化学
多面体
结晶学
金属
配体(生物化学)
吸附
水溶液中的金属离子
有机化学
组合数学
受体
数学
生物化学
作者
Xianhui Tang,Chunlong Meng,Nakul Rampal,Aurelia Li,Xu Chen,Wei Gong,Hong Jiang,David Fairen‐Jimenez,Yong Cui,Yan Liu
摘要
Metal-organic polyhedra featuring non-Archimedean/Platonic architectures with multiple kinds of vertices have aroused great attention for their fascinating structures and properties but are yet challenging to achieve. Here, we report a combinatorial strategy to make such nonclassic polyhedral cages by combining kinetically labile metal ions with non-planar organic linkers instead of the usual only inert metal centers and planar ligands. This facilitates the synthesis of an enantiopure twisted tetra(3-pyridyl)-based TADDOL (TADDOL = tetraaryl-1,3-dioxolane-4,5-dimethanol) ligand (L) capable of binding Ni(II) ions to produce a regular convex cage, Ni6L8, with two mixed metal/organic vertices and three rarely reported concave cages Ni14L8, Ni18L12, and Ni24L16 with three or four mixed vertices. Each of the cages has an amphiphilic cavity decorated with chiral dihydroxyl functionalities and packs into a three-dimensional structure. The enantioselective adsorption and separation performances of the cages are strongly dependent on their pore structure features. Particularly, Ni14L8 and Ni18L12 with wide openings can be solid adsorbents for the adsorptive and solid-phase extractive separation of a variety of racemic spirodiols with up to 98% ee, whereas Ni6L8 and Ni24L16 with smaller pore apertures cannot adsorb the racemates. The combination of single-crystal X-ray diffraction analysis of the host-guest adduct and GCMC simulation indicates that the enantiospecific recognition capabilities originate from the well-organized chiral inner sphere as well as multiple interactions within the chiral microenvironment. This work therefore provides an attractive strategy for the rational design of polyhedral cages, showing geometrically fascinating structures with properties different from those of classic assemblies.
科研通智能强力驱动
Strongly Powered by AbleSci AI