Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Dynamic Active Sites In Situ Formed in Metal Nanoparticle Reshaping under Reaction Conditions

Understanding the nonequilibrium transformation of nanocatalysts under reaction conditions is important because metastable atomic structures may be created during the process, which offers unique activities in reactions. Although reshaping of metal nanoparticles (NPs) under reaction conditions has been widely recognized, the dynamic reshaping process has been less studied at the atomic scale. Here, we develop an atomistic kinetic Monte Carlo model to simulate the complete reshaping process of Pt nanoparticles in a CO environment and reveal the in situ formation of atomic clusters on the NP surface, a new type of active site beyond conventional understanding, boosting the reactivities in the CO oxidation reaction. Interestingly, highly active peninsula and inactive island clusters both form on the (111) facets and interchange in varying states of dynamic equilibrium, which influences the catalytic activities significantly. This study provides new fundamental knowledge of nanocatalysis and new guidance for the rational design of nanocatalysts.

Visualizing Dynamic Single Atom Catalysis

Many industrial chemical processes, including for producing fuels, foods, pharmaceuticals, chemicals and environmental controls, employ heterogeneous solid state catalysts at elevated temperatures in gas or liquid environments. Dynamic reactions at the atomic level play a critical role in catalyst stability and functionality. In situ visualization and analysis of atomic-scale processes in real time under controlled reaction environments can provide important insights into practical frameworks to improve catalytic processes and materials. This review focuses on innovative real time in situ electron microscopy (EM) methods, including recent progress in analytical in situ environmental (scanning) transmission EM (E(STEM), incorporating environmental scanning TEM (ESTEM) and environmental transmission EM (ETEM), with single atom resolution for visualizing and analysing dynamic single atom catalysis under controlled flowing gas reaction environments. ESTEM studies of single atom dynamics of reactions, and of sintering deactivation, contribute to a better-informed understanding of the yield and stability of catalyst operations. Advances in in situ technologies, including gas and liquid sample holders, nanotomography, and higher voltages, as well as challenges and opportunities in tracking reacting atoms, are highlighted. The findings show that the understanding and application of fundamental processes in catalysis can be improved, with valuable economic, environmental, and societal benefits.

Site-Recognition-Induced Structural and Photoluminescent Evolution of the Gold-Pincer Nanocluster

The induced structural transformation provides an efficient way to precisely modulate the fine structures and the corresponding performance of gold nanoclusters, thus constituting one of the important research topics in cluster chemistry. However, the driving forces and mechanisms of these processes are still ambiguous in many cases, limiting further applications. In this work, based on the unique coordination mode of the pincer ligand-stabilized gold nanocluster Au8(PNP)4, we revealed the site-recognition mechanism for induced transformations of gold nanoclusters. The "open nitrogen sites" on the surface of the nanocluster interact with different inducers including organic compounds and metals and trigger the conversion of Au8(PNP)4 to Au13 and Au9Ag4 nanoclusters, respectively. Control experiments verified the site-recognition mechanism, and the femtosecond and nanosecond transient absorption spectroscopy revealed the electronic and photoluminescent evolution accompanied by the structural transformation.

Unexpectedly high thermal stability of Au nanotriangle@mSiO2 yolk-shell nanoparticles

The shape of Au nanoparticles (NPs) plays a crucial role for applications in, amongst others, catalysis, electronic devices, biomedicine, and sensing. Typically, the deformation of the morphology of Au NPs is the most significant cause of loss of functionality. Here, we systematically investigate the thermal stability of Au nanotriangles (NTs) coated with (mesoporous) silica shells with different morphologies (core-shell (CS): Au NT@mSiO2/yolk-shell (YS): Au NT@mSiO2) and compare these to 'bare' nanoparticles (Au NTs), by a combination of in situ and/or ex situ TEM techniques and spectroscopy methods. Au NTs with a mesoporous silica (mSiO2) coating were found to show much higher thermal stability than those without a mSiO2 coating, as the mSiO2 shell restricts the (self-)diffusion of surface atoms. For the Au NT@mSiO2 CS and YS NPs, a thicker mSiO2 shell provides better protection than uncoated Au NTs. Surprisingly, the Au NT@mSiO2 YS NPs were found to be as stable as Au NT@mSiO2 CS NPs with a core-shell morphology. We hypothesize that the only explanation for this unexpected finding was the thicker and higher density SiO2 shell of YS NPs that prevents diffusion of Au surface atoms to more thermodynamically favorable positions.

Environmental TEM Study of the Dispersion of Au/α-MoC: From Nanoparticles to Two-Dimensional Clusters

The synthesis of highly dispersed Au nanoclusters that are stable under elevated temperatures in heterogeneous catalysis is challenging. Here, we directly observe a strong metal-support interaction (SMSI)-induced dispersion of Au nanoparticles (NPs) on α-MoC using an environmentally atomically resolved secondary imaging technique. Under a realistic environment, Au NPs flatten and spread out on the α-MoC to form two-dimensional atomic layered clusters. The formed highly dispersed Au/α-MoC catalyst shows excellent stability at 600 °C for 160 h in the reverse water-gas shift reaction. The X-ray photoelectron spectrum and extended X-ray absorption fine structure results show that Au NPs gradually become low-coordination-number cluster species and lose electrons to become Auδ+; these form chemical bonds with the α-MoC support and are responsible for the dispersion behavior. This work provides an insightful understanding of dispersion behavior and promotes the rational design and synthesis of reverse sintering catalysts.

Catalytic NO Reduction by NO Pre-Adsorbed RhCeO2 NO- Clusters

A fundamental understanding on the dynamically structural evolution of catalysts induced by reactant gases under working conditions is challenging but pivotal in catalyst design. Herein, in combination with state-of-the-art mass spectrometry for cluster reactions, cryogenic photoelectron imaging spectroscopy, and quantum-chemical calculations, we identified that NO adsorption on rhodium-cerium bimetallic oxide cluster RhCeO₂ ^- can create a Ce³⁺ ion in product RhCeO₂ NO^- that serves as the starting point to trigger the catalysis of NO reduction by CO. Theoretical calculations substantiated that the reduction of another two NO molecules into N₂ O takes place exclusively on the Ce³⁺ ion while Rh behaves like a promoter to buffer electrons and cooperates with Ce³⁺ to drive NO reduction. Our finding demonstrates the importance of NO in regulating the catalytic behavior of Rh under reaction conditions and provides much-needed insights into the essence of NO reduction over Rh/CeO₂ , one of the most efficient components in three-way catalysts for NO_x removal.

Toward a new definition of surface energy for late transition metals

Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined density functional theory (DFT) calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model predicting the average surface energy of metal structures independently of their shape. The metals considered are W, Ru, Co, Ir, Ni, Pd, Pt, Cu, Ag and Au. Lorentzian trends derived from the DFT data proved effective at predicting the surface energy of metallic surfaces but not for metal clusters. We used machine-learning protocols to build an algorithm that improves the Lorentzian trend's accuracy and is able to predict the surface energies of metal surfaces of any crystal structure, i.e., face-centred cubic, hexagonal close-packed, and body-centred cubic, but also of nanostructures and sub-nanometer clusters. The machine-learning neural network takes easy-to-compute geometric features to predict metallic moieties surface energies with a mean absolute error of 0.091 J m^-2 and an R² score of 0.97.

Identifying the morphology of Pt nanoparticles for the optimal catalytic activity towards CO oxidation

The morphology of nanoparticles (NPs) is crucial for determining their catalytic performance. The dramatic changes in the morphology of metal NPs during reactions observed in many in situ experiments pose great challenges for the identification of the geometry for optimal catalytic activities, which arouses the controversial understanding of the reaction mechanism. In this work, taking CO oxidation as a model reaction, we coupled a multiscale structure reconstruction model with kinetic Monte Carlo simulations to study the catalytic performance of the Pt NPs with changing morphology and reaction conditions. Through the quantitative analysis of contour plots for turnover frequencies, we show that the NPs with more well-coordinated sites exhibit optimal activity under CO-rich conditions at higher temperatures, while the reactivity of NPs with more low-coordination sites is optimal under O₂-rich conditions at lower temperatures. Further analysis indicates that the competitive adsorption of CO and O₂ plays the key role, in which the structure with optimal activity has a closer CO and O coverage. This work not only reconciles the controversy of the active geometry in the experiments, but offers an efficient method to guide the rational design of high-performance catalysts.

Periodic structural changes in Pd nanoparticles during oscillatory CO oxidation reaction

Nanoparticle (NP) catalysts are ubiquitous in energy systems, chemical production, and reducing the environmental impact of many industrial processes. Under reactive environments, the availability of catalytically active sites on the NP surface is determined by its dynamic structure. However, atomic-scale insights into how a NP surface reconstructs under reaction conditions and the impact of the reconstruction on catalytic activity are still lacking. Using operando transmission electron microscopy, we show that Pd NPs exhibit periodic round-to-flat transitions altering their facets during CO oxidation reaction at atmospheric pressure and elevated temperatures. This restructuring causes spontaneous oscillations in the conversion of CO to CO₂ under constant reaction conditions. Our study reveals that the oscillatory behavior stems from the CO-adsorption-mediated periodic restructuring of the nanocatalysts between high-index-faceted round and low-index-faceted flat shapes. These atomic-scale insights into the dynamic surface properties of NPs under reactive conditions play an important role in the design of high-performance catalysts.

Unveiling the Au Surface Reconstruction in a CO Environment by Surface Dynamics and Ab Initio Thermodynamics

Surface reconstruction changes the atomic configuration of the metal surface and thus alters its intrinsic physical and chemical properties. Recent in situ experiments have shown a variety of surface reconstructions under reaction conditions, but how to effectively predict and characterize these structures remains challenging. Herein, we combine a DFT-based kinetic Monte Carlo simulation method and ab initio thermodynamics to explore the low-energy configurations of metal surface reconstructions, which takes the surface dynamics under the reactive environment into account. We systematically simulate 13 Au surfaces ((100), (110), (111), (210), (211), (221), (310), (311), (320), (321), (322), (331), and (332)) in the CO environment and identify 19 candidate reconstruction patterns driven by CO adsorption. The breakup of the original surfaces is attributed to the lateral interactions among the nearest-neighboring adsorbates. This work provides an efficient approach to unveil the reconstructed metal surface structures in reactive environments for guiding the experiments.

Design of CuCs-doped Ag-based catalyst for ethylene epoxidation

Our recent theoretical studies have screened out CuCs-doped Ag-based promising catalysts for ethylene epoxidation [ACS Catal. 11, 3371 (2021)]. The theoretical results were based on surface modeling, while in the actual reaction process Ag catalysts are particle shaped. In this work, we combine density functional theory (DFT), Wulff construction theory, and micro kinetic analysis to study the catalytic performance of Ag catalysts at the particle model. It demonstrates that the CuCs-doped Ag catalysts are superior to pure Ag catalysts in terms of selectivity and activity, which is further proved by experimental validation. The characterization analysis finds that both Cu and Cs dopant promote particle growth as well as particle dispersion, resulting in a grain boundary-rich Ag particle. Besides, CuCs also facilitate electrophilic atomic oxygen formation on catalyst surface, which is benefitial for ethylene oxide formation and desorption. Our work provides a case study for catalyst design by combining theory and experiment.

Revealing Surface Restraint-Induced Hexagonal Pd Nanocrystals via In Situ Transmission Electron Microscopy

Achieving metal nanocrystals with metastable phase draws much attention due to their anticipated fascinating properties, wheras it is still challenging because their polymorphism nature and phase transition mechanism remain elusive. Here, phase stability of face-centered cubic (fcc) Pd nanocrystals was studied via in situ spherical aberration (Cs)-corrected transmission electron microscopy (TEM). By constructing a well-defined Pd/C composite structure, Pd nanocrystals encapsulated by graphite, the dispersion process of fcc Pd was observed through a nucleation and growth process. Interestingly, Cs-corrected scanning TEM analysis demonstrated that the newly formed Pd nanocrystals could adopt a metastable hexagonal phase, which was considered challenging to obtain. Accordingly, formation mechanism of the hexagonal Pd nanocrystals was proposed, which involved the combined effect of two factors: (1) templating of graphite and (2) size effect. This work is expected to offer new insight into the polymorphism of Pd nanocrystals and pave the way for the future design of metastable metal nanomaterials.

Atomic level fluxional behavior and activity of CeO2-supported Pt catalysts for CO oxidation

Reducible oxides are widely used catalyst supports that can increase oxidation reaction rates by transferring lattice oxygen at the metal-support interface. There are many outstanding questions regarding the atomic-scale dynamic meta-stability (i.e., fluxional behavior) of the interface during catalysis. Here, we employ aberration-corrected operando electron microscopy to visualize the structural dynamics occurring at and near Pt/CeO₂ interfaces during CO oxidation. We show that the catalytic turnover frequency correlates with fluxional behavior that (a) destabilizes the supported Pt particle, (b) marks an enhanced rate of oxygen vacancy creation and annihilation, and (c) leads to increased strain and reduction in the CeO₂ support surface. Overall, the results implicate the interfacial Pt-O-Ce bonds anchoring the Pt to the support as being involved also in the catalytically-driven oxygen transfer process, and they suggest that oxygen reduction takes place on the highly reduced CeO₂ surface before migrating to the interfacial perimeter for reaction with CO.

Construction of a Unique Structure of Ru Sites in the RuP Structure for Propane Dehydrogenation

It is an important task to develop low-cost and anticoking catalysts for the propane dehydrogenation (PDH) reaction. In this work, the P element is introduced to the Ru-based catalyst to obtain Ru sites with a unique structure and the obtained Ru_xP_y (x/y = 2:1, 1:1, 1:2) catalysts are then employed in PDH. Density functional theory (DFT) results show that the addition of P leads to the formation of separated Ru sites and the adjustment of the valance band state of Ru. The upward shift of the d-band center leads to a reduction of the reaction energy barrier for dehydrogenation of propane and an enhancement of catalytic activity. The analysis of the competition between propylene deep dehydrogenation and propylene desorption for each catalyst shows that desorption of propylene is preferred on the RuP(112) surface. Considering both catalytic activity and propylene selectivity, the RuP catalyst is potential for the propane dehydrogenation reaction. On the RuP surface, the PDH reaction proceeds by the dehydrogenation of the H atom on the methylene group (isopropyl pathway), thus restraining the deep dehydrogenation of propylene. The Ru_xP_y catalysts are also synthesized in experiments, and PDH evaluation shows that the RuP structure is a remarkable PDH catalyst with a stable structure, anticoking ability, and low cost.

Directing reaction pathways via in situ control of active site geometries in PdAu single-atom alloy catalysts

The atomic scale structure of the active sites in heterogeneous catalysts is central to their reactivity and selectivity. Therefore, understanding active site stability and evolution under different reaction conditions is key to the design of efficient and robust catalysts. Herein we describe theoretical calculations which predict that carbon monoxide can be used to stabilize different active site geometries in bimetallic alloys and then demonstrate experimentally that the same PdAu bimetallic catalyst can be transitioned between a single-atom alloy and a Pd cluster phase. Each state of the catalyst exhibits distinct selectivity for the dehydrogenation of ethanol reaction with the single-atom alloy phase exhibiting high selectivity to acetaldehyde and hydrogen versus a range of products from Pd clusters. First-principles based Monte Carlo calculations explain the origin of this active site ensemble size tuning effect, and this work serves as a demonstration of what should be a general phenomenon that enables in situ control over catalyst selectivity.

Nanocrystallites, adsorption, surface tension, and Wulff rule

Chemisorption on the surface of metal nanocrystallites (NCs) sometimes induces their reshaping. This interesting phenomenon was observed experimentally in various systems. Related theoretical studies imply that it can be described using the Wulff rule with the surface tension dependent on the coverage of the NC facets by adsorbate. There is, however, no agreement as to how the surface tension should be calculated in this case. Relying on the laws of statistical physics, I clarify the situation in this area in general and also in the framework of the mean-field approximation in three situations: (i) with adsorption-desorption equilibrium, (ii) with a fixed amount of adsorbate at a NC, and (iii) with a fixed amount of adsorbate at facets of a NC. Under these conditions, the surface tension is shown to be described by the same expressions.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

Propane dehydrogenation: catalyst development, new chemistry, and emerging technologies

This review describes recent advances in the propane dehydrogenation process in terms of emerging technologies, catalyst development and new chemistry.

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pd_n (n = 1-21) clusters supported on CeO₂(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pd_n clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

Structural changes in noble metal nanoparticles during CO oxidation and their impact on catalyst activity

The dynamical structure of a catalyst determines the availability of active sites on its surface. However, how nanoparticle (NP) catalysts re-structure under reaction conditions and how these changes associate with catalytic activity remains poorly understood. Using operando transmission electron microscopy, we show that Pd NPs exhibit reversible structural and activity changes during heating and cooling in mixed gas environments containing O₂ and CO. Below 400 °C, the NPs form flat low index facets and are inactive towards CO oxidation. Above 400 °C, the NPs become rounder, and conversion of CO to CO₂ increases significantly. This behavior reverses when the temperature is later reduced. Pt and Rh NPs under similar conditions do not exhibit such reversible transformations. We propose that adsorbed CO molecules suppress the activity of Pd NPs at lower temperatures by stabilizing low index facets and reducing the number of active sites. This hypothesis is supported by thermodynamic calculations.