Selective Hydrogenation Properties of Ni-Based Bimetallic Catalysts

Metallic Ni shows high activity for a variety of hydrogenation reactions due to its intrinsically high capability for H2 activation, but it suffers from low chemoselectivity for target products when two or more reactive functional groups are present on one molecule. Modification by other metals changes the geometric and electronic structures of the monometallic Ni catalyst, providing an opportunity to design Ni-based bimetallic catalysts with improved activity, chemoselectivity, and durability. In this review, the hydrogenation properties of these catalysts are described starting from the typical methods of preparing Ni-based bimetallic nanoparticles. In most cases, the reasons for the enhanced catalysis are discussed based on the geometric and electronic effects. This review provides new insights into the development of more efficient and well-structured non-noble metal-based bimetallic catalytic systems for chemoselective hydrogenation reactions.

Predicting Ruthenium Catalysed Hydrogenation of Esters using Machine Learning

Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often the bottlenecks in the commercialization of such technologies. The conventional approach of catalyst discovery is based on empiricism that makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. We demonstrate here the approach of machine learning for the prediction of out-comes for the catalytic hydrogenation of esters. Our models can predict the reaction yields with high mean accuracies of up to 91% (test set) and suggest that the use of certain chemical descriptors selectively can result in a more accurate model. Furthermore, cata-lysts and some of their corresponding descriptors can also be pre-dicted with mean accuracies of 85%, and >90%, respectively.

Gas phase vs. liquid phase: monitoring H2 and CO adsorption phenomena on Pt/Al2O3 by IR spectroscopy

The adsorption phenomena occurring at the surface of a highly-dispersed Pt/Al2O3 catalyst for hydrogenation reactions were thoroughly investigated in the gas-phase by transmission IR spectroscopy and in the liquid-phase by...

Highly Enhanced Catalytic Stability of Copper by the Synergistic Effect of Porous Hierarchy and Alloying for Selective Hydrogenation Reaction

Supported copper has a great potential for replacing the commercial palladium-based catalysts in the field of selective alkynes/alkadienes hydrogenation due to its excellent alkene selectivity and relatively high activity. However, fatally, it has a low catalytic stability owing to the rapid oligomerization of alkenes on the copper surface. In this study, 2.5 wt% Cu catalysts with various Cu:Zn ratios and supported on hierarchically porous alumina (HA) were designed and synthesized by deposition–precipitation with urea. Macropores (with diameters of 1 μm) and mesopores (with diameters of 3.5 nm) were introduced by the hydrolysis of metal alkoxides. After in situ activation at 350 °C, the catalytic stability of Cu was highly enhanced, with a limited effect on the catalytic activity and alkene selectivity. The time needed for losing 10% butadiene conversion for Cu1Zn3/HA was ~40 h, which is 20 times higher than that found for Cu/HA (~2 h), and 160 times higher than that found for Cu/bulky alumina (0.25 h). It was found that this type of enhancement in catalytic stability was mainly due to the rapid mass transportation in hierarchically porous structure (i.e., four times higher than that in bulky commercial alumina) and the well-dispersed copper active site modified by Zn, with identification by STEM–HAADF coupled with EDX. This study offers a universal way to optimize the catalytic stability of selective hydrogenation reactions.