Probing Molecular Motions in Metal-Organic Frameworks by Three-Dimensional Electron Diffraction.

Journal of the American Chemical Society(2021)

引用 11|浏览12
暂无评分
摘要
Flexible metal-organic frameworks (MOFs) are known for their vast functional diversities and variable pore architectures. Dynamic motions or perturbations are among the highly desired flexibilities, which are key to guest diffusion processes. Therefore, probing such motions, especially at an atomic level, is crucial for revealing the unique properties and identifying the applications of MOFs. Nuclear magnetic resonance (NMR) and single-crystal X-ray diffraction (SCXRD) are the most important techniques to characterize molecular motions but require pure samples or large single crystals (>5 × 5 × 5 μm3), which are often inaccessible for MOF synthesis. Recent developments of three-dimensional electron diffraction (3D ED) have pushed the limits of single-crystal structural analysis. Accurate atomic information can be obtained by 3D ED from nanometer- and submicrometer-sized crystals and samples containing multiple phases. Here, we report the study of molecular motions by using the 3D ED method in MIL-140C and UiO-67, which are obtained as nanosized crystals coexisting in a mixture. In addition to an ab initio determination of their framework structures, we discovered that motions of the linker molecules could be revealed by observing the thermal ellipsoid models and analyzing the atomic anisotropic displacement parameters (ADPs) at room temperature (298 K) and cryogenic temperature (98 K). Interestingly, despite the same type of linker molecule occupying two symmetry-independent positions in MIL-140C, we observed significantly larger motions for the isolated linkers in comparison to those reinforced by π-π stacking. With an accuracy comparable to that of SCXRD, we show for the first time that 3D ED can be a powerful tool to investigate dynamics at an atomic level, which is particularly beneficial for nanocrystalline materials and/or phase mixtures.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要