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.