Login / Signup

Changes in Porous Parameters of the Ion Exchanged X Zeolite and Their Effect on CO 2 Adsorption.

Andżelika Gęsikiewicz-PuchalskaMichal ZgrzebnickiBeata MichalkiewiczAgnieszka KałamagaUrszula NarkiewiczAntoni W MorawskiRafal Wrobel
Published in: Molecules (Basel, Switzerland) (2021)
Zeolite 13X (NaX) was modified through ion-exchange with alkali and alkaline earth metal cations. The degree of ion exchange was thoroughly characterized with ICP, EDS and XRF methods. The new method of EDS data evaluation for zeolites was presented. It delivers the same reliable results as more complicated, expensive, time consuming and hazardous ICP approach. The highest adsorption capacities at 273 K and 0.95 bar were achieved for materials containing the alkali metals in the following order K < Na < Li, respectively, 4.54, 5.55 and 5.94 mmol/g. It was found that it is associated with the porous parameters of the ion-exchanged samples. The Li 0.61 Na 0.39 X form of zeolite exhibited the highest specific surface area of 624 m 2 /g and micropore volume of 0.35 cm 3 /g compared to sodium form 569 m 2 /g and 0.30 cm 3 /g, respectively. The increase of CO 2 uptake is not related with deterioration of CO 2 selectivity. At room temperature, the CO 2 vs. N 2 selectivity remains at a very high stable level prior and after ion exchange in co-adsorption process (X CO2 during adsorption 0.15; X CO2 during desorption 0.95) within measurement uncertainty. Additionally, the Li 0.61 Na 0.39 X sample was proven to be stable in the aging adsorption-desorption tests (200 sorption-desorption cycles; circa 11 days of continuous process) exhibiting the CO 2 uptake decrease of about 6%. The exchange with alkaline earth metals (Mg, Ca) led to a significant decrease of SSA and micropore volume which correlated with lower CO 2 adsorption capacities. Interestingly, the divalent cations cause formation of mesopores, due to the relaxation of lattice strains.
Keyphrases
  • aqueous solution
  • room temperature
  • ionic liquid
  • escherichia coli
  • climate change
  • deep learning
  • artificial intelligence