Graphical Abstract The Front Cover illustrates the dehydrogenation reaction of propane on the surface of gallium oxide. In this context, NiGa nanoalloys generate hydrogen shields that protect the oxide surface from coke deposition. In their comprehensive Research Article, R. Naumann d'Alnoncourt et al. demonstrate how the addition of nickel to gallium oxide influences coke formation during propane dehydrogenation. Through X-ray diffraction analysis and electron microscopy, they reveal the formation of NiGa nanoparticles distributed on the catalysts surface. Interestingly, these nanoalloys do not directly participate in propane conversion. However, in situ thermogravimetric experiments show a significant reduction in the coking rate, when gallium oxide is decorated with a small amount of NiGa. The findings suggest that the activation of hydrogen by NiGa plays a crucial role in preventing coke formation. More information can be found in the Research Article by R. Naumann d'Alnoncourt et al.

Due to their availability, low cost, and activity, cobalt-based catalysts are a promising alternative to platinum for the industrial propane dehydrogenation processes. However, their low stability due to sintering, phase transformation, and coke deposition leads to severe deactivation. In this work, the synthesis of amorphous, ordered mesoporous alumina with stabilized Co2+ nanoclusters (Co-m-Al2O3) via an evaporation-induced self-assembly synthesis route is presented. The ordered mesoporous alumina is characterized for containing a large amount of defective pentacoordinate Al3+ sites and a small amount of strong acid sites. The incorporation of Co2+ clusters within the m-Al2O3 structure enhances the dispersion and stability and preserves their reduction even after prolonged time on stream. This leads to a highly selective and steady catalytic performance in the propane dehydrogenation reaction under industrial-relevant conditions. A significantly low deactivation rate of 0.53 d(-1) with stable propylene selectivity of 95% is observed after 23 h, resulting in a 117% higher space-time yield toward propylene compared to the state-of-the-art impregnated Co/gamma-Al2O3 catalyst. Furthermore, Co-m-Al2O3 leads to 4.6 times less coke formation, measured in situ for the first time. The detailed study of the nature of the cobalt sites, together with the acidic properties of the alumina supports, provides a deeper understanding of cobalt-based catalysts for dehydrogenation reactions.

Atomic layer deposition was applied on mesoporous silica to synthesize a highly dispersed gallium oxide catalyst. This system was used as starting material to investigate different loadings of nickel in the dehydrogenation of propane under industrially relevant, Oleflex-like conditions. The formation of NiGa alloys was confirmed by X-ray diffraction analysis and electron microscopy. Surprisingly, the nanoalloys enhanced the selectivity towards C3H6 while decreasing the tendency for coking. Herein, in situ thermogravimetry, and measured mass fractions of carbon revealed that the coking rate was reduced by over 50 % compared to the pristine gallium oxide. Generally, the increased selectivity can be explained by the partial hydrogenation and reduction of the gallium oxide surface. The optimum temperature for the removal of deposited carbon was evaluated by a temperature programmed oxidation. Finally, the best-performing Ni-GaOx catalyst was employed in a cycled experiment with periodic reaction and regeneration tests. After regeneration, the selected Ni-GaOx catalyst provided a higher yield of propylene compared to the unmodified gallium oxide. A supported gallium oxide catalyst, synthesized by atomic layer deposition, was modified by the addition of nickel, and used in the dehydrogenation of propane. Resulting NiGa nanoparticles helped to reduce the coke formation on gallium oxide under industrially relevant conditions. Finally, the catalyst system could be regenerated by an oxidative treatment.image

This study on surface-modifications of bulk oxidation catalysts with sub-monolayers of PO x , BO x and MnO x via atomic layer deposition demonstrates this method to be a powerful tool for tuning the performance in selective oxidations of light alkanes.

We propose a novel high-throughput workflow, combining DFT-derived atomic scale interaction parameters with experimental data to identify key performance-related descriptors in a CO2 to methanol reaction, for In-based catalysts. Utilizing advanced machine learning algorithms suitable for small datasets, secondary descriptors with high predictive power for catalytic activity were constructed. These descriptors, which highlight the crucial role of hydroxyl sites, can be applied to designing new materials and to bringing them to the test with high-throughput screening, paving the path for accelerated catalyst design.