Utilizing Design of Experiments Approach to Assess Kinetic Parameters for a Mn Homogeneous Hydrogenation Catalyst. Issue 23 (14th September 2021)

• Record Type: Journal Article
• Authors: van Schendel, Robin K. A.; Yang, Wenjun; Uslamin, Evgeny A.; Pidko, Evgeny A.
• Is Part Of: ChemCatChem. Volume 13:Issue 23(2021)
• Page Start: 4886
• Page End: 4896
• Publication Date: 2021-09-14
• Subjects: Catalytic hydrogenation -- Design of experiments -- High-throughput experimentation -- Reaction kinetics -- Statistical analysis; Catalysis -- Periodicals; 541.39505
• DOI: 10.1002/cctc.202101140
• Languages: English
• ISSNs: 1867-3880

Abstract

Abstract: 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. Abstract : Statistical modelling : A rapid and powerful approach to assess the catalytic reaction kinetics is developed by combining the design of experiments with statistical modelling. Equating the regression equations and derived coefficients of resulting statistical models, we are able to measure the activation energy, to capture different reaction regimes, and to map the response of the observed reaction rate to condition parameters.