New developments in liquid chromatography for the analysis of complex samples.
The research described in this proposal aims at improving the analysis of complex samples in diverse fields of applications (pharmaceutical, (bio)medical, environmental…) by investigating and improving the three parameters that determine the resolution of a chromatographic separation: efficiency, selectivity and retention. New methodologies are developed to improve the accuracy with which the different contributions to mass transfer can be measured to obtain improved models for band broadening. These models can ultimately be used to design improved column architectures for the separation of both small and large (bio)molecules. Innovative hardware components are designed to combine orthogonal separation mechanisms into so-called multi-dimensional analyses, allowing to increase the selectivity of the separation substantially. Finally, a machine learning approach is employed to improve and automate the decision-making process involved in the optimization of separations, both with and without knowledge of the compounds’ identity.