Suppr超能文献

用于多相催化的共价有机框架:原理、现状与挑战

Covalent Organic Frameworks for Heterogeneous Catalysis: Principle, Current Status, and Challenges.

作者信息

Guo Jia, Jiang Donglin

机构信息

State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, 2205 Songhu Road, Shanghai 200438, China.

Department of Chemistry, Faculty of Science, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.

出版信息

ACS Cent Sci. 2020 Jun 24;6(6):869-879. doi: 10.1021/acscentsci.0c00463. Epub 2020 May 29.

Abstract

Heterogeneous catalysts offer a cyclable platform for exploring efficient transformation systems, and their promising applications underpin a broad research interest. Covalent organic frameworks (COFs) are a class of crystalline porous networks that can integrate organic units into ordered skeletons and pores, offering an insoluble and robust platform for exploring heterogeneous catalysts. In this Outlook, we describe a conceptual scheme for designing catalytic COFs to promote various transformations. We summarize the general strategy for designing COFs to construct tailor-made skeletons and pores by emphasizing their structural uniqueness. We introduce different approaches to develop catalytic functions by sampling COFs into four regimes, i.e., skeletons, walls, pores, and systematically organized systems. We scrutinize their catalytic features and elucidate interplays with electrons, holes, and molecules by highlighting the key role of interface design in exploring catalytic COFs. We further envisage the key issues to be challenged, future research directions, and perspectives to show a full picture of designer heterogeneous catalysis based on COFs.

摘要

多相催化剂为探索高效转化系统提供了一个可循环的平台,其前景广阔的应用引发了广泛的研究兴趣。共价有机框架(COF)是一类晶体多孔网络,能够将有机单元整合到有序的骨架和孔中,为探索多相催化剂提供了一个不溶性且坚固的平台。在本展望中,我们描述了一种设计催化COF以促进各种转化的概念方案。我们通过强调其结构独特性,总结了设计COF以构建定制骨架和孔的一般策略。我们介绍了通过将COF分为四种类型(即骨架、壁、孔和系统组织的体系)来开发催化功能的不同方法。我们通过突出界面设计在探索催化COF中的关键作用,仔细研究它们的催化特性,并阐明与电子、空穴和分子的相互作用。我们进一步设想了有待挑战的关键问题、未来的研究方向和前景,以全面展示基于COF的定制多相催化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4901/7318070/a95db923ea2d/oc0c00463_0001.jpg

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