Linear Supramolecular Polymers Driven By Anion-Anion Dimerization Of Difunctional Phosphonate Monomers Inside Cyanostar Macrocycles

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY(2019)

引用 55|浏览16
暂无评分
摘要
Supramolecular polymers have enabled far-reaching fundamental science and the development of diverse macro-molecular technologies owing to the reversible and noncovalent chemical connectivities that define their properties. Despite the unabated development of these materials using highly tailorable recognition elements, anion-based polymers remain rare as a result of the weak interactions they mediate. Here, we use design rules inspired by cation-driven polymers to demonstrate a new noncovalent link based on receptor-stabilized anion-anion interactions that enables the efficient linear polymerization of simple difunctional phosphonates. The linear main chain connectivity and molecular topology were confirmed by single crystal X-ray diffraction, which demonstrates the rare 2:2 stoichiometry between the anionic phosphonate end groups and a pair of pi-stacked cyanostar macrocycles. The stability of these links enables rapid polymerization of difunctional phosphonates employing different aliphatic linkers (C6H12, C8H16, C10H20, C12H24). Diphosphonates with greater chain flexibility (C12H24) enable greater polymerization with an average degree of polymerization of nine emerging at 10 mM. Viscosity measurements show a transition from oligomers to polymers at the critical polymerization concentration of 5 mM. In a rare correlation, NMR spectroscopy shows a coincident molecular signature of the polymerization at 5 mM. These polymers are highly concentration dependent, reversibly polymerize with acid and base, and respond to competitive anions. They display the design simplicity of metallo-supramolecular polymers with transfer of the strong 2:2 recognition chemistry to macromolecules. The simplicity and understanding of this new class of supramolecular polymer is anticipated to open opportunities in tailoring anion-based functional 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