Zeolite-confined subnanometric PtSn mimicking mortise-and-tenon joinery for catalytic propane dehydrogenation

Insight into the Catalytic Oxidation Mechanism of Hydrogen Isotopes by Pt Clusters Confined by Silicalite-1

Highly efficient removal of low concentrations of hydrogen isotope gas in air is crucial for the safe operation of nuclear energy plants. Herein, silicalite-1-confined Pt cluster catalysts were used for the catalytic oxidation of hydrogen isotopes, and the related catalytic mechanism was revealed. Increased temperature in direct hydrogen reduction treatment slightly increased the size of Pt clusters from 1.6 nm at 400 °C to 1.8 nm at 600 °C. The catalyst reduced at 600 °C exhibited excellent performance (99%) in hydrogen isotope oxidation at 75 °C, as well as high stability and catalytic efficiency in continuous and intermittent operation for 7200 min. X-ray absorbance spectroscopy confirmed the existence of Pt clusters in the catalysts, and the theoretical results showed that the total net charge was -0.07 e, indicating a slight charge transfer from the zeolite to the Pt atoms. The metal-support interaction thermally stabilized Pt clusters and altered the metal electronic structure, which enhanced the catalytic activity following a hydroperoxyl (OOH)-mediated route. Based on the low reaction temperature, efficient hydrogen conversion rate, and high stability, the silicalite-1-confined Pt cluster catalyst is expected to be used in hydrogen isotope oxidation treatment to achieve nuclear safety.

Atomic Ru-Pt dual sites boost the mass activity and cycle life of alkaline hydrogen evolution

The development of highly efficient and ultrastable electrocatalysts for hydrogen generation from water/real seawater faces huge challenges. Herein, porous carbon-supported amorphous RuPt nanoclusters (Ru5.67Pt/PC) achieve mass activities of 42.28/10.93 A mgPt-1 and ultralong cycling stability in alkaline water/seawater because of the unique cluster structure and atomic Ru-Pt dual sites.

Strengthening the Metal-Acid Interactions by Using CeO2 as Regulators of Precisely Placing Pt Species in ZSM-5 for Furfural Hydrogenation

Understanding the synergism between the metal site and acid site is of great significance in boosting the efficiency of bi-functional catalysts in many heterogeneous reactions, particularly in biomass upgrading. Herein, a "confined auto-redox" strategy is reported to fix CeO2-anchored Pt atoms on the inner wall of a ZSM-5 cage, achieving the target of finely controlling the placements of the two active sites. Compared with the conventional surface-supported counterpart, the encapsulated Pt/CeO2@ZSM-5 catalyst possesses remarkably-improved activity and selectivity, which can convert >99% furfural into cyclopentanone with 97.2% selectivity in 6 h at 160 °C. Besides the excellent catalytic performance, the ordered metal-acid distribution also makes such kind of catalyst an ideal research subject for metal-acid interactions. The following mechanization investigation reveals that the enhancement is strongly related to the unique encapsulation structure, which promotes the migration of the reactants over different active sites, thereby contributing to the tandem reaction.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Migration of zeolite-encapsulated subnanometre platinum clusters via reactive neural network potentials

The migration of atoms and small clusters is an important process in sub-nanometre scale heterogeneous catalysis, affecting activity, accessibility and deactivation through sintering. Control of migration can be partially achieved via encapsulation of sub-nanometre metal particles into porous media such as zeolites. However, a general understanding of the migration mechanisms and their sensitivity to particle size and framework environment is lacking. Here, we extend the time-scale and sampling of atomistic simulations of platinum cluster diffusion in siliceous zeolite frameworks, by introducing a reactive neural network potential of density functional quality. We observe that Pt atoms migrate in a qualitatively different manner from clusters, occupying the dense region of the framework and avoiding the free pore space. We also find that for cage-like zeolite CHA there exists a maximum in self diffusivity for the Pt dimer beyond which, confinement effects hinder intercage migration. By extending the quality of sampling, NNP-based methods allow for the discovery of novel dynamical processes at the atomistic scale, bringing modelling closer to operando experimental characterization of catalytic materials.

Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts

The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts.

Dynamic Construction and Maintenance of Confined Nanoregions via Hydrogen-Bond Networks between Acetylene Reactants and a Polyoxometalate-Based Metal-Organic Framework

The nanoconfinement effect in catalysis has attracted much attention because it provides a novel means of regulating the molecular properties and related reactions. Confined nanoregions composed of both reactants and catalysts through weak interactions are expected to improve the catalytic performance and promote the mass transport of relevant molecules simultaneously. However, at reaction temperatures, the structural variation of such confined spaces constructed via weak interactions remains unclear. Herein, through density functional theory calculations combined with ab initio molecular dynamics simulations, we have systematically investigated the dynamic structural evolution of the confined space constructed by acetylene reactants and a polyoxometalate-based metal-organic framework (POMOF) via hydrogen-bond networks. It is found that, at the reaction temperature of acetylene semihydrogenation, the hydrogen-bond networks and generated confined nanoregions are not rigid but are constantly changing and dynamically maintained. The steering role played by the O atoms at the surfaces of the polyoxometalate clusters is essential for generation of the hydrogen-bond networks and maintenance of the nanoregions. Upon confinement, the acetylene reactants can be better activated than those in an unconstrained atmosphere, which is reflected by the different dynamic distributions of the ∠CHC bending magnitude. Moreover, from a comparison of the distinct interaction characteristics between acetylene/ethylene and POMOF, the different manifestations in the adsorption energy variations of the confined molecules can be interpreted. This work helps to elucidate the underlying mechanisms of confined catalysis and may promote its application in practical catalytic processes.

Ordered Hierarchical Porous Structure of PtSn/3DOMM-Al2O3 Catalyst for Promoting Propane Non-Oxidative Dehydrogenation

Herein, the hierarchical porous catalyst of 3-dimensional ordered macro-mesoporous (3DOMM) Al₂O₃ supported active PtSn nanoparticles (NPs) was prepared by the combined synthesized path of evaporation-induced self-assembly with colloid crystal template (EISA-CCT) methods. The hierarchical macro-mesoporous composite structure can markedly increase the specific surface area, accommodate the diffusion of propene, and decrease the number of surface acid sites. In addition, the special surface property and pore structure of 3DOMM-Al₂O₃ can modify the interaction between metals and substrates, as well as stabilize the metal nanoparticle, which promotes the formation of a highly active and stable PtSn phase. The PtSn/3DOMM-Al₂O₃ catalyst exhibits higher productivity and stability than PtSn/Al₂O₃ catalysts with macropore and mesopore structures. The PtSn/3DOMM-Al₂O₃ catalyst displays the best catalytic performance with propylene selectivity over 95% at a propane conversion of 33.9%. The study of the ordered hierarchical porous structure of PtSn/3DOMM-Al₂O₃ catalysts can contribute to obtaining improved catalysts in industrial processes.

Recent Progress of Ga-Based Catalysts for Catalytic Conversion of Light Alkanes

The efficient and clean conversion of light alkanes is a research hotspot in the petrochemical industry, and the development of effective and eco-friendly non-noble metal-based catalysts is a key factor in this field. Among them, gallium is a metal component with good catalytic performance, which has been extensively used for light alkanes conversion. Herein, we critically summarize recent developments in the preparation of gallium-based catalysts and their applications in the catalytic conversion of light alkanes. First, we briefly describe the different routes of light alkane conversion. Following that, the remarkable preparation methods for gallium-based catalysts are discussed, with their state-of-the-art application in light alkane conversion. It should be noticed that the directional preparation of specific Ga species, strengthening metal-support interactions to anchor Ga species, and the application of new kinds of methods for Ga-based catalysts preparation are at the leading edge. Finally, the review provides some current limitations and future perspectives for the development of gallium-based catalysts. Recently, different kinds of Ga species were reported to be active in alkane conversion, and how to separate them with advanced in situ and ex situ characterizations is still a problem that needs to be solved. We believe that this review can provide base information for the preparation and application of Ga-based catalysts in the current stage. With these summarizations, this review can inspire new research directions of gallium-based catalysts in the catalysis conversion of light alkanes with ameliorated performances.