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 a '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: (i) being ineffective for the transformation of interest; (ii) forming too slowly or even not forming at all; (iii) being inhibited by pre-catalyst initiation by-products; or (iv) being 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.