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Copper-Catalysed Aryl-Chloride Amination: A Quality-by-Design Approach Based on Improved Mechanistic Understanding

Copper-Catalysed Aryl-Chloride Amination: A Quality-by-Design Approach Based on Improved Mechanistic Understanding
铜催化芳基氯胺化:基于改进机理理解的质量设计方法
批准号:
2275542
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

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中文摘要
翻译
近年来,铜盐和铜配合物已显示出它们本身是用于交叉偶联反应的绿色、经济和通用的试剂,其范围与钯的范围类似,并且报道了在碳-碳、碳-杂原子、甚至碳-氢和碳-金属键形成中的应用。然而,铜在偶联方案中的使用主要限于研究实验室中的小规模研究,并且通常不用于大规模批量制备。支撑这一点的是目前缺乏对这些反应的机制的理解,以及配体和碱在速率决定步骤和催化剂失活途径中的作用的问题。该项目将设法解决这些不足之处,主要目标如下:使用广泛的实验,光谱和动力学方法对铜催化的胺化反应建立更深入的机理理解确定辅助配体,碱和溶剂在催化循环和停止循环事件(包括催化剂失活)中的作用和相互依赖性利用机理和速率信息开发新的和改进的辅助配体系统用于铜催化的芳基氯活化,最初侧重于草酸二酰胺配体过渡学习高度关注的系统,包括胺,酰胺,磺酰胺和亲核碳底物以及向放大工艺的进展
英文摘要
Recently copper salts and complexes have shown themselves to be Green, economical and versatile reagents for cross-coupling reactions with breadth of scope similar to that of palladium, and reported applications in carbon-carbon, carbon-heteroatom, and even carbon-hydrogen and carbon-metal bond formation. However, the use of copper in coupling protocols has mainly been limited to small-scale studies in research labs and has not commonly been employed on larger-scale batch preparations. Underpinning this is a current lack of understanding of the mechanism of these reactions, and questions on the role of the ligand and base in both the rate determining step and catalyst deactivation pathways. This project will seek to address these shortfalls with the following main objectives:Build a deeper mechanistic understanding of copper catalysed amination reactions using a wide range of experimental, spectroscopic, and kinetic methodsDetermination of the roles and interdependencies of the ancillary ligand, base, and solvent in both the catalytic cycle and off-cycle events including catalyst deactivationUtilise mechanistic and rate information to develop new and improved ancillary ligand systems for copper-catalysed aryl-chloride activation, initially focussing upon oxalic diamide ligandsTransition learning to systems of high interest including the application of amine, amide, sulphonamide, and nucleophilic carbon substrates and progression towards scaled-up processes
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