Elucidation of Selectivity Reversals for Binary Mixture Adsorption in Microporous Adsorbents

Area of Science:

• Materials Science
• Physical Chemistry
• Computational Chemistry

Background:

• Adsorption selectivity is crucial for mixture separations using adsorbents.
• Ideal Adsorbed Solution Theory (IAST) is commonly used to estimate selectivity but often fails to predict selectivity reversals.
• Selectivity reversals and adsorption azeotropy have been experimentally observed under varying conditions.

Purpose of the Study:

• To investigate the underlying mechanisms of adsorption selectivity reversals.
• To understand the limitations of IAST in predicting these phenomena.
• To identify scenarios leading to selectivity reversals using advanced simulation techniques.

Main Methods:

• Configurational-bias Monte Carlo (CBMC) simulations were employed.
• Analysis focused on understanding adsorbate distribution and entropy effects within porous materials.
• Simulations explored adsorption in various zeolite structures (mordenite, DDR, CHA, LTA, NaX, MFI).

Main Results:

• Two distinct scenarios for selectivity reversals were identified.
• Scenario 1: Inhomogeneous adsorbate distribution due to preferential guest species location (e.g., CO2 in zeolite pores).
• Scenario 2: Entropy-driven reversals near pore saturation, favoring components with higher packing efficiency (e.g., smaller alkanes, linear isomers).

Conclusions:

• CBMC simulations provide crucial insights into selectivity reversals beyond IAST predictions.
• IAST's assumption of uniform adsorbate competition limits its ability to capture these complex phenomena.
• Understanding these mechanisms is vital for designing effective adsorbents for challenging mixture separations.