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.

Network-Like Platinum Nanosheets Enabled by a Calorific-Effect-Induced-Fusion Strategy for Enhanced Catalytic Hydrogenation Performance

In this paper, we report the construction of network-like platinum (Pt) nanosheets based on Pt/reduced graphite oxide (Pt/rGO) hybrids by delicately utilizing a calorific-effect-induced-fusion strategy. The tiny Pt species first catalyzed the H2-O2 combination reaction. The released heat triggered the combustion of the rGO substrate under the assistance of the Pt species catalysis, which induced the fusion of the tiny Pt species into a network-like nanosheet structure. The loading amount and dispersity of Pt on rGO are found to be crucial for the successful construction of network-like Pt nanosheets. The as-prepared products present excellent catalytic hydrogenation activity and superior stability towards unsaturated bonds such as olefins and nitrobenzene. The styrene can be completely converted into phenylethane within 60 min. The turnover frequency (TOF) value of network-like Pt nanosheets is as high as 158.14 h−1, which is three times higher than that of the home-made Pt nanoparticles and among the highest value of the support-free bimetallic catalysts ever reported under similar conditions. Furthermore, the well dispersibility and excellent aggregation resistance of the network-like structure endows the catalyst with excellent recyclability. The decline of conversion could be hardly identified after five times recycling experiments.

Covalent Organic Frameworks Anchored with Frustrated Lewis Pairs for Hydrogenation of Alkynes with H2

Frustrated Lewis Paris (FLPs) chemistry has been widely explored in the field of catalytic hydrogenation. However, the FLPs, which was usually used as homogeneous catalysts, quickly lost their catalytic reactivity...

Hierarchical Graphitic Carbon-Encapsulating Cobalt Nanoparticles for Catalytic Hydrogenation of 2,4-Dinitrophenol

Cobalt hierarchical graphitic carbon nanoparticles (Co@HGC) (1), (2), and (3) were prepared by simple pyrolysis of a cobalt phenanthroline complex in the presence of anthracene at different temperatures and heating times, under a nitrogen atmosphere. The samples were used for the catalytic hydrogenation of 2,4-dinitrophenol. Samples (1) and (3) were prepared by heating at 600 °C and 800 °C respectively, while (2) was prepared by heating at 600 °C with an additional intermediate stage at 300 °C. This work revealed that graphitization was catalyzed by cobalt nanoparticles and occurred readily at temperatures of 600 °C and above. The nanocatalysts were characterized by Scanning Electron Microscopy SEM, energy dispersive X-ray analysis EDX, Raman, Xrd, and XPS. The analysis revealed the presence of cobalt and cobalt oxide species as well as graphitized carbon, while TEM analysis indicated that the nanocatalyst contains mainly cobalt nanoparticles of 3–20 nm in size embedded in a lighter graphitic web. Some bamboo-like multiwall carbon nanotubes and graphitic onion-like nanostructures were observed in (3). The structures and chemical properties of the three catalysts were correlated with their catalytic activities. The apparent rate constants kapp (min−1) of the 2,4-dinitrophenol reductions were 0.34 for (2), 0.17 for (3), 0.04 for (1), 0.005 (no catalyst). Among the three studied catalysts, the highest rate constant was obtained for (2), while the highest conversion yield was achieved by (3). Our data show that an increase in agglomeration of the cobalt species reduces the catalytic activity, while an increase in pyrolysis temperature improves the conversion yield. The nanocatalyst enhances hydrogen generation in the presence of sodium borohydride and reduces 2,4-dinitrophenol to p-diamino phenol. The best nanocatalyst (3) was prepared at 800 °C. It consisted of uniformly distributed cobalt nanoparticles sheltered by hierarchical graphitic carbon. The nanocatalyst is easily separated and recycled from the reaction system and proved to be degradation resistant, to have robust stability, and high activity towards the reduction reaction of nitrophenols.