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催化活性中电子结构和界面相互作用特征的发现。

Discovery of Electronic Structure and Interfacial Interaction Features in Catalytic Activity.

机构信息

Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.

Key Laboratory of Hunan Province for Clean and Efficient Utilization of Strategic Calcium-Containing Mineral Resources, School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, P. R. China.

出版信息

Langmuir. 2022 Apr 5;38(13):3959-3968. doi: 10.1021/acs.langmuir.2c00176. Epub 2022 Mar 25.

Abstract

The selective transformation of inert bonds (C-H, C-O, C-C, C-F, etc.) via various catalysts is one of the most challenging areas, with applications in organic synthesis, materials science, and biological and pharmaceutical chemistry. The catalytic performance of homogeneous and heterogeneous catalysts can be rationally controlled in two ways: (i) electronic structure modulation of the active site, such as the metal center, ligands, and coordination modes, to improve the catalytic activity and stability and (ii) tuning intermolecular or interfacial interactions to promoting the reaction kinetics by accelerating the transmission of electrons between the catalyst and solvents or support. The rational design of catalysts based on adjustable features, such as metal (monometallic or bimetallic) active sites, crystal phase, ligands, solvents, and supports for inert bond activation under mild conditions remains a challenge. This Perspective summarizes the features of electronic structures, interfacial interactions, and their effects on molecular catalysis, metal-organic frameworks (MOFs), and natural mineral catalysis. The discovery of efficient catalysts could be promoted using machine-learning methods with high-performance descriptors. More attention should be paid to high-throughput quantum-chemical computations and experiments, automatic searches of chemical reaction pathways, and efficient machine-learning or deep-learning methods to accelerate catalyst design and synthesis in the future.

摘要

通过各种催化剂选择性地转化惰性键(C-H、C-O、C-C、C-F 等)是最具挑战性的领域之一,其应用涉及有机合成、材料科学、生物和药物化学。均相和多相催化剂的催化性能可以通过两种方式进行合理控制:(i)通过调节活性位点(如金属中心、配体和配位模式)的电子结构来提高催化活性和稳定性,(ii)通过加速催化剂与溶剂或载体之间的电子传递来促进反应动力学,从而调节分子间或界面相互作用。基于可调特性(如金属(单金属或双金属)活性位点、晶体相、配体、溶剂和载体)设计用于在温和条件下激活惰性键的催化剂仍然是一个挑战。本综述总结了电子结构、界面相互作用及其对分子催化、金属有机骨架(MOFs)和天然矿物催化的影响。可以使用具有高性能描述符的机器学习方法来促进高效催化剂的发现。未来应更多地关注高通量量子化学计算和实验、化学反应途径的自动搜索以及高效的机器学习或深度学习方法,以加速催化剂的设计和合成。

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