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Nanosized Zeolite P for Enhanced CO 2 Adsorption Kinetics.

Jaouad Al AtrachAbdelhafid AitblalAbdallah AmedlousYing XiongMarie DesmursValérie RuauxRémy Guillet-NicolasValentin Valtchev
Published in: ACS applied materials & interfaces (2024)
Downsizing zeolite crystals is a rational solution to address the challenge of slow adsorption rates for industrial applications. In this work, we report an environmentally friendly seed-assisted method for synthesizing nanoscale zeolite P, which has been shown to be promising for binary separations. The potassium-exchanged form of nanoagglomerates demonstrates dramatically enhanced CO 2 adsorption capacity, improved diffusion rate, and separation performance. Single-component CO 2 adsorption at equilibrium demonstrated higher CO 2 uptake and faster adsorption kinetics (ca. 1400 s vs >130000 s) for nanosized zeolite (KP1) compared to its micron-sized (KP2) counterpart. The diffusion kinetics analysis revealed the relation between the crystal size and the transport mechanism. The micron-sized KP2 sample was primarily governed by a surface barrier resistance mechanism, while in KP1, the diffusion process involved both intracrystalline and surface barrier resistance, facilitating the surface diffusion process and enhancing the overall diffusion rate. Breakthrough curve analysis confirmed these findings as fast and efficient CO 2 /N 2 and CO 2 /CH 4 separations recorded for the nanosized sample. The results showed remarkably enhanced breakthrough time for KP2 vs KP1 in CO 2 /N 2 (1.0 vs 10.9 min) and CO 2 /CH 4 (1.1 vs 9.9 min) mixtures, along with much higher adsorption capacity for CO 2 /N 2 (0.18 vs 1.33 mmol/g) and CO 2 /CH 4 (0.18 vs 1.21 mmol/g) mixtures. The set of experimental data demonstrates the importance of zeolite crystal engineering for improving the gas separation performance of processes involving CO 2 , N 2 , and CH 4 .
Keyphrases
  • aqueous solution
  • room temperature
  • ionic liquid
  • heavy metals
  • machine learning
  • liquid chromatography
  • molecular dynamics
  • wastewater treatment
  • deep learning
  • solid state