Zhang Xiaofeng, Chen Xinfa, Fu Shiguo, Cao Ziping, Gong Wei, Liu Yan, Cui Yong
School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules and State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University, Shanghai, 200240, China.
Angew Chem Int Ed Engl. 2024 May 27;63(22):e202403878. doi: 10.1002/anie.202403878. Epub 2024 Apr 18.
Optically active π-conjugated polymers (OACPs) have garnered increasing research interest for their resemblance to biological helices and intriguing chirality-related functions. Traditional methods for synthesizing involve decorating achiral conjugated polymer architectures with enantiopure side substituents through complex organic synthesis. Here, we report a new approach: the templated synthesis of unsubstituted OACPs via supramolecularly confined polymerizations of achiral monomers within nanopores of 2D or 3D chiral covalent organic frameworks (CCOFs). We show that the chiral π-rich nanospaces facilitate the in situ enantiospecific polymerization and self-propagation, akin to nonenzymatic polymerase chain reaction (PCR) system, resulting in chiral imprinting. The stacked polymer chains are kinetically inert enough to memorize the chiral information after liberating from CCOFs, and even after treatment at temperature up to 200 °C. The isolated OACPs demonstrate robust enantiodiscrimination, achieving up to 85 % ee in separating racemic amino acids. This underscores the potential of utilizing CCOFs as templates for supramolecularly imprinting optical activity into CPs, paving the way for synthetic evolution and advanced functional exploration of OACPs.
光学活性π共轭聚合物(OACPs)因其与生物螺旋结构的相似性以及与手性相关的有趣功能而受到越来越多的研究关注。传统的合成方法是通过复杂的有机合成,用对映体纯的侧链取代基修饰非手性共轭聚合物结构。在此,我们报道一种新方法:通过在二维或三维手性共价有机框架(CCOFs)的纳米孔内对非手性单体进行超分子受限聚合,来模板合成未取代的OACPs。我们表明,富含手性π的纳米空间促进了原位对映体特异性聚合和自我传播,类似于非酶聚合酶链反应(PCR)系统,从而导致手性印记。堆叠的聚合物链在从CCOFs中释放后,甚至在高达200 °C的温度下处理后,在动力学上仍具有足够的惰性以保留手性信息。分离得到的OACPs表现出强大的对映体识别能力,在分离外消旋氨基酸时,对映体过量值(ee)高达85 %。这突出了利用CCOFs作为模板将光学活性超分子印记到CPs中的潜力,为OACPs的合成进化和高级功能探索铺平了道路。