Abstract
We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.
Highlights
It is one of the most studied processes in the history of chemistry, catalytic hydrogenation is still full of surprises.[1]
An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to > 99.9%, with no obvious structure/activity correlations
We demonstrate the utility of combining multicomponent ligand synthesis and catalytic performance studies with predictive modelling for catalyst discovery and optimization
Summary
It is one of the most studied processes in the history of chemistry, catalytic hydrogenation is still full of surprises.[1]. The problem is that whilst research on homogeneous catalytic hydrogenation has provided us with effective solutions to specific reactions, there is no “grand unified theory” for finding good hydrogenation catalysts. The computer can “synthesize” sets of virtual catalysts (see flowchart in Figure 1), and predict their characteristics (descriptor values) and performance (figures of merit). These predictions can be validated experimentally, closing the cycle
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