Towards complete elucidation of structural factors controlling thermal stability of IL/MOF composites: effects of ligand functionalization on MOFs

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.

Advancements in visible light responsive MOF composites for photocatalytic decontamination of textile wastewater: A review

Heterogeneous photocatalysis using metal-organic frameworks (MOFs) is expected to provide a pivotal solution for the remediation of toxic dyes and heavy metals from textile wastewater. However, MOFs often suffer from a low removal efficiency, due to the rapid recombination between holes and electrons, generated upon photoexcitation. Additionally, the MOFs exhibit poor water stability, which restricts their large-scale application. In this regard, various approaches (i.e. doping of metal nanoparticle, semiconductor, quantum dot, and ligand functionalization) have been adopted for the formation of multifunctional composites. The MOF-composites possess suitable photochemical, surface, optical, and electronic properties, resulting in enhanced water stability, visible light absorption, and reduced recombination between photogenerated species. This comprehensive review targets to provide an insight into the synthesis and subsequent application of various MOF composites for photocatalytic removal of organic contaminants (dyes) and inorganic (Cr(VI)) contaminants from water. MOFs/graphene oxide composites possess improved surface area and reusability whereas noble metal incorporated MOFs composites suffer from photocorrosion and are relatively costly. Zr and Ti based MOFs exhibit tuning from UV to visible light response and surpass the poor water stability upon binary/ternary composite formation. The role of the dopants in enhancing the efficiency of the composites; the effect of influencing factors such as solution pH, pollutant concentration; the mechanism, and the kinetics of reactions have been outlined. In spite of many advancements, the article also summarizes some roadblocks that need to be unraveled to achieve the energy-water-environment nexus and scope for future breakthrough research in this field.