Merging traditional and high-throughput approaches results in efficient design, synthesis and screening of catalysts for an industrial process

In this manuscript, a strategy is presented that focuses on the maximization of the information retrieve by balancing the use of HT technology and other highly informative but time consuming sources. The methodology is applied to the epoxidation of a model olefin with organic peroxides, using titanium grafted on MCM-41 and ITQ-2 as catalysts for the reaction. The presented work represents a contribution in the research methodology for heterogeneous catalytic studies, where HT and data mining are used in close collaboration with characterization, computational chemistry, and quantitative structure activity and property relationship (QSAR/QSPR) analyses. It is shown how such an overlapping of the different disciplines results in greater benefits from the point of view of saving experiments and knowledge acquisition, so that main properties of the catalysts can be optimized in a rational way using an automated strategy.

The epoxidation of a model olefin with organic peroxides using titanium grafted on MCM-41 and ITQ-2 is investigated as a model reaction for the epoxidation of methyl-oleate. This contribution deals with the research methodology where HT and data mining are used with characterization, computational chemistry, and QSAR-QSPR analyses. We show how this results in greater benefits considering experiments saving and knowledge acquisition.Figure optionsDownload high-quality image (86 K)Download as PowerPoint slide