Solid-to-liquid phase transitions of sub-nanometer clusters enhance chemical transformation

Entropy in catalyst dynamics under confinement

Entropy during the dynamic structural evolution of catalysts has a non-trivial influence on chemical reactions. Confinement significantly affects the catalyst dynamics and thus impacts the reactivity. However, a full understanding has not been clearly established. To investigate catalyst dynamics under confinement, we utilize the active learning scheme to effectively train machine learning potentials for computing free energies of catalytic reactions. The scheme enables us to compute the reaction free energies and entropies of O2 dissociation on Pt clusters with different sizes confined inside a carbon nanotube (CNT) at the timescale of tens of nanoseconds while keeping ab initio accuracy. We observe an entropic effect owing to liquid-to-solid phase transitions of clusters at finite temperatures. More importantly, the confinement effect enhances the structural dynamics of the cluster and leads to a lower melting temperature than those of the bare cluster and cluster outside the CNT, consequently facilitating the reaction to occur at lower temperatures and preventing the catalyst from forming unfavorable oxides. Our work reveals the important influence of confinement on structural dynamics, providing useful insight into entropy in dynamic catalysis.

Regulating Contact-Electro-Catalysis Using Polymer/Metal Janus Composite Catalysts

Interfacial contact electrification can catalyze redox reactions through a process called contact-electro-catalysis (CEC). The two main reaction paths for producing reactive oxygen species via CEC are the water oxidation reaction (WOR) and the oxygen reduction reaction (ORR). Herein, we designed a polymer/metal Janus composite catalyst that regulated the reaction rates of the WOR and ORR based on the catalyst composition. The ORR was preferentially enhanced when the polymer was negatively charged during contact electrification, while the WOR was preferentially enhanced when the polymer was positively charged. This phenomenon was observed for various conductive materials. The increase in the enhancement of the reaction rates depended on the conductivity and work function of the metal. We expect that this efficient CEC method can form a universal strategy for improving the performance of existing catalysts, as contact electrification is common in nature.

Going for Gold(-Standard): Attaining Coupled Cluster Accuracy in Oxide-Supported Nanoclusters

The structure of oxide-supported metal nanoclusters plays an essential role in their sharply enhanced catalytic activity over that of bulk metals. Simulations provide the atomic-scale resolution needed to understand these systems. However, the sensitive mix of metal-metal and metal-support interactions, which govern their structure, puts stringent requirements on the method used, requiring calculations beyond standard density functional theory (DFT). The method of choice is coupled cluster theory [specifically CCSD(T)], but its computational cost has so far prevented its application to these systems. In this work, we showcase two approaches to make CCSD(T) accuracy readily achievable in oxide-supported nanoclusters. First, we leverage the SKZCAM protocol to provide the first benchmarks of oxide-supported nanoclusters, revealing that it is specifically metal-metal interactions that are challenging to capture with DFT. Second, we propose a CCSD(T) correction (ΔCC) to the metal-metal interaction errors in DFT, reaching accuracy comparable to that of the SKZCAM protocol at significantly lower cost. This approach forges a path toward studying larger systems at reliable accuracy, which we highlight by identifying a ground-state structure in agreement with experiments for Au20 on MgO, a challenging system where DFT models have yielded conflicting predictions.

Pd8 Cluster: Too Small to Melt? A BOMD Study

The question of whether a solid-liquid phase transition occurs in small clusters poses a fundamental challenge. In this study, we attempt to elucidate this phenomenon through a thorough examination of the thermal behavior and structural stability of Pd8 clusters employing ab initio simulations. Initially, a systematic global search is carried out to identify the various isomers of the Pd8 cluster. This is accomplished by employing an ab initio basin-hopping algorithm and using the PBE/SDD scheme integrated in the Gaussian code. The resulting isomers are further refined through reoptimization using the deMon2k package. To ensure the structural firmness of the lowest-energy isomer, we calculated normal modes. The structural stability as a function of temperature is analyzed through the Born-Oppenheimer molecular dynamics (BOMD) approach. Multiple BOMD trajectories at distinct simulated temperatures are examined with data clustering analysis to determine cluster isomers. This analysis establishes a connection between the potential energy landscape and the simulated temperature. To address the question of cluster melting, canonical parallel-tempering BOMD runs are performed and analyzed with the multiple-histogram method. A broad maximum in the heat capacity curve indicates a melting transition between 500 and 600 K. To further examine this transition, the mean-squared displacement and the pair-distance distribution function are calculated. The results of these calculations confirm the existence of a solid-liquid phase transition, as indicated by the heat capacity curve.

Tailoring the catalytic activity and selectivity on CO2 to C1 products by the synergistic effect of reactive molecules: A DFT study

The conversion of CO2 to CO is one of the crucial pathways in the carbon dioxide reduction reaction (CO2RR). Iron and nitrogen co-doped carbon matrix (FeN4) is a promising catalyst for converting CO2to CO with excellent activity and selectivity. However, the reactive mechanism of CO2RR on the FeN4 catalyst is not fully unveiled. For example, it is still evasive that the obtained C1 product is methanol and/or methane instead of CO in some cases. Herein, DFT calculation is conducted to unravel the effect from both solvent molecules and intermediates as axial groups on the selectivity of C1 products in CO2RR using FeN4 catalyts. Calculation results demonstrate that the FeN4(H), FeN4(OH), FeN4(COOH), and FeN4(CO) configurations are not only beneficial to the removal of CO, but also effectively suppress the hydrogen evolution reaction, whereas the FeN4, FeN4(CO2) and FeN4(H2O) configurations are inclined to produce CH3OH and/or CH4. The mechanism studied in this work provides an inspiration of optimizing the selectivity of C1 products in CO2RR from the perspective of regulating solvent molecules and intermediates as axial groups on FeN4.

Neural Network Accelerated Investigation of the Dynamic Structure-Performance Relations of Electrochemical CO2 Reduction over SnO x Surfaces

Heterogeneous catalysts, especially metal oxides, play a curial role in improving energy conversion efficiency and production of valuable chemicals. However, the surface structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. This paper describes a strategy of the multiscale simulation to investigate the SnO _x reduction process and to build a structure-performance relation of SnO _x for CO₂ electroreduction. Employing high-dimensional neural network potential accelerated molecular dynamics and stochastic surface walking global optimization, coupled with density functional theory calculations, we propose that SnO₂ reduction is accompanied by surface reconstruction and charge density redistribution of active sites. A regulatory factor, the net charge, is identified to predict the adsorption capability for key intermediates on active sites. Systematic electronic analyses reveal the origin of the interaction between the adsorbates and the active sites. These findings uncover the quantitative correlation between electronic structure properties and the catalytic performance of SnO _x so that Sn sites with moderate charge could achieve the optimally catalytic performance of the CO₂ electroreduction to formate.

Frustrations of supported catalytic clusters under operando conditions predicted by a simple lattice model

The energy landscape with a number of close minima separated by low barriers is a well-known issue in computational heterogeneous catalysis. In the framework of the emerging out-of-equilibrium material science, the navigation through such involved landscapes is associated with the functionality of materials. Current advancements in the cluster catalysis has brought and continues to bring essential nuances to the topic. One of them is the possibility of frustration of the catalytic centre under operando conditions. However, this conjecture is difficult to check either experimentally or theoretically. As a step in this direction, as-simple-as-possible lattice model is used to estimate how the supposed frustrations may couple with the elementary reaction and manifest themselves at the macroscopic scale.

Structural dynamics of Ru clusters during nitrogen dissociation in ammonia synthesis

The dynamic evolution of catalyst structures greatly influences the reactivity, especially sub-nanometer clusters, exhibiting complex configurational fluctuation. In the present work, we study the structural dynamics of a Ru₁₉ cluster during the dissociation of N₂ and calculate the reaction free energies using ab initio molecular dynamics (AIMD). Our AIMD calculation predicts a peak-shaped reaction entropy curve due to the adsorption-induced phase transition of the Ru₁₉ cluster. The low melting points of sub-nanometer clusters make it possible to activate N₂ at low temperatures. This work demonstrates that the dynamic changes of cluster structures have a non-negligible effect on reaction free energy and offer an opportunity for achieving ammonia synthesis under mild conditions.

Size-dependent phase transitions boost catalytic activity of sub-nanometer gold clusters

The characterization and identification of the dynamics of cluster catalysis are crucial to unraveling the origin of catalytic activity. However, the dynamical catalytic effects during the reaction process remain unclear. Herein, we investigate the dynamic coupling effect of elementary reactions with the structural fluctuations of sub-nanometer Au clusters with different sizes using ab initio molecular dynamics and the free energy calculation method. It was found that the adsorption-induced solid-to-liquid phase transitions of the cluster catalysts give rise to abnormal entropy increase, facilitating the proceeding of reaction, and this phase transition catalysis exists in a range of clusters with different sizes. Moreover, clusters with different sizes show different transition temperatures, resulting in a non-trivial size effect. These results unveil the dynamic effect of catalysts and help understand cluster catalysis to design better catalysts rationally.

Recent advances in plasmon-enhanced Raman spectroscopy for catalytic reactions on bifunctional metallic nanostructures

Metallic nanostructures exhibit superior catalytic performance for diverse chemical reactions and the in-depth understanding of reaction mechanisms requires versatile characterization methods. Plasmon-enhanced Raman spectroscopy (PERS), including surface-enhanced Raman spectroscopy (SERS), shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and tip-enhanced Raman spectroscopy (TERS), appears as a powerful technique to characterize the Raman fingerprint information of surface species with high chemical sensitivity and spatial resolution. To expand the range of catalytic reactions studied by PERS, catalytically active metals are integrated with plasmonic metals to produce bifunctional metallic nanostructures. In this minireview, we discuss the recent advances in PERS techniques to probe the chemical reactions catalysed by bifunctional metallic nanostructures. First, we introduce different architectures of these dual-functionality nanostructures. We then highlight the recent works using PERS to investigate important catalytic reactions as well as the electronic and catalytic properties of these nanostructures. Finally, we provide some perspectives for future PERS studies in this field.

Complex Reaction Network Thermodynamic and Kinetic Autoconstruction Based on Ab Initio Statistical Mechanics: A Case Study of O2 Activation on Ag4 Clusters

An approach based on ab initio statistical mechanics is demonstrated for autoconstructing complex reaction networks. Ab initio molecular dynamics combined with Markov state models are employed to study relevant transitions and corresponding thermodynamic and kinetic properties of a reaction. To explore the capability and flexibility of this approach, we present a study of oxygen activation on Ag4 as a model reaction. Specifically, with the same sampled trajectories, it is possible to study the structural effects and the reaction rate of the cited reaction. The results show that this approach is suitable for automatized construction of reaction networks, especially for non-well-studied reactions, which can benefit from this ab initio molecular dynamics based approach to construct comprehensive reaction networks with Markov state models without prior knowledge about the potential energy landscape.

Size-Sensitive Dynamic Catalysis of Subnanometer Cu Clusters in CO2 Dissociation

Small cluster catalysts are highly size-dependent and exhibit complex structural dynamic effects during catalytic reactions. Understanding their structural dynamics is of great importance in tuning the catalytic performances of small clusters that widely exist in supported catalysts. However, very little is known about the size dependence of the dynamic effect of small clusters. In this work, we systematically study the free energies and barriers of catalytic dissociation of CO₂ at different temperatures on dynamical Cu clusters with different sizes by ab initio molecular dynamics. The reaction shows an abnormal entropic effect on Cu clusters, and more interestingly, it shows size sensitivity. On the Cu₇ cluster, the entropy curve shows a reverse peak shape with increasing temperature, and it is surprising to find that it has a complex pulse shape on the Cu₁₉ cluster. The detailed analysis shows that such temperature dependences can be attributable to the nontrivial behaviors of adsorption-induced phase transitions of the subnanometer Cu clusters during the dissociation of CO₂. Our work not only demonstrates the complexity of the temperature dependence of the surface reaction on cluster sizes but also provides useful insight into the phase transition catalysis of dynamic clusters.

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium

In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states a priori. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction-the dissociation of molecular nitrogen on ruthenium-at density-functional-theory-level accuracy. We find that the vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an "on-top" adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects.

Adsorption-Induced Liquid-to-Solid Phase Transition of Cu Clusters in Catalytic Dissociation of CO2

Sub-nanometer metal clusters widely existing in catalysts have a large ensemble of metastable isomers that can interconvert during catalytic reactions, exhibiting complex dynamical catalytic effects. In this work, we systematically investigate the temperature dependent structural dynamics of the Cu₁₃ cluster in CO₂ dissociation using ab initio molecular dynamics and the free energy calculation method. We find an abnormal entropic effect due to adsorption-induced liquid-to-solid phase transition of the cluster during the course of the elementary dissociation step at transition temperatures. In the dissociation product, the formation of a rigid Cu₃O unit decreases the dynamical fluidity of the cluster and increases the melting temperature, causing a decrease in the entropy of the dissociation product. Our work demonstrates the nontrivial effects of surface adsorption on phase transition behaviors of dynamic clusters and offers a new perspective to dynamic catalysis.

Polynuclear organometallic clusters: synthesis, structure, and reactivity studies

This feature article highlights our recent advances in the controllable synthesis of carbon-centered polynuclear organometallic clusters: from synthesis to transformation, reactivity and mechanism.

An approach to calculate the free energy changes of surface reactions using free energy decomposition on ab initio brute-force molecular dynamics trajectories

To understand the mechanisms and kinetics of catalytic reactions in heterogeneous catalysis, ab initio molecular dynamics is one of the powerful methods used to explore the free energy surface (FES) of surface elementary steps.