Supported Pt nanoparticles are widely used for the catalytic dehydrogenation of propane to propylene. However, Pt nanoparticles suffer from strong deactivation under dehydrogenation conditions. In this study, we present a...
The first molecular carbonyl RhMn cluster Na2[Rh3Mn3(CO)18] 2 with highly labile CO ligands and predefined Rh‐Mn bonds could be realized and successfully used for the preparation of the silica (davisil)‐supported RhMnOx catalysts for the conversion of syngas (CO, H2) to ethanol (StE); it has been synthesized through the salt metathesis reaction of RhCl3 with Na[Mn(CO)5] 1 and isolated in 49 % yields. The dianionic Rh3Mn3 cluster core of 2 acts as a molecular single‐source precursor (SSP) for the low‐temperature preparation of selective high‐performance RhMnOx catalysts. Impregnation of 2 on silica (davisil) led to three different silica‐supported RhMnOx catalysts with dispersed Rh nanoparticles tightly surrounded by a MnOx matrix. By using this molecular SSP approach, Rh and MnOx are located in close proximity on the oxide support. Therefore, the number of tilted CO adsorption sites at the RhMnOx interface increased leading to a significant enhancement in selectivity and performance. Investigations on the spent catalysts after several hours time‐on‐stream revealed the influence of rhodium carbide RhCx formation on the long‐term stability.
Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO 2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STY HA ) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO 2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property−performance relations of complex catalytic systems.
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