• Desmet, Gert (Administrative Promotor)
  • Broeckhoven, Ken (Co-Promotor)
  • Eeltink, Sebastiaan (Co-Promotor)
  • Cabooter, Deirdre (Co-Promotor)
  • Lynen, Frederic (Co-Promotor)
  • Hubert, Philippe (Co-Promotor)
  • Tyteca, Eva (Co-Promotor)
  • Focant, Jean François (Co-Promotor)


The ChIMiC-project aims at creating a paradigm shift in the quality and speed with which the detailed chemical composition of vapors, mixtures, cells and tissues can be measured. Searching for a disease marking molecule in exhaled breath or a toxic waste product in drinking water literally corresponds to finding a needle in a haystack. To detect these molecules, they first need to be (partially) separated from thousands of other components. The better the separation, the more reliable and precise the measurement. Today, the identification problems in environmental and life sciences have become so complex that the separation capacity needed to solve them can no longer be achieved using a single separation mode but requires a multi-dimensional separation space. To resolve this, a concerted effort is proposed, providing both hardware and software solutions to enlarge this separation space as well as to help the chemical analysts make maximal use of it. For the latter end, a powerful decision-supporting software tool will be developed to undo with the current practice based on trial-and-error or analysts' intuition. This new rational and algorithmic approach to chemical measurement design can be expected to boost the development of new drugs, and provide more refined clinical diagnostic tests and safer food. The project consortium consists of 5 well-connected research groups with a strong international recognition in separation science, computer-modelling and microfluidics.
Short titleChIMiC
Effective start/end date1/01/1831/12/21

    Flemish discipline codes

  • Theoretical and computational chemistry not elsewhere classified

    Research areas

  • Chromatography, retention modelling, microfluidics, expert systems, chemomectrics

ID: 36110164