Olefin/paraffin separation in polymer/mof mixed-matrix membranes

Abstract

Membranes, due to their low cost, high energy efficiency and ease of processing, have aroused great interest in the field of gas separation. Polymer membranes currently occupy a dominant position in the commercial market, despite the existing tradeoff between permeability and selectivity associated with their use. Over the past decade a novel class of inorganic-organic porous materials, Metal-Organic Frameworks (MOFs), has emerged as a new research domain in solid state materials. These hybrid nanoporous materials formed by the self-assembly of metal ions or clusters, linked together via a variety of bridging ligands, creating stable open structures with sufficiently large pores for industrially-important applications, such as in gas adsorption, storage and separation. Indeed, a number of recent studies have demonstrated that MOFs could be optimal candidates for membrane-based gas separation processes. In addition, owing to the remarkable properties of MOFs, an alternative strategy to overcome the selectivity/permeability trade-off limits of polymer membranes is to make mixed-matrix membranes (MMMs), in which MOF particles are incorporated into polymer matrices. Typically, the alkane/alkene separation is highly topical since it was identified recently as one of the "7 chemical separation to change the world". Propylene (C3H6) is with ethylene (C2H4), the largest feedstock in petrochemical industries with a global production that exceeds 200 million tons per year, with these chemicals mostly used to produce polymer-grade and plastic products, particularly the widely utilized polypropylene. The objective of the PhD will be to predict the separation performances of a series of MMMs for diverse olefin/paraffin separation based on atomistic models constructed for the corresponding MMMs using a combination of force field and quantum calculations. More specifically, we implement an MC/MD simulation scheme to perform simulations of membrane permeation processes. This prediction will pave the way towards the development of the corresponding MMM and their separation testing by collaborators.