Any catalyst should be efficient and stable to be implemented in practice. This requirement is particularly valid for manganese hydrogenation catalysts. While representing a more sustainable alternative to conventional noble metal-based systems, manganese hydrogenation catalysts are prone to degrade under catalytic conditions once operation temperatures are high. Herein, we report a highly efficient Mn(I)-CNP pre-catalyst which gives rise to the excellent productivity (TOF° up to 41 000 h⁻¹) and stability (TON up to 200 000) in hydrogenation catalysis. This system enables near-quantitative hydrogenation of ketones, imines, aldehydes and formate esters at the catalyst loadings as low as 5–200 p.p.m. Our analysis points to the crucial role of the catalyst activation step for the catalytic performance and stability of the system. While conventional activation employing alkoxide bases can ultimately provide catalytically competent species under hydrogen atmosphere, activation of Mn(I) pre-catalyst with hydride donor promoters, e.g. KHBEt₃, dramatically improves catalytic performance of the system and eliminates induction times associated with slow catalyst activation.

Homogeneous hydrogenation catalysts based on metal complexes provide a diverse and highly tunable tool for the fine chemical industry. To fully unleash their potential, fast and effective methods for the evaluation of catalytic properties are needed. In turn, this requires changes in the experimental approaches to test and evaluate the performance of the catalytic processes. Design of experiment combined with statistical analysis can enable time- and resource-efficient experimentation. In this work, we employ a set of different statistical models to obtain the detailed kinetic description of a highly active homogeneous Mn (I) ketone hydrogenation catalyst as a representative model system. The reaction kinetics were analyzed using a full second order polynomial regression model, two models with eliminated parameters and finally a model which implements “chemical logic”. The coefficients obtained are compared with the corresponding high-quality kinetic parameters acquired using conventional kinetic experiments. We demonstrate that various kinetic effects can be well captured using different statistical models, providing important insights into the reaction kinetics and mechanism of a complex catalytic reaction.