Research team


During my Ph.D. research, I studied the physicochemical properties of neoteric reaction media (deep eutectic solvents) and the behavior of enzymes and enzymatic reactions in them. This included computational simulations using various methods. I studied the stability of enzymes and changes in their structure in different solvents using classical molecular dynamics. The properties of the solvents, such as density, viscosity and solubility were estimated by a combined approach of machine learning and group contribution method. In my postdoctoral projects, I'm focusing on the sustainability and recyclability of polymers and polymer-based products. This includes the calculation of the associated environmental impact through life cycle assessment, but also more fundamental simulations. Currently, I'm exploring the application of a hybrid modeling workflow for the separation and substitution of chemically recycled polyurethane streams. In this work, I combine classical and coarse-grained molecular dynamics simulations with conceptual density functional theory and deep neural networks. Some of the projects I am partially involved in include the analysis of previously collected data sets from various chemistry related projects to gain additional understanding of the given problem and to facilitate qualitative predictions.

A structured methodology for NADES selection and formulation for enzymatic reactions. 01/10/2019 - 30/09/2022


Natural deep eutectic solvents (NADES) show great promise as media for enzymatic reactions in sectors where (bio)compatibility with natural or medical products is a must. Whereas in theory they can be tailored to the envisioned reaction, ensuring optimized yields, to date the knowledge is predominantly empirical, with some mechanistic reports giving a fragmented view at best. Therefore, even merely explaining experimental observations is not straightforward, let alone making predictions. This doctoral study aims at building a structured, holistic understanding of the effect of NADES media on enzymatic reactions, whereby effects on solubility, solvation, viscosity, inhibition and denaturation will be distinguished. The solubility, solvation energy and viscosity will be predicted by first principles and molecular dynamics calculations, serving as input for a group contribution model using machine learning. Experiments will train and validate the model, and learnings from observed reaction kinetics will be further benchmarked against molecular dynamics calculations of enzyme structures and interactions in NADES. Structural changes of the enzyme will be demonstrated using Raman optical activity spectroscopy. The combination of these methods ensures fundamental knowledge acquisition, while the group contribution model is part of a structured methodology. The findings of this project are transferable to other uses of NADES.


Research team(s)

  • Intelligence in PRocesses, Advanced Catalysts and Solvents (iPRACS)

Project type(s)

  • Research Project