Project description
High-performing ZIF membranes
With CO2 emissions destroying the environment, cheap ways to separate the compound 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. The EU-funded SmartDeZIgn project provides a novel method for the design of zeolitic imidazolate frameworks (ZIFs) for CO2 selective membranes. Experts aim to develop a computational tool based on machine learning methods that will screen all the suitable metals and linkers in combination with the hundreds of available ZIF topologies.
Objective
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.
Fields of science
Programme(s)
Funding Scheme
MSCA-IF-EF-RI - RI – Reintegration panelCoordinator
15341 Agia Paraskevi
Greece