Operando characterization of rhodium catalyst degradation in hydroformylation

For a comprehensive understanding of catalyst stability, knowledge of deactivation processes is an important keystone in addition to activity and selectivity. The underlying mechanisms and kinetics of deactivation help to understand and control the formation of undesired byproducts, thus preventing a loss of selectivity. This work addresses catalyst deactivation in the homogeneously Rh/BiPhePhos-catalyzed hydroformylation of a long-chain olefin by hydroperoxides. Using operando FTIR spectroscopy, kinetic perturbation experiments with tert-butyl hydroperoxide were able to correlate the loss of regioselectivity in hydroformylation with the structural changes of the catalyst, providing evidence for degradation. Comparison of experimental and DFT calculated vibrational bands indicated the oxidative degradation of the diphosphite ligand and the formation of Rh carbonyl clusters. To predict a critical degradation of the ligand by hydroperoxides over time, which leads to a loss of regioselectivity, deactivation kinetics were derived and parameterized. The developed kinetic model of deactivation predicts the critical degradation of the ligand at different temperatures and concentrations of tert-butyl hydroperoxide well. It offers a model-based approach for process stabilization and optimization e.g. using ligand dosing strategies in the future.

Automated summary

In addition to activity and selectivity, understanding the deactivation processes is essential for a comprehensive understanding of catalyst stability. This study explores catalyst deactivation in the hydroformylation reaction using Rh/BiPhePhos catalyst and hydroperoxides. By investigating the structural changes of the catalyst with operando FTIR spectroscopy and kinetic perturbation experiments, the loss of regioselectivity in the hydroformylation reaction is correlated with the degradation of the catalyst. The developed kinetic model predicts the critical degradation of the ligand and provides a model-based approach for process stabilization and optimization.