RESUMEN
Global warming associated with CO2 emission has led to frequent extreme weather events in recent years. Carbon capture using porous solid adsorbents is promising for addressing the greenhouse effect. Herein, we report a series of robust metal-organic cages (MOCs) featuring various functional groups, such as methyl and amine groups, for CO2/N2 separation. Significantly, the amine-group-functionalized MOC-QW-3-NH2 displays the best selective CO2 adsorption performance, as confirmed by single-component adsorption and transient breakthrough experiments. The distinct CO2 adsorption mechanism has been well studied via theoretical calculations, confirming that the amine groups play a vital role for efficiently selective CO2 adsorption resulting from hierarchical adsorbate-framework interaction.
RESUMEN
Pore engineering plays a significant role in the applications of porous materials, especially in the area of separation and catalysis. Here, we demonstrated a metal-organic framework (MOF) solid solution (MOSS) strategy to homogeneously and controllably mix NU-1000 and NU-901 structures inside single MOF nanocrystals. The key for the homogeneous mixing and forming of MOSS was the bidentate modulator, which was designed to have a slightly longer distance between two carboxylate groups than the original tetratopic ligand. All of the MOSS nanocrystals showed a uniform pore size distribution with a well-tuned ratio of mesopores to micropores. Because of the appropriate pore ratio, MOSS nanocrystals can balance the thermodynamic interactions and kinetic diffusion of the substrates, thus showing exceedingly higher separation abilities and a unique elution sequence. Our work proposes a rational strategy to design mixed-porous MOFs with controlled pore ratios and provides a new direction to design homogeneously mixed MOFs with a high separation ability and unique separation selectivity.
RESUMEN
Adsorptive separation based on porous solid adsorbents has emerged as an excellent effective alternative to energy-intensive conventional separation methods in a low energy cost and high working capacity manner. However, there are few stable mesoporous metal-organic frameworks (MOFs) for efficient purification of methane from other light hydrocarbons in natural gas. Herein, we report a series of stable mesoporous MOFs, MIL-101-Cr/Fe/Fe-NH2, for efficient separation of CH4 and C3H8 from a ternary mixture CH4/C2H6/C3H8. Experimental results show that all three MOFs possess excellent thermal, acid/basic, and hydrothermal stability. Single-component adsorption suggested that they have high C3H8 adsorption capacity and commendable selectivity for C3H8 and C2H6 over CH4. Transient breakthrough experiments further certified the ability of direct separation of CH4 from simulated natural gas and indirect recovery of C3H8 from the packing column. Theoretical calculations illustrated that the van der Waals force proportional to the molecular weight is the key factor and that the structural integrity and defect can impact separation performances.
RESUMEN
The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal-organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was performed by combining grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) technique for the first time. From the comparison of eight classical ML models, it is found that the XGBoost model with seven structural descriptors has superior accuracy in predicting the adsorption and separation performance of MOFs to Xe/Kr. Compared with energetic or electronic descriptors, structural descriptors are easier to obtain. Note that the determination coefficients R 2 of the generalized model for the Xe adsorption and Xe/Kr selectivity are very close to 1, at 0.951 and 0.973, respectively. In addition, 888 and 896 MOFs have been successfully predicted by the XGBoost model among the top 1000 MOFs in adsorption capacity and selectivity by GCMC simulation, respectively. According to the feature engineering of the XGBoost model, it is shown that the density (ρ), porosity (Ï), pore volume (Vol), and pore limiting diameter (PLD) of MOFs are the key features that affect the Xe/Kr adsorption property. To test the generalization ability of the XGBoost model, we also tried to screen MOF adsorbents on the CO2/CH4 mixture, it is found that the prediction performance of XGBoost is also much better than that of the traditional machine learning models although with the unbalanced data. Note that the dimension of features of MOFs is low while the quantity of MOF samples in database is very large, which is suitable for the prediction by model such as XGBoost to search the global minimum of cost function rather than the model involving feature creation. The present study represents the first report using the XGBoost algorithm to discover the MOF adsorbates.