Projektbeschreibung
Hochleistungsmembranen aus zeolithischen Imidazolatgerüsten
Da CO2-Emissionen im Begriff sind, die Umwelt zu zerstören, zählen kostengünstige Möglichkeiten, die Verbindung von verwandten Gasgemischen zu trennen, zu den größten umweltbezogenen Herausforderungen des Jahrhunderts. Eine Alternative zu den bestehenden Verfahren ist die Trennung mithilfe von Membranen. Das EU-finanzierte Projekt SmartDeZIgn stellt eine neuartige Methode zur Gestaltung von zeolithischen Imidazolatgerüsten für CO2-selektive Membranen vor. Die Fachleute haben das Ziel, ein computergestütztes Instrument zu entwickeln, das auf maschinelles Lernen zurückgreift und alle geeigneten Metalle und Verknüpfer in Kombination mit den Hunderten für zeolithische Imidazolatgerüste verfügbaren Topologien überprüft.
Ziel
With CO2 emissions being an eminent threat of unprecedent global impact, cheap ways to separate it from related gas mixtures are regarded as one of the biggest environmental challenges of our century. One alternative to the current methods is membrane-based separations. However, with today’s available materials, membranes are trapped in an upper boundary permeability-selectivity performance, below the target values of industry related applications. Zeolitic-imidazolate frameworks (ZIFs) can lead to the development of membranes with high performance due to their functionalization that alters their separation performance. They haven’t achieved the status of game changer materials, though, due to limited knowledge of the structural modification-separation performance correlation. Although there are indications that replacement of the organic linker or the metal in ZIFs, affects considerably the diffusivity and separation of gases, no systematic investigation has been carried towards this direction.
I propose a novel method for the design of ZIFs of unprecedented selectivity for CO2 urgent separations: H2/CO2, CO2/N2 and CO2/CH4. The design will be based on the substitution of the organic linker and/or the metal centers of ZIFs. I will develop a computational tool based on machine learning methods which will screen all the suitable metals/linkers in combination with the hundreds of available ZIF topologies. The algorithm’s goal will be to find the missing correlation between these replacements and their impact on the separation efficiency of ZIFs. To achieve this, and contrary to the current screening machine learning-based methods, which focus solely on “static” host-guest interactions (sorption), my algorithm will take into account also the diffusivity (the governing mechanism in membrane-based separations), by adopting realistic structural flexibility response. This will facilitate the design of the optimum material for the three separations.
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MSCA-IF-EF-RI - RI – Reintegration panelKoordinator
15341 Agia Paraskevi
Griechenland