Understanding Nickel (Pre-)Catalyst Activation, Speciation, and Inhibition

了解镍（预）催化剂的活化、形态形成和抑制

• 批准号: 2887930
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887930
• 关键词: Understanding; Nickel; Pre; Catalyst; Activation

Nickel-catalysed methodology often relies on the in situ formation of an active catalyst from a nickel complex and an ancillary ligand; this can be 'simple' ligand exchange at a nickel(0) complex or the reduction of a nickel(II)/ligand complex to nickel(I) or nickel(0). When the ancillary ligand is an N-heterocyclic carbene (NHC), this is typically added as an azolium salt which must be deprotonated. The in situ formation of the active catalyst is convenient for screening reaction conditions or for high-throughput experimentation, but a lack of turnover might then be the result of the active catalyst being: (i) ineffective for the transformation of interest; (ii) forming too slowly or even not forming at all; (iii) inhibited by pre-catalyst initiation by-products; or (iv) unstable under the reaction conditions, and therefore prone to decomposition.This hinders attempts to understand ligand effects in a structured and data-driven manner: ligand structure will affect the rate of active catalyst formation, the speciation of the active catalyst, the rate and selectivity of the reaction of interest, and the rate at which the active catalyst decomposes. This is especially important as the use of machine learning and other data analysis techniques take an increasing role in reaction understanding and optimisation, because these require high-quality datasets. Furthermore, the use of single time-point yields makes it difficult to assess whether reaction failure is due to slow turnover, or to rapid turnover plus rapid catalyst death. This project comprises a detailed and structured analysis of the way(s) in which nickel (pre)-catalysts generate active catalysts, and how this process depends on ancillary ligand structure and reaction conditions. This will allow the confident interpretation of data from screening and optimisation studies that use these nickel (pre-)catalysts.

镍催化方法通常依赖于镍配合物和辅助配体在原位形成活性催化剂；这可以是镍(0)配合物的“简单”配体交换，也可以是镍(II)/配体配合物还原为镍(I)或镍(0)。当辅助配体是n -杂环碳（NHC）时，通常以必须去质子化的偶氮盐的形式加入。活性催化剂的原位形成便于筛选反应条件或进行高通量实验，但缺乏周转率可能是活性催化剂的结果：(i)对目标转化无效；（ii）成形太慢，甚至根本不成形；（iii）被预催化剂引发副产物抑制；或（iv）在反应条件下不稳定，因此容易分解。这阻碍了以结构化和数据驱动的方式理解配体效应的尝试：配体结构将影响活性催化剂形成的速率，活性催化剂的形态，感兴趣的反应的速率和选择性，以及活性催化剂分解的速率。这一点尤其重要，因为机器学习和其他数据分析技术在反应理解和优化中发挥着越来越重要的作用，因为这些需要高质量的数据集。此外，单时间点产率的使用使得很难评估反应失败是由于缓慢的转化，还是由于快速的转化加上快速的催化剂死亡。本项目包括对镍（预）催化剂生成活性催化剂的方式进行详细和结构化的分析，以及该过程如何依赖于辅助配体结构和反应条件。这将允许对使用这些镍（预）催化剂的筛选和优化研究的数据进行自信的解释。