Impregnation of 1-n-Butyl-3-methylimidazolium Dicyanide [BMIM][DCA] into ZIF-8 as a Versatile Sorbent for Efficient and Selective Separation of CO2

Enhanced CO2 Capture through SAPO-34 Impregnated with Ionic Liquid

The concurrent utilization of an adsorbent and absorbent for carbon dioxide (CO2) adsorption with synergistic effects presents a promising technique for CO2 capture. Here, 1-butyl-3-methylimidazole acetate ([Bmim][Ac]), with a high affinity for CO2, and the molecular sieve SAPO-34 were selected. The impregnation method was used to composite the hybrid samples of [Bmim][Ac]/SAPO-34, and the pore structure and surface property of prepared samples were characterized. The quantity and kinetics of the sorbed CO2 for loaded samples were measured using thermogravimetric analysis. The study revealed that SAPO-34 could retain its pristine structure after [Bmim][Ac] loading. The CO2 uptake of the loaded sample was 1.879 mmol g-1 at 303 K and 1 bar, exhibiting a 20.6% rise compared to that of the pristine SAPO-34 recording 1.558 mmol g-1. The CO2 uptake kinetics of the loaded samples were also accelerated, and the apparent mass transfer resistance for CO2 sorption was significantly reduced by 11.2% compared with that of the pure [Bmim][Ac]. The differential scanning calorimetry method revealed that the loaded sample had a lower CO2 desorption heat than that of the pure [Bmim][Ac], and the CO2 desorption heat of the loaded samples was between 30.6 and 40.8 kJ mol-1. The samples exhibited good cyclic stability. This material displays great potential for CO2 capture applications, facilitating the reduction of greenhouse gas emissions.

Composite of MIL-101(Cr) with a Pyrrolidinium-Based Ionic Liquid Providing High CO2 Selectivity

Capturing CO₂ selectively from flue gas and natural gas addresses the criteria of a sustainable society. In this work, we incorporated an ionic liquid (IL) (1-methyl-1-propyl pyrrolidinium dicyanamide, [MPPyr][DCA]) into a metal organic framework (MOF), MIL-101(Cr), by wet impregnation and characterized the resulting [MPPyr][DCA]/MIL-101(Cr) composite in deep detail to identify the interactions between [MPPyr][DCA] molecules and MIL-101(Cr). Consequences of these interactions on the CO₂/N₂, CO₂/CH₄, and CH₄/N₂ separation performance of the composite were examined by volumetric gas adsorption measurements complemented by the density functional theory (DFT) calculations. Results showed that the composite offers remarkably high CO₂/N₂ and CH₄/N₂ selectivities of 19,180 and 1915 at 0.1 bar and 15 °C corresponding to 1144- and 510-times improvements, respectively, as compared to the corresponding selectivities of pristine MIL-101(Cr). At low pressures, these selectivities reached practically infinity, making the composite completely CO₂-selective over CH₄ and N₂. The CO₂/CH₄ selectivity was improved from 4.6 to 11.7 at 15 °C and 0.001 bar, yielding a 2.5-times improvement, attributed to the high affinity of [MPPyr][DCA] toward CO₂, validated by the DFT calculations. These results offer broad opportunities for the design of composites where ILs are incorporated into the pores of MOFs for high performance gas separation applications to address the environmental challenges.

Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation

Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-n-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF₄]) with a large variety of MOFs for CO₂ and N₂ adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF₄]/MOF composites. The most important features that affect the CO₂/N₂ selectivity of composites were extracted from ML and utilized to computationally generate an IL/MOF composite, [BMIM][BF₄]/UiO-66, which was not present in the original material data set. This composite was finally synthesized, characterized, and tested for CO₂/N₂ separation. Experimentally measured CO₂/N₂ selectivity of the [BMIM][BF₄]/UiO-66 composite matched well with the selectivity predicted by the ML model, and it was found to be comparable, if not higher than that of all previously synthesized [BMIM][BF₄]/MOF composites reported in the literature. Our proposed approach of combining molecular simulations with ML models will be highly useful to accurately predict the CO₂/N₂ separation performances of any [BMIM][BF₄]/MOF composite within seconds compared to the extensive time and effort requirements of purely experimental methods.