Hierarchically Micro- and Mesoporous Zeolitic Imidazolate Frameworks Through Selective Ligand Removal

A new method to engineer hierarchically porous zeolitic imidazolate frameworks (ZIFs) through selective ligand removal (SeLiRe) is presented. This innovative approach involves crafting mixed-ligand ZIFs (ML-ZIFs) with varying proportions of 2-aminobenzimidazole (NH2-bIm) and 2-methylimidazole (2-mIm), followed by controlled thermal treatments. This process creates a dual-pore system, incorporating both micropores and additional mesopores, suggesting selective cleavage of metal-ligand coordination bonds. Achieving this delicate balance requires adjustment of heating conditions for each mixed-ligand ratio, enabling the targeted removal of NH2-bIm from a variety of ML-ZIFs while preserving their inherent microporous framework. Furthermore, the distribution of the initial thermolabile ligand plays a pivotal role in determining the resulting mesopore architecture. The efficacy of this methodology is aptly demonstrated through the assessment of hierarchically porous ZIFs for their potential in adsorbing diverse organic dyes in aqueous environments. Particularly striking is the performance of the 10%NH2-ZIF-2 h, which showcases an astonishing 40-fold increase in methylene blue adsorption capacity compared to ZIF-8, attributed to larger pore volumes that accelerate the diffusion of dye molecules to adsorption sites. This versatile technique opens new avenues for designing micro/mesoporous ZIFs, particularly suited for liquid media scenarios necessitating efficient active site access and optimal diffusion kinetics, such as purification, catalysis, and sensing. The research focuses on the construction of mixed-ligand and ligand-removal zeolitic imidazolate frameworks ZIF-8, followed by the ligand thermolysis strategy to selectively remove thermolabile amino-ligands in order to design controllable hierarchical pores that provide larger pore volumes and facilitating faster transport of the dye molecules to the adsorption sites.image