Login / Signup

Morphological Study before and after Thermal Treatment of Polymer-Polymer Mixed-Matrix Membranes for Gas Separations.

Pedro PradanosCenit SotoFrancisco Javier CarmonaÁngel E LozanoAntonio HernándezLaura Palacio
Published in: Polymers (2024)
A good integration of the polymer materials that form a mixed-matrix membrane (MMM) for gas separation is essential to reaching interesting permselective properties. In this work, a porous polymer network (PPN), obtained by combining triptycene and trifluoroacetophenone, has been used as a filler, which was blended with two o-hydroxypolyamides (HPAs) that act as polymer matrices. These polymer matrices have been thermally treated to induce a thermal rearrangement (TR) of the HPAs to polybenzoxazoles (β-TR-PBOs) through a solid-state reaction. For its structural study, various techniques have been proposed that allow us to undertake a morphological investigation into the integration of these materials. To access the internal structure of the MMMs, three different methods were used: a polishing process for the material surface, the partial dissolution of the polymer matrix, or argon plasma etching. The argon plasma technique has not only revealed its potential to visualize the internal structure of these materials; it has also been proven to allow for the transformation of their permselective properties. Force modulation and phase contrast in lift-mode techniques, along with the topographic images obtained via the tapping mode using a scanning probe microscope (SPM), have allowed us to study the distribution of the filler particles and the interaction of the polymer and the filler. The morphological information obtained via SPM, along with that of other more commonly used techniques (SEM, TGA, DSC, FTIR, WASX, gas adsorption, and permeability measurements), has allowed us to postulate the most probable structural configuration in this type of system.
Keyphrases
  • hyaluronic acid
  • solid state
  • magnetic resonance
  • magnetic resonance imaging
  • mass spectrometry
  • machine learning
  • combination therapy
  • ionic liquid
  • convolutional neural network
  • network analysis
  • fluorescent probe