Dissecting La2Ce2O7 catalyst to unravel the origin of the surface active sites devoting to its performance for oxidative coupling of methane (OCM)

Atomistic insights from DFT calculations into the catalytic properties on ceria-lanthanum clusters for methane activation

Improving the catalytic performance of materials based on cerium oxide (CeO2) for the activation of methane (CH4) can be achieved through the following strategies: mixture of CeO2 with different oxides (e.g., CeO2-La2O3) and the use of particles with different sizes. In this study, we present a theoretical investigation of the initial CH4 dehydrogenation on (La2Ce2O7)n clusters, where n = 2, 4, and 6. Our framework relies on density functional theory calculations combined with the unity bond index-quadratic exponential potential approximation. Our results indicate that chemical species arising from the first dehydrogenation of CH4, that is, CH3 and H, bind through the formation of C-O and H-O bonds with the clusters, respectively. The coordination of the adsorption site and the chemical environment plays a crucial role in the magnitude of the adsorption energy; for example, species adsorb more strongly in the low-coordinated topO sites located close to the La atoms. Thus, it affects the activation energy barrier, which tends to be lower in configurations where the adsorption of the chemical species is stronger. During CH4 dehydrogenation, the CH3 radical can be present in a planar or tetrahedral configuration. Its conformation changes as a function of the charge transference between the molecule and the cluster, which depends on the CH3-cluster distance. Finally, we analyze the effects of the Hubbard effective parameter (Ueff) on adsorption properties, as the magnitude of localization of Ce f-states affects the hybridization of the interaction between the molecule and the clusters and hence the magnitude of the adsorption energies. We obtained a linear decrease in the adsorption energies by increasing the Ueff parameter; however, the activation energy is only slightly affected.

Surface basicity controls C-C coupling rates during carbon dioxide-assisted methane coupling over bifunctional Ca/ZnO catalysts

Carbon dioxide-assisted coupling of methane offers an approach to chemically upgrade two greenhouse gases and components of natural gas to produce ethylene and syngas. Prior research on this reaction has concentrated efforts on catalyst discovery, which has indicated that composites comprised of both reducible and basic oxides are especially promising. There is a need for detailed characterization of these bifunctional oxide systems to provide a more fundamental understanding of the active sites and their roles in the reaction. We studied the dependence of physical and electronic properties of Ca-modified ZnO materials on Ca content via X-ray photoelectron and absorption spectroscopies, electron microscopy, and infrared spectroscopic temperature-programmed desorption (IR-TPD). It was found that introduction of only 0.6 mol% Ca onto a ZnO surface is necessary to induce significant improvement in the catalytic production of C₂ species: C₂ selectivity increases from 5% on un-modified ZnO to 58%, at similar conversions. Evidence presented shows that this selectivity increase results from the formation of an interface between the basic CaO and reducible ZnO phases. The basicity of these interface sites correlates directly with catalytic activity over a wide composition range, and this relationship indicates that moderate CO₂ adsorption strength is optimal for CH₄ coupling. These results demonstrate, for the first time to our knowledge, a volcano-type relationship between CO₂-assisted CH₄ coupling activity and catalyst surface basicity, which can inform further catalyst development.

High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane

Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...