Computational design of nanoporous structures for sensor devices

The access to small-scale, easily integrable sensors with high sensitivity and high selectivity are key to the digital revolution. Nanoporous structures, such as metal-organic frameworks (MOFs) and zeolites, are ideal candidates for next-generation sensing due to their high surface-to-volume ratio and research into these materials has accelerated over the last years. Furthermore, these nanoporous structures can be fine-tuned via a large array of possible scaffold or detector molecule chemistries. However, choosing the right ingredients is a significant challenge as the influence of the chemical structure on the sensing properties is not straightforward and subtle changes in the chemistry can have unforeseen side-effects. In this project, a digital workflow will be developed to predict the properties of MOF and zeolite-based sensors to speed up the design process and enable new breakthrough experiments. The workflow will span the full range, from suggesting possible synthesis routes for the new chemical structures to predicting sensor observables. Experiments will validate the workflow, providing essential feedback to iteratively improve the workflow accuracy. On the basis of predictive simulations we will design sensors based on MOFs (gas) and zeolites (liquid). The project will initiate and intensify collaboration between eight groups from five institutes. Due to its extensible design, it will provide a valuable tool for future studies.