基于多氮唑-羧酸双官能团配体的金属有机骨架材料的合成、结构与吸附分离性能

项目名称： 基于多氮唑-羧酸双官能团配体的金属有机骨架材料的合成、结构与吸附分离性能

项目编号： No.21261028

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 数理科学和化学

项目作者： 朱爱新

作者单位： 云南师范大学

项目金额： 48万元

中文摘要： 金属有机骨架作为一种新型多孔材料，由于在分离、催化、气体贮存、离子交换、分子磁体等方面具有广泛的应用前景正成为材料领域一大研究热点。此类材料的选择性吸附作用可克服传统方法上气体/溶剂分子分离所需的低温和高压的条件，或高耗能的分馏步骤。因此，该类材料在气体/溶剂分子的选择性分离领域的研究备受关注。本项目拟采用含多氮唑与羧酸双官能团的有机桥联配体，通过取代基的改变、多氮唑基团类型及其与羧酸相对位置和个数的改变，不同配位特性的金属离子和实验条件的选择，来调控金属有机骨架的结构及其孔结构，探索该类材料的控制合成条件。在单晶和粉末X-衍射确定其空间结构的基础上，通过吸附测试手段研究对气体和溶剂分子的吸附与选择性分离性能，获得比较系统的信息，总结其构效关系，为定向组装具有高效吸附和选择性分离功能的金属有机骨架化合物提供经验和规律，并开发出具有选择性高效分离功能的新型多孔材料，降低工业能耗的目的。

中文关键词： 金属有机骨架；多氮唑；羧酸；气体吸附与分离；选择性识别

英文摘要： Metal-organic Frameworks are a new class of porous material and have already currently become a hot topic of material fields because of their potential applications as separation, catalysis, molecular storage,ion exchange and molecular magnets. Especially, they have attracted more attentions for their merits on facile gas/solvent preparations because the process of selective adsorptions for this kind of porous material can overcome the shortcomings of the conventional methods requiring special low temperature and high pressure, and/or large energy-consuming fractional process. This project will adopt the bifunctional organic ligands containing azolate and carboxylate groups, vary the subsitituent groups, the type of azolate, and the relative position and number with carboxylate groups in order to adjust the framework and pore structure, and seek for the controlling synthesis conditions by using different metal ions with diverse coordination modes and changing the diverse experiment conditions. Moreover, on the basis of the structures of the frameworks defined by single crystal and powder X-ray diffraction, the systemic information will be acquired and the relationship between the structures and properties are summarized by studying the properties of sorption and separation through sorption measurements, which pr

英文关键词： metal-organic framework；azolate；carboxylate；gas adsorption and separation；selective sensing