Kinetic Trapping of Photoluminescent Frameworks During High-Concentration Synthesis of Nonemissive Metal-Organic Frameworks

CHEMISTRY OF MATERIALS(2023)

引用 0|浏览6
暂无评分
摘要
Metal-organic frameworks (MOFs) are porous, crystalline materials constructed from organic linkers and inorganic nodes with potential utility in gas separation, drug delivery, sensing, and catalysis. Small variations in the MOF synthesis conditions can lead to a range of accessible frameworks with divergent chemical or photophysical properties. New methods to controllably access phases with tailored properties would broaden the scope of MOFs that can be reliably prepared for specific applications. Herein, we demonstrate that simply increasing the reaction concentration during the solvothermal synthesis of M-2(dobdc) (M = Mg, Mn, Ni; dobdc(4-) = 2,5-dioxido-1,4-benzenedicarboxylate) MOFs unexpectedly leads to trapping of a new framework termed CORN-MOF-1 (CORN = Cornell University) instead. In-depth spectroscopic, crystallographic, and computational studies support that CORN-MOF-1 has a structure similar to that of M-2(dobdc) but with partially protonated linkers and charge-balancing or coordinated formate groups in the pores. The resultant variation in linker spacing causes CORN-MOF-1 (Mg) to be strongly photoluminescent in the solid state, whereas H(4)dobdc and Mg-2(dobdc) are weakly emissive due to excimer formation. In-depth photophysical studies suggest that CORN-MOF-1 (Mg) is the first MOF based on the H(2)dobdc(2-) linker that likely does not emit via an excited-state intramolecular proton transfer (ESIPT) pathway. In addition, CORN-MOF-1 variants can be converted to high-quality samples of the thermodynamic M-2(dobdc) phases by heating in N,N-dimethylformamide (DMF). Overall, our findings support that high-concentration synthesis provides a straightforward method to identify new MOFs with properties distinct from known materials and to produce highly porous samples of MOFs, paving the way for the discovery and gram-scale synthesis of framework materials.
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