Study of physical, mechanical and transport properties of polymeric membranes for gas separation

Abstract

The work in this thesis is organised in different main topics. The first part is devoted to present Atomic Force Microscopy (AFM), carried out in force spectroscopy mode, as a powerful alternative to the more commonly used tensile tests for the analysis of the mechanical properties of polymers, and MMMs in particular. AFM force spectroscopy measurements are carried out with nanometric and micrometric tips on dense membranes of neat Pebax®1657 and on mixed matrix membranes of Pebax®1657 with different concentrations of an ionic liquid. This offers good perspectives for the analysis of samples where traditional tensile tests cannot be used, for instance composite membranes or particularly small samples. The second part of the research is focused on the relationship, between the transport properties and Young’s modulus for films of polymers of intrinsic microporosity (PIM) and on the effect of physical aging, investigated using pure gas permeability and atomic force microscopy (AFM) measurements in force spectroscopy mode. In the third part, the transport properties of polymer blend membranes are evaluated. In the last part, using a computational approach, it is possible to predict missing values for permeability starting with a collection of existing permeability values for other polymers. The data are estimated by means of machine learning models that correlate the behaviour of different gases. Thus, this thesis is structured as follows: Chapter 1 and Chapter 2 provide a general introduction on membrane technology and characterization methods used in this thesis, as well as the theoretical background and the description of all experimental techniques used; Chapter 3 describes the mechanical study on MMMs of blends of Pebax® and the ionic liquid ([BMIM][BF4]); Chapter 4 describes mechanical and gas transport studies on PIMs; Chapter 5 presents the gas transport analysis on Matrimid®5218/AO-PIM blend membranes; Chapter 6 discusses the results of the machine learning model. Chapter 7 presents the overall conclusions of the work and gives a brief future outlook of possible and desired developments in the field.