A first-principles microkinetic study on the hydrogenation of carbon dioxide over Cu(211) in the presence of water

Catalysis Enhanced by Catalyst Wettability

Heterogeneous catalysis is a surface phenomenon where the adsorption, desorption, and transfer of reactants and products are critical for catalytic performance. Recent results show that catalyst wettability is strongly related to the adsorption, desorption, and transfer of reactants and products. In this review, we briefly summarize strategies for regulating wettability to enrich reactants, accelerate the desorption of products, and promote mass transfer in heterogeneous catalysis. In addition, we explore insights into catalyst wettability for the enhancement of catalytic performance. Finally, the concerns and challenges in this subject are outlined, and practical strategies are proposed for the regulation of catalyst wettability. We hope that this review will be helpful for designing highly efficient heterogeneous catalysts in the future.

Adjustment of Molecular Sorption Equilibrium on Catalyst Surface for Boosting Catalysis

ConspectusFor chemical reactions with complex pathways, it is extremely difficult to adjust the catalytic performance. The previous strategies on this issue mainly focused on modifying the fine structures of the catalysts, including optimization of the geometric/electronic structure of the metal nanoparticles (NPs), regulation of the chemical composition/morphology of the supports, and/or adjustment of the metal-support interactions to modulate the reaction kinetics on the catalyst surface. Although significant advances have been achieved, the catalytic performance is still unsatisfactory.It is accepted that the chemical equilibrium of a reaction can be disturbed by changing the concentration of the reactants or products, and the equilibrium will shift to another side to offset the perturbation until a new equilibrium is established. This is known as Le Chatelier's principle. Following this understanding, we show that the catalytic performance can be significantly modulated by adjusting the molecular sorption equilibrium on the catalyst surface. For example, enriching the reactants and/or intermediates on the catalyst surface pushes the reaction forward, thus increasing the catalytic conversion; removing the product away from the catalyst surface improves the catalytic conversion and product selectivity; and inhibiting the side reactions enhances the product selectivity and catalyst durability. Using these strategies has successfully enhanced the catalytic performances in many challenging reactions, such as increasing H2O2 concentration around the metal active sites to enhance methane oxidation, enriching olefin on the catalyst surface to boost hydroformylation, selective combustion of H2 to shift the reaction equilibrium and improve ethane conversion in ethane dehydrogenation, and removing water from the reaction system to enhance Fischer-Tropsch synthesis. The key to these successes is effectively shifting the molecular sorption equilibrium under the working conditions.In this Account, we briefly summarize recent advances in adjusting molecular sorption equilibrium for boosting catalysis, with a focus on the equilibrium shift for a desired pathway by the unique functions of zeolites and polymers such as silanol nests on zeolite for olefin adsorption, the "molecular fence" effect of zeolite for H2O2 enrichment, MFI zeolite nanosheets for olefin diffusion, and the hydrophobic zeolite sheath and polymer for water separation/diffusion. We report the adjustment of the molecular sorption equilibrium on the catalyst surface via enriching the reactants and intermediates, removing the products, and inhibiting the side reactions to enhance the catalytic performance. As a result, high activity, excellent selectivity, and outstanding durability of the catalysts were achieved. In addition, current challenges and perspectives of applying this strategy to more important industrial reactions are discussed. Applications of advanced characterization tools, machine learning, and artificial intelligence for monitoring the dynamic structural changes of the catalyst and predicting the structural evolutions under working conditions are anticipated to continuously play important roles in catalyst design. We believe that this strategy will open a door for the development of highly efficient catalysts with potential applications in the future.

Understanding and Tuning the Effects of H2O on Catalytic CO and CO2 Hydrogenation

Catalytic COx (CO and CO2) hydrogenation to valued chemicals is one of the promising approaches to address challenges in energy, environment, and climate change. H2O is an inevitable side product in these reactions, where its existence and effect are often ignored. In fact, H2O significantly influences the catalytic active centers, reaction mechanism, and catalytic performance, preventing us from a definitive and deep understanding on the structure-performance relationship of the authentic catalysts. It is necessary, although challenging, to clarify its effect and provide practical strategies to tune the concentration and distribution of H2O to optimize its influence. In this review, we focus on how H2O in COx hydrogenation induces the structural evolution of catalysts and assists in the catalytic processes, as well as efforts to understand the underlying mechanism. We summarize and discuss some representative tuning strategies for realizing the rapid removal or local enrichment of H2O around the catalysts, along with brief techno-economic analysis and life cycle assessment. These fundamental understandings and strategies are further extended to the reactions of CO and CO2 reduction under an external field (light, electricity, and plasma). We also present suggestions and prospects for deciphering and controlling the effect of H2O in practical applications.

Origin of Autocatalytic Behavior of Water over CuZn Alloy in CO2 Hydrogenation

Water plays a significant role in CO2 hydrogenation, which is capable of accelerating the reaction in an autocatalytic manner, but the reason for water promotion in the system is still controversial. This work dissects the mechanisms behind the autocatalytic behavior of water in CO2 hydrogenation. Based on the stable structure of CuZn(211) alloy under the reaction condition, density functional theory is employed to systematically explore all possible autocatalytic modes of water. We find that the influence of water on the reaction is mainly reflected in O-H bonding, in which water tends to facilitate the O-H bond formation by a direct participator mechanism. The nature of the facilitating effect is attributed to the nucleophilic property of O-H bonding. Due to the involvement of water, the reaction activity is enhanced with the improvement of CO selectivity. This work can provide a paradigm for investigating the origin of the autocatalytic behavior of water in heterogeneous catalysis.

Physical regulation of copper catalyst with a hydrophobic promoter for enhancing CO2 hydrogenation to methanol

The hydrogenation of CO₂ to methanol, which is restricted by water products, requires a selective removal of water from the reaction system. Here, we show that physically combining hydrophobic polydivinylbenzene with a copper catalyst supported by silica can increase methanol production and CO₂ conversion. Mechanistic investigation reveals that the hydrophobic promoter could hinder the oxidation of copper surface by water, maintaining a small fraction of metallic copper species on the copper surface with abundant Cu^δ+, resulting in high activity for the hydrogenation. Such a physically mixed catalyst survives the continuous test for 100 h owing to the thermal stability of the polydivinylbenzene promoter.

Methanol Synthesis from CO2/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

Methanol synthesis on industrial Cu/ZnO/Al₂O₃ catalysts via the hydrogenation of CO and CO₂ mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO₂ in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO₂ and CO hydrogenations on thermodynamically favorable Cu-Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn-Zn dimeric sites being avoided. CO₂ and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO₂ hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [-Zn-OH-Zn-] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system.

Discovery of a New Solvent Co-Catalyzed Mechanism in Heterogeneous Catalysis: A First-Principles Study with Molecular Dynamics on Acetaldehyde Hydrogenation on Birnessite

Heterogenous hydrogenation reactions are essential in a wide range of chemical industries. In this work, we find that the hydrogenation of acetaldehyde on birnessite cannot occur through the traditional mechanisms due to the strong adsorption of the aldehyde and hydrogen on the surface, using first-principles calculations. We discover that this reaction can occur feasibly via a solvent-cocatalyzed mechanism with molecular hydrogen in the liquid phase: a methanol solvent or a similar solvent is required for the reaction. Free energy calculations shows that the methanol solvent preferentially fills the oxygen vacancies of the catalyst surface and spontaneously dissociates on the surface, in which the resulting hydroxyl group then acts as the coordination site for the carbonyl bond and allows the reaction to proceed without adsorption of the reactants on the surface. The reasons this new mechanism is more favorable over the traditional mechanisms in the literature are scrutinized and discussed. The new mechanism may be followed in many other systems.

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials

There is an increasing demand for free-energy calculations using ab initio molecular dynamics these days. Metadynamics (MetaD) is frequently utilized to reconstruct the free-energy surface, but it is often computationally intractable for the first-principles calculations. Machine learning potentials (MLPs) have become popular alternatives. However, the training could be a long and arduous process before using them in practical applications. To accelerate MetaD use with MLPs for the free-energy calculation in an easy manner, we propose the adaptive machine learning potential-accelerated metadynamics (AMLP-MetaD). In this method, the MLP in the form of a Gaussian approximation potential (GAP) can adapt itself based on its uncertainty estimation, which decides whether to accept the model prediction or recalculate it with a reference method (usually density functional theory) for further training during the MetaD simulation. We demonstrate that the free-energy landscape similar to the ab initio one can be obtained using AMLP-MetaD with a 10-time speedup. Moreover, the quality of the free-energy results can be deeply improved using Δ-MLP, which is the GAP-corrected density functional tight binding in our case. We exemplify this novel method with two model systems, CO adsorption on the Pt₁₃ cluster and the Pt(111) surface, which are of vital importance in heterogeneous catalysis. The successful application in these two tests highlights that our proposed method can be used in both cluster and periodic systems and for up to two collective variables.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Investigating the innate selectivity issues of methane to methanol: consideration of an aqueous environment

The higher reactivity of the methanol product over the methane reactant for the direct oxidation of methane to methanol is explored. C-H activation, C-O coupling, and C-OH coupling are investigated as key steps in the selective oxidation of methane using DFT. These elementary steps are initially considered in the gas phase for a variety of fcc (111) pristine metal surfaces. Methanol is found to be consistently more reactive for both C-H activation and subsequent oxidation steps. With an aqueous environment being understood experimentally to have a profound effect on the selectivity of this process, these steps are also considered in the aqueous phase by ab initio molecular dynamics calculations. The water solvent is modelled explicity, with each water molecule given the same level of theory as the metal surface and surface species. Free energy profiles for these steps are generated by umbrella sampling. It is found that an aqueous environment has a considerable effect on the kinetics of the elementary steps yet has little effect on the methane/methanol selectivity-conversion limit. Despite this, we find that the aqueous phase promotes the C-OH pathway for methanol formation, which could enhance the selectivity for methanol formation over that of other oxygenates.

Influence of surface defects on activity and selectivity: a quantitative study of structure sensitivity of Pd catalysts for acetylene hydrogenation

The structure sensitivity of Pd catalysed acetylene hydrogenation is quantitatively examined using a coverage-dependent microkinetic model. Pd(211) was found to be more active than Pd(111), but present a poorer selectivity toward ethylene.

Unraveling the Photogenerated Electron Localization on the Defect-Free CH3NH3PbI3(001) Surfaces: Understanding and Implications from a First-Principles Study

The localization of photogenerated electrons in photovoltaic and photocatalytic materials is crucial for reducing the electron-hole recombination rate. Here, the photogenerated electron localization is systematically investigated on the CH₃NH₃PbI₃ (MAPbI₃) perovskite using first-principles calculations. It is found that under vacuum conditions, the photogenerated electron is delocalized in the MAPbI₃ bulk as well as on the stochiometric MAPbI₃(001) surface with the CH₃NH₃I (MAI) termination, while it is trapped on the defect-free PbI₂-terminated surface. Our ab initio molecular dynamics simulations reveal that the introduction of solutions will prompt the formation of localized electronic states. The photogenerated electron is discovered to be localized on both the MAI- and PbI₂-terminated surfaces in the presence of solutions with different concentrations of HI, from pure water to the saturated solution. We demonstrate that the Pb-I bond weakening or breaking resulting in an unsaturated coordination of a Pb site is the prerequisite to trap the photogenerated electron.

Origin of CO2 as the main carbon source in syngas-to-methanol process over Cu: theoretical evidence from a combined DFT and microkinetic modeling study

A theoretical study combining DFT and microkinetic modeling provides evidence that CO₂ is the main carbon source in methanol synthesis from syngas (CO, CO₂ and H₂) over Cu.

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.