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.
Please note: unless otherwise specified, the internships are only available for students with a nationality of an EU-member state and/or students from a Dutch university.
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.
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.
Evaluating plasmonic heating and hot-charge carrier effects in plasmon-driven syntheses
Our future energy infrastructure will need ways of efficiently converting, transporting and storing electricity from sustainable but fluctuating sources. One approach uses sustainable electricity for the reverse combustion of atmospheric CO2 into so-called solar fuels, thereby converting electricity into the chemical bonds of high-density fuels. DIFFER pursues plasma-assisted conversion of CO2 into CO and O2 as an exciting new approach to recycling carbon dioxide into fuels, thereby closing the carbon cycle and eliminating the need for fossil fuels.
Photo-electrochemical (PEC) solar fuel conversion is one of the most promising techniques to convert solar energy directly into its most versatile form of energy, a fuel. However, the efficiency is still low and degradation too high. We have several open BSc/MSc/internship projects for both experiments and modeling & simulation.