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Supramolecular Polymer Network Membranes with Molecular-Sieving Nanocavities for Efficient Pre-Combustion CO 2 Capture.

Ji WuTai-Shung Chung
Published in: Small methods (2021)
Pre-combustion membrane CO 2 capture from syngas before utilizing the clean hydrogen fuel, demands very challenging membrane materials with simultaneous high thermal resistance, precise subnanometer size-selectivity, and robust processability. Here, an unconventional yet ultra-facile nanocomposite membrane design using 4-sulfocalix[4]arene (SCA4) molecules, a highly interactive member of soluble organic macrocyclic cavitands (OMCs) with a precise ≈3.0Å open cavity, is reported, to effectively sieve CO 2 (3.3Å) from H 2 (2.89Å). By simply infiltrating dissolved SCA4 molecules into prefabricated polymer membranes, they form extensive 3D supramolecular polymer networks (SPNs) with the polymer backbones through multi-site ionic interactions. Bearing distinctly molecular-sieving nanocavities, these otherwise amorphous SPN membranes deliver drastically enhanced mixed-gas H 2 /CO 2 separation under an industrial high-temperature-and-pressure environment with 4.35 times higher selectivity being achieved, allowing them to well outperform most existing polymer-based materials and even rival many state-of-the-art but delicate inorganic and framework-based membranes. They also demonstrate enhanced mechanical properties and long-term operation stability. Most attractively, the SPN membranes obtain a molecularly homogeneous, single-phase composite structure that can significantly surpass conventional phase-segregated mixed-matrix membranes in processability. Accompanied by the widely tunable OMC structures, this work can provide a versatile toolbox for designing advanced molecular-sieving membranes with an optimal balance of performance, robust properties, and scalability.
Keyphrases
  • water soluble
  • high temperature
  • heavy metals
  • minimally invasive
  • particulate matter
  • wastewater treatment
  • sewage sludge
  • liquid chromatography
  • tandem mass spectrometry
  • network analysis