Suppr超能文献

基于铼(I)的杂配五角环形金属穴状配体:自组装与分子识别研究

Rhenium(I)-Based Heteroleptic Pentagonal Toroid-Shaped Metallocavitands: Self-Assembly and Molecular Recognition Studies.

作者信息

Bhol Mamina, Shankar Bhaskaran, Sathiyendiran Malaichamy

机构信息

School of Chemistry, University of Hyderabad, Hyderabad 500 046, India.

Department of Chemistry, Thiagarajar College of Engineering, Madurai 625015, India.

出版信息

Inorg Chem. 2022 Jul 25;61(29):11497-11508. doi: 10.1021/acs.inorgchem.2c02061. Epub 2022 Jul 12.

Abstract

A family of neutral, heteroleptic, dinuclear MLL'-type pentagonal toroid-shaped metallomacrocycles () were synthesized using flexible ditopic N donors (L = L-L), rigid bis-chelating ligands (H-L' = H-E), and Re(CO) in a one-pot solvothermal self-assembly approach. The ligands and the metallomacrocycles were characterized using ATR-IR, electrospray ionization mass spectrometry, nuclear magnetic resonance, ultraviolet-visible, and emission spectroscopy methods. The molecular structures of , , , , and were confirmed by an X-ray diffraction study and are similar to those of calix[5]arene. The cyclic inner cavities of the metallomacrocycles accommodate toluene/mesitylene/acetone/chlorobenzene as guest molecules that are stabilized by cumulative C-H···π and π···π interactions with the cyclic framework of metallomacrocycle. The photophysical properties of the ligands and the metallomacrocycles were studied. The host-guest recognition properties of metallocavitands , , , and as a model host with phenol and nitrobenzene derivatives as guest molecules were studied by emission spectroscopy methods.

摘要

采用一锅法溶剂热自组装方法,使用柔性双齿氮供体(L = L-L)、刚性双螯合配体(H-L' = H-E)和Re(CO)合成了一族中性、杂配双核MLL'-型五角环面状金属大环化合物()。使用衰减全反射红外光谱(ATR-IR)、电喷雾电离质谱、核磁共振、紫外可见光谱和发射光谱方法对配体和金属大环化合物进行了表征。通过X射线衍射研究确定了、、、和的分子结构,它们与杯[5]芳烃的结构相似。金属大环化合物的环状内腔容纳甲苯/均三甲苯/丙酮/氯苯作为客体分子,这些客体分子通过与金属大环化合物的环状骨架的累积C-H···π和π···π相互作用而得以稳定。研究了配体和金属大环化合物的光物理性质。通过发射光谱方法研究了金属穴合物、、、作为模型主体与苯酚和硝基苯衍生物作为客体分子的主客体识别性质。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验