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Modelling non-equilibrium plasmas for CO2 activation is very challenging due to the complex network of chemical reactions and different timescales for the physical and chemical processes involved. An accurate description of electron kinetics is fundamental to calculate chemical rate coefficients and transport parameters that are used to describe the plasma discharge. In the CPPC group, we develop fast and accurate computational approaches for electron kinetics. This MSc project focuses on the application of those approaches to CO2 plasmas investigated experimentally at DIFFER.


The discovery of new energy materials is becoming a large-scale challenge that is far beyond the reach of experimentation but also stretching the limits of conventional computation. At DIFFER; our AMD research group is working on to improve the speed and the prediction capability of computational methods for the discovery of new energy materials. We use machine learning (ML), which is essentially a method to make predictions and to optimize a performance criterion based on the available example data.


The computational MSc thesis project is part of a theory-experiment collaboration effort between DIFFER and our industrial partner. The overall aim is to understand the fundamentals of new Fe-based model catalysts, and to tune them further for the Fischer-Tropsch (FT) synthesis of fuels using renewable energy. The fundamental aim is to know how Fe metal layers grow on different Cu metal substrates and how these newly grown Fe layers behave during the adsorption of atomic and molecular species, such as H, C, O, and CO, which are all needed for the synthesis of commercially valuable fuels.


We expect applications from senior scientists who wish to lead our SF research programme, establish their own research group within this programme, and join the management team of the institute.


In a previous Master Thesis project a neural network regression was performed of the warm plasma Ordinary mode dispersion relation. In order to extend this work to the eXtraordinary mode, first a thorough analysis of the different mode branches of the X-mode is required. In particular, around the second harmonic resonance the warm plasma dispersion is characterized by a complex interplay between the fast X-mode and the Bernstein mode, which needs to be documented before a neural network regression can be attempted.


The 2D reduced MHD code RUTH is used to study the linear and nonlinear evolution of (neoclassical) tearing modes. Within this broad range of topics various thesis projects can be developed ranging from the implementation of more efficient numerical schemes to the implementation of additional physics models and effects such as the ion polarization current, cylindrical coordinates and radial asymmetries and the benchmarking of the effects on the nonlinear growth of the mode against the generalized Rutherford equation.

Technical support

Om de unieke experimenten te kunnen doen is elektronica onmisbaar bij DIFFER. We zijn daarom op zoek naar een


Evaluating plasmonic heating and hot-charge carrier effects in plasmon-driven syntheses

Postdoc position

This position is part of a recently granted NWO project, entitled CO2SPARE, which is aimed at the valorisation of CO2 in biogas. This project is executed jointly with the Non-equilibrium Fuel Conversion (NFC) group of DIFFER. It entails fundamental studies of the reverse Boudouard reaction, in support of an overall process involving pyrolysis of methane follows by reaction of CO2 with the carbon thus formed.