Dynamic structural evolution of MgO-supported palladium catalysts: from metal to metal oxide nanoparticles to surface then subsurface atomically dispersed cations.

Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states-which influence the metal nuclearities, oxidation states, and catalytic properties. In this investigation, we report the results of in situ X-ray absorption spectroscopy, scanning transmission electron microscopy, and other physical characterization techniques, bolstered by density functional theory, to elucidate the structural transformations of a set of MgO-supported palladium catalysts under oxidative treatment conditions. As the calcination temperature increased, the as-synthesized supported metallic palladium nanoparticles underwent oxidation to form palladium oxides (at approximately 400 °C), which, at approximately 500 °C, were oxidatively fragmented to form mixtures of atomically dispersed palladium cations. The data indicate two distinct types of atomically dispersed species: palladium cations located at MgO steps and those embedded in the first subsurface layer of MgO. The former exhibit significantly higher (>500 times) catalytic activity for ethylene hydrogenation than the latter. The results pave the way for designing highly active and stable supported palladium hydrogenation catalysts with optimized metal utilization.

Reversible Intrapore Redox Cycling of Platinum in Platinum-Ion-Exchanged HZSM-5 Catalysts.

Isolated platinum(II) ions anchored at acid sites in the pores of zeolite HZSM-5, initially introduced by aqueous ion exchange, were reduced to form platinum nanoparticles that are stably dispersed with a narrow size distribution (1.3 ± 0.4 nm in average diameter). The nanoparticles were confined in reservoirs within the porous zeolite particles, as shown by electron beam tomography and the shape-selective catalysis of alkene hydrogenation. When the nanoparticles were oxidatively fragmented in dry air at elevated temperature, platinum returned to its initial in-pore atomically dispersed state with a charge of +2, as shown previously by X-ray absorption spectroscopy. The results determine the conditions under which platinum is retained within the pores of HZSM-5 particles during redox cycles that are characteristic of the reductive conditions of catalyst operation and the oxidative conditions of catalyst regeneration.

Reduction of Cofed Carbon Dioxide Modifies the Local Coordination Environment of Zeolite-Supported, Atomically Dispersed Chromium to Promote Ethane Dehydrogenation.

The reduction of CO2 is known to promote increased alkene yields from alkane dehydrogenations when the reactions are cocatalyzed. The mechanism of this promotion is not understood in the context of catalyst active-site environments because CO2 is amphoteric, and even general aspects of the chemistry, including the significance of competing side reactions, differ significantly across catalysts. Atomically dispersed chromium cations stabilized in highly siliceous MFI zeolite are shown here to enable the study of the role of parallel CO2 reduction during ethylene-selective ethane dehydrogenation. Based on infrared spectroscopy and X-ray absorption spectroscopy data interpreted through calculations using density functional theory (DFT), the synthesized catalyst contains atomically dispersed Cr cations stabilized by silanol nests in micropores. Reactor studies show that cofeeding CO2 increases stable ethylene-selective ethane dehydrogenation rates over a wide range of partial pressures. Operando X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectra indicate that during reaction at 650 °C the Cr cations maintain a nominal 2+ charge and a total Cr-O coordination number of approximately 2. However, CO2 reduction induces a change, correlated with the CO2 partial pressure, in the population of two distinct Cr-O scattering paths. This indicates that the promotional effect of parallel CO2 reduction can be attributed to a subtle change in Cr-O bond lengths in the local coordination environment of the active site. These insights are made possible by simultaneously fitting multiple EXAFS spectra recorded in different reaction conditions; this novel procedure is expected to be generally applicable for interpreting operando catalysis EXAFS data.

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts.

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training.

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials.

Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-ab initio accuracy.

Elucidating correlated defects in metal organic frameworks using theory-guided inelastic neutron scattering spectroscopy.

Metal organic frameworks (MOFs) that incorporate metal oxide cluster nodes, exemplified by UiO-66, have been widely studied, especially in terms of their deviations from the ideal, defect-free crystalline structures. Although defects such as missing linkers, missing nodes, and the presence of adventitious synthesis-derived node ligands (such as acetates and formates) have been proposed, their exact structures remain unknown. Previously, it was demonstrated that defects are correlated and span multiple unit cells. The highly specialized techniques used in these studies are not easily applicable to other MOFs. Thus, there is a need to develop new experimental and computational approaches to understand the structure and properties of defects in a wider variety of MOFs. Here, we show how low-frequency phonon modes measured by inelastic neutron scattering (INS) spectroscopy can be combined with density functional theory (DFT) simulations to provide unprecedented insights into the defect structure of UiO-66. We are able to identify and assign peaks in the fingerprint region (<100 cm-1) which correspond to phonon modes only present in certain defective topologies. Specifically, this analysis suggests that our sample of UiO-66 consists of predominantly defect-free fcu regions with smaller domains corresponding to a defective bcu topology with 4 and 2 acetate ligands bound to the Zr6O8 nodes. Importantly, the INS/DFT approach provides detailed structural insights (e.g., relative positions and numbers of acetate ligands) that are not accessible with microscopy-based techniques. The quantitative agreement between DFT simulations and the experimental INS spectrum combined with the relative simplicity of sample preparation, suggests that this methodology may become part of the standard and preferred protocol for the characterization of MOFs, and, in particular, for elucidating the structure defects in these materials.

Interconversion of Atomically Dispersed Platinum Cations and Platinum Clusters in Zeolite ZSM-5 and Formation of Platinum gem-Dicarbonyls.

Catalysts composed of platinum dispersed on zeolite supports are widely applied in industry, and coking and sintering of platinum during operation under reactive conditions require their oxidative regeneration, with the platinum cycling between clusters and cations. The intermediate platinum species have remained only incompletely understood. Here, we report an experimental and theoretical investigation of the structure, bonding, and local environment of cationic platinum species in zeolite ZSM-5, which are key intermediates in this cycling. Upon exposure of platinum clusters to O2 at 700 °C, oxidative fragmentation occurs, and Pt2+ ions are stabilized at six-membered rings in the zeolite that contain paired aluminum sites. When exposed to CO under mild conditions, these Pt2+ ions form highly uniform platinum gem-dicarbonyls, which can be converted in H2 to Ptδ+ monocarbonyls. This conversion, which weakens the platinum-zeolite bonding, is a first step toward platinum migration and aggregation into clusters. X-ray absorption and infrared spectra provide evidence of the reductive and oxidative transformations in various gas environments. The chemistry is general, as shown by the observation of platinum gem-dicarbonyls in several commercially used zeolites (ZSM-5, Beta, mordenite, and Y).

Atomically Dispersed Platinum in Surface and Subsurface Sites on MgO Have Contrasting Catalytic Properties for CO Oxidation.

Atomically dispersed metals on metal oxide supports are a rapidly growing class of catalysts. Developing an understanding of where and how the metals are bonded to the supports is challenging because support surfaces are heterogeneous, and most reports lack a detailed consideration of these points. Herein, we report two atomically dispersed CO oxidation catalysts having markedly different metal-support interactions: platinum in the first layer of crystalline MgO powder and platinum in the second layer of this support. Structural models have been determined on the basis of data and computations, including those determined by extended X-ray absorption fine structure and X-ray absorption near edge structure spectroscopies, infrared spectroscopy of adsorbed CO, and scanning transmission electron microscopy. The data demonstrate the transformation of surface to subsurface platinum as the temperature of sample calcination increased. Catalyst performance data demonstrate the lower activity but greater stability of the subsurface platinum than of the surface platinum.

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts.

Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically dispersed platinum supported on crystalline MgO (chosen for its well-defined bonding sites) as a prototypical example, we demonstrate how systematic density functional theory calculations for assessing all the potentially stable platinum sites, combined with automated analysis of extended X-ray absorption fine structure (EXAFS) spectra, leads to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging and EXAFS and high-energy resolution fluorescence detection X-ray absorption near edge spectroscopy. The proposed platinum sites are in agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active site than what is currently possible with conventional EXAFS analyses. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities.

Understanding trends in C-H bond activation in heterogeneous catalysis.

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

DFT-Derived Force Fields for Modeling Hydrocarbon Adsorption in MIL-47(V).

Generic force fields such as UFF and DREIDING are widely used for predicting molecular adsorption and diffusion in metal-organic frameworks (MOFs), but the accuracy of these force fields is unclear. We describe a general framework for developing transferable force fields for modeling the adsorption of alkanes in a nonflexible MIL-47(V) MOF using periodic density functional theory (DFT) calculations. By calculating the interaction energies for a large number of energetically favorable adsorbate configurations using DFT, we obtain a force field that gives good predictions of adsorption isotherms, heats of adsorption, and diffusion properties for a wide range of alkanes and alkenes in MIL-47(V). The force field is shown to be transferable to related materials such as MIL-53(Cr) and is used to calculate the free-energy differences for the experimentally observed phases of MIL-53(Fe).

Role of amine structure on carbon dioxide adsorption from ultradilute gas streams such as ambient air.

A fundamental study on the adsorption properties of primary, secondary, and tertiary amine materials is used to evaluate what amine type(s) are best suited for ultradilute CO(2) capture applications. A series of comparable materials comprised of primary, secondary, or tertiary amines ligated to a mesoporous silica support via a propyl linker are used to systematically assess the role of amine type. Both CO(2) and water adsorption isotherms are presented for these materials in the range relevant to CO(2) capture from ambient air and it is demonstrated that primary amines are the best candidates for CO(2) capture from air. Primary amines possess both the highest amine efficiency for CO(2) adsorption as well as enhanced water affinity compared to other amine types or the bare silica support. The results suggest that the rational design of amine adsorbents for the extraction of CO(2) from ambient air should focus on adsorbents rich in primary amines.