Membrane Science & Technology
Norwegian University of Life Sciences (NMBU)
Ås, Norway
Organisation from Academic Sector
I lead Membrane Laboratory www.memlab.no at the Faculty of Science and Technology, where we focus on interface and colloid science, separation process engineering, and data science to secure clean water and sustainable critical raw materials.
The Membrane Laboratory www.memlab.no develops knowledge and technologies for more selective, energy- and resource-efficient separations.
Our core interest is how membrane structure, interfacial properties, and solution chemistry control molecular and ionic transport. We examine pore size, surface charge, electrostatic interactions, concentration polarisation, and nanoscale confinement to understand separation performance and guide material and process design. This approach helps us address the permeability–selectivity trade-off and improve performance in complex, multicomponent solutions.
Expertise we can bring:
Membrane separation: nanofiltration, reverse osmosis, diafiltration, and transport characterisation, including quantitative correction for concentration polarisation.
Electromembrane and electrochemical processes: selective ion transport, electroosmosis, ion-exchange systems, electrically polarised interfaces, and electrochemical oxidation.
Hybrid processes: integration of membranes with electrochemical or adsorptive mechanisms for selective separation, contaminant concentration, and treatment.
Characterisation and experimentation: membrane permeability and rejection measurements, porometry, surface and particle electrokinetics, interfacial characterisation, and electrochemical testing.
Modelling and data science: mechanistic transport models, numerical and multiphysics simulations, statistical experimental design, chemometrics, and machine learning.
We combine experiments with physical models and data analysis to identify governing mechanisms and quantify relationships between structure, properties, and performance of membranes. Our aim is to make predictions that support design decisions and remain interpretable across operating conditions.