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Updated: Jan 21, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
Published on: September 5, 2014
Acetylene Storage and Separation Using Metal-Organic Frameworks with Open Metal Sites
A Luna-Triguero1, J M Vicent-Luna1, R M Madero-Castro1
1Department of Physical, Chemical and Natural Systems , Universidad Pablo de Olavide , Ctra. Utrera Km. 1 , ES-41013 Seville , Spain.
Abstract:
Efficient separation and storage of gas streams involving light hydrocarbons is essential for industrial applications. These hydrocarbons are widely used as energy resources and/or chemical raw materials in various chemical reactions. Here, we focus on the separation of acetylene from methane and carbon dioxide. The separation of acetylene from carbon dioxide is, in particular, challenging due to the similar kinetic diameters and boiling points of the molecules. In recent years, considerable progress has been made in adsorption-based separations using porous metal-organic frameworks (MOFs). Most reported studies are experimental. We present a computational study on these gas separations using a variety of MOFs. This allows investigation of the competitive gas adsorption, which is experimentally challenging, as well as understanding the adsorption mechanisms at the molecular level, which in turn allows further experimental MOF design for this application. MOFs with open metal sites, and particularly Fe-MOF-74, seem to be good for this separation, with a trade-off between physical adsorption capacity and selectivity. Based on experimental single-adsorption isotherms at various temperatures, we developed and validated a specific parameterization to account for the interactions of the olefin with the open metal sites. In addition to volumetric and calorimetric adsorption, we comprehensively investigate the characteristics of the interaction between the MOFs and the guest molecules in terms of binding sites and density profiles. The overall agreement of our simulated results with experimental data for pure components points to the reliability of the models and methods to successfully predict the separation of mixtures.
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