Three Grants For Research Into A More Sustainable, Efficient Chemical Industry

Three projects by researchers at Radboud University have received funding from, among others, NWO and the National Growth Fund project Big Chemistry, to conduct research into a more sustainable and efficient chemical industry.

A total of eight projects received funding in the NGF Big Chemistry grant round. Three of these were awarded to projects for which researchers from Radboud University were the main applicants. In total, nearly €2.8 million in NWO funding was awarded. The eight projects also raised €556,000 in co-funding.

The aim of this call was to accelerate the search for chemicals with the right, desired properties. High-throughput experiments, in which multiple experiments are carried out simultaneously, allow a large amount of data to be collected. This data is analysed using artificial intelligence (AI) to identify patterns. These patterns can be used to predict all kinds of properties of new chemicals, such as taste, smell or solubility. Ultimately, this will be integrated into a fully automated RobotLab.

High-throughput measurements and deep learning of polymer properties
Main applicant: Wilhelm Huck, Institute for Molecules and Materials, Radboud University. Consortium partners: TNO, VLCI.

The solution to problems with polymers. In this project, we will use high-throughput experiments and robots for handling liquids and solids to determine the solubility of 1,000 polymers in more than 25 different solvents and measure the viscosity of polymer solutions at many different concentrations. This dataset will be used to train a deep learning model to predict the properties of polymers in solution. The dataset for polymer properties and the deep learning model will accelerate innovation in formulation science.

High-throughput characterization for improved prediction of surfactant mixture properties
Principal applicant: Wilhelm Huck, Institute for Molecules and Materials, Radboud University. Consortium partner: Croda International

Almost every formulation we use in our daily lives, such as shampoos, paints or cosmetics, contains a mixture of surfactants. However, the interactions between these surfactants are difficult to predict, making it challenging to design new formulations in a targeted manner. In this project, we will implement methods to analyse important formulation properties, such as surface tension, solubility and foaming, at high speed. We will use these methods to collect a dataset from which we can learn the relationship between the composition of the mixture of surfactants and the formulation properties. This dataset and model will thereby accelerate the design of new formulations.

Automated navigation of bio-based building block implementation in complex resin formulations
Principal applicant: Peter Korevaar, Institute for Molecules and Materials, Radboud University. Consortium partners: Koninklijke van Wijhe Verf, WYDO NBD

A major challenge in the formulation of complex paint solutions is the introduction of bio-based polymer binders as replacements for oil-based ingredients. Formulating a new compound in dispersions or emulsions with rheological properties of paint requires navigating highly multidimensional parameter spaces of polymer structures, additives (including dispersants, co-solvents, surfactants), concentrations and preparation protocols. Using polyhydroxyalkanoates as a platform for bio-based paint binders, we will develop high-throughput methodologies and machine learning strategies. These will significantly accelerate the necessary transition to “the paint of the future” and ultimately also help to propose suitable molecular structures as new bio-based paint ingredients.