Reversibility Iteration Method for Understanding Reaction Networks and for Solving Microkinetics in Heterogeneous Catalysis

Design of MoS2 edge-anchored single-atom catalysts for propane dehydrogenation driven by DFT and microkinetic modeling

The design of efficient catalysts for direct propane dehydrogenation (PDH) to inhibit coke formation and deactivation of traditional Pt-based catalysts are challenging tasks. Herein, by exploiting the unique geometric feature and tunability of single atom catalysts (SACs), a wide range of 3d-5d transition metals anchored on the MoS2 edge in the single atom form (TM1-S4/edge) are comprehensively investigated for the PDH application by first-principles calculations, ab initio molecular dynamics (AIMD) simulations and microkinetic modeling. Five criteria are assessed in terms of the feasibility of preparation, practical stability, feasibility of recovery after air oxidation, activity and selectivity. We identified Ru1-S4/edge SAC as the most promising candidate with activity six times higher than that of the conventional Pt(111) catalyst. Interestingly, AIMD simulations show that the motif region of the highly reactive TM1-S4/edge SACs (such as Ru, Os, Rh, and Ir) exhibits a dynamic change, with a TM-coordinated S atom tending to flutter at reaction temperatures and return to its initial position when the species is adsorbed on TMs, thereby affecting the PDH activities. In addition to identifying the potential PDH catalyst from a practical application point of view, we believe that this study also provides a comprehensive picture for the theoretical screening of low-coordination single-atom catalysts.

Selective CO2 electrolysis to CO using isolated antimony alloyed copper

Renewable electricity-powered CO evolution from CO₂ emissions is a promising first step in the sustainable production of commodity chemicals, but performing electrochemical CO₂ reduction economically at scale is challenging since only noble metals, for example, gold and silver, have shown high performance for CO₂-to-CO. Cu is a potential catalyst to achieve CO₂ reduction to CO at the industrial scale, but the C-C coupling process on Cu significantly depletes CO* intermediates, thus limiting the CO evolution rate and producing many hydrocarbon and oxygenate mixtures. Herein, we tune the CO selectivity of Cu by alloying a second metal Sb into Cu, and report an antimony-copper single-atom alloy catalyst (Sb₁Cu) of isolated Sb-Cu interfaces that catalyzes the efficient conversion of CO₂-to-CO with a Faradaic efficiency over 95%. The partial current density reaches 452 mA cm^-2 with approximately 91% CO Faradaic efficiency, and negligible C₂₊ products are observed. In situ spectroscopic measurements and theoretical simulations reason that the atomic Sb-Cu interface in Cu promotes CO₂ adsorption/activation and weakens the binding strength of CO*, which ends up with enhanced CO selectivity and production rates.

Positive Valent Metal Sites in Electrochemical CO2 Reduction Reaction

The discovery of high-performance catalysts for the electrochemical CO₂ reduction reaction (CO₂ RR) has faced an enormous challenge for years. The lack of cognition about the surface active structures or centers of catalysts in complex conditions limits the development of advanced catalysts for CO₂ RR. Recently, the positive valent metal sites (PVMS) are demonstrated as a kind of potential active sites, which can facilitate carbon dioxide (CO₂ ) activation and conversation but are always unstable under reduction potentials. Many advanced technologies in theory and experiment have been utilized to understand and develop excellent catalysts with PVMS for CO₂ RR. Here, we present an introduction of some typical catalysts with PVMS in CO₂ RR and give some understanding of the activity and stability for these related catalysts.

Steering from electrochemical denitrification to ammonia synthesis

The removal of nitric oxide is an important environmental issue, as well as a necessary prerequisite for achieving high efficiency of CO₂ electroreduction. To this end, the electrocatalytic denitrification is a sustainable route. Herein, we employ reaction phase diagram to analyze the evolution of reaction mechanisms over varying catalysts and study the potential/pH effects over Pd and Cu. We find the low N₂ selectivity compared to N₂O production, consistent with a set of experiments, is limited fundamentally by two factors. The N₂OH* binding is relatively weak over transition metals, resulting in the low rate of as-produced N₂O* protonation. The strong correlation of OH* and O* binding energies limits the route of N₂O* dissociation. Although the experimental conditions of varying potential, pH and NO pressures can tune the selectivity slightly, which are insufficient to promote N₂ selectivity beyond N₂O and NH₃. A possible solution is to design catalysts with exceptions to break the scaling characters of energies. Alternatively, we propose a reverse route with the target of decentralized ammonia synthesis.

Predictive Theoretical Model for the Selective Electroreduction of Nitrate to Ammonia

The electrochemical reduction of nitrate (eNO₃RR) emerges as a promising route for decentralized ammonia synthesis. However, the competitive production of nitrite at low overpotentials is a challenging issue. Herein, using the combination of density functional theory and microkinetic modeling, we show that the selectivity for NH₃ surpasses that of NO₂^- at -0.66 V_RHE, which nicely reproduced the experimental value on titania. NH₂OH* → NH₂* is the kinetically controlling step at a low overpotential for NH₃ generation, while NO₂* → HNO₂ has the highest barrier to producing nitrite. Based on these mechanistic insights, we suggest that ΔG₁ (NH₂OH* → NH₂*) - ΔG₂ (NO₂* → HNO₂) can serve as a descriptor to predict the S(NO₂^-)/S(NH₃) crossover potential. Such a model is verified by the experimental results on Ag, Cu, TiO_2-x, Fe₃O₄, and Fe-MoS₂ and can be extended to the Au catalyst. Thus, this work sheds light on the rational design of catalysts that are simultaneously energy-efficient and selective to NH₃.

Activity Self-Optimization Steered by Dynamically Evolved Fe3+@Fe2+ Double-Center on Fe2O3 Catalyst for NH3-SCR

Identification of the active centers dynamically stable under the reaction condition is of paramount importance but challenging because of the limited knowledge of steady-state chemistry on catalysts at the atomic level. Herein, focusing on the Fe₂O₃ catalyst for the selective catalytic reduction of NO with NH₃ (NH₃-SCR) as a model system, we reveal quantitatively the self-evolving Fe³⁺@Fe²⁺ (∼1:1) double-centers under the in-situ condition by the first-principles microkinetic simulations, which enables the accurate prediction of the optimal industry operating temperature (590 K). The cooperation of this double-center achieves the self-optimization of catalytic activity and rationalizes the intrinsic origin of Fe₂O₃ catalyzing NH₃-SCR at middle-high temperatures instead of high temperatures. Our findings demonstrate the atomic-level self-evolution of active sites and the dynamically adjusted activity variation of the catalyst under the in-situ condition during the reaction process and provide insights into the reaction mechanism and catalyst optimization.

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.

Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling

Microkinetic modeling based on density functional theory (DFT) energies plays an essential role in heterogeneous catalysis because it reveals the fundamental chemistry for catalytic reactions and bridges the microscopic understanding from theoretical calculations and experimental observations. Microkinetic modeling requires building a set of ordinary differential equations (ODEs) based on the calculation results of thermodynamic properties of adsorbates and kinetic parameters for the reaction elementary steps. Solving a microkinetic model can extract information on catalytic chemistry, including critical reaction intermediates, reaction pathways, the surface species distribution, activity, and selectivity, thus providing vital guidelines for altering catalysts.

However, the quantitative reliability of traditional microkinetic models is often insufficient to conclusively extrapolate the mechanistic details of complex reaction systems. This can be attributed to several factors, the most important of which is the limitation of obtaining an accurate estimation of the energy inputs via traditional calculation methods. These limitations include the difficulty of using static DFT methods to calculate reaction energies of adsorption/desorption processes, often rate-controlling or selectivity-determining steps, and the inadequate consideration of surface coverage effects. In addition, the robust microkinetic software is rare, which also complicates the resolution of complex catalytic systems.

In this Account, we review our recent works toward refining the predictions of microkinetic modeling in heterogeneous catalysis and achieving theory–experiment parity for activity and selectivity. First, we introduce CATKINAS, a microkinetic software developed in our group, and show how it disentangles the problem that traditional microkinetic software has and how it can now be applied to obtain kinetic results for more sophisticated reaction systems. Second, we describe a molecular dynamics method developed recently to obtain the free-energy changes for the adsorption/desorption process to fill in the missing energy inputs. Third, we show that a rigorous consideration of surface coverage effects is pivotal for building more realistic models and obtaining accurate kinetic results. Following a series of studies on acetylene hydrogenation reactions on Pd catalysts, we demonstrate how this new approach can provide an improved quantitative understanding of the mechanism, active site, and intrinsic structural sensitivity. Finally, we conclude with a brief outlook and the remaining challenges in this field.

Copper-catalysed exclusive CO2 to pure formic acid conversion via single-atom alloying

Converting CO₂ emissions, powered by renewable electricity, to produce fuels and chemicals provides an elegant route towards a carbon-neutral energy cycle. Progress in the understanding and synthesis of Cu catalysts has spurred the explosive development of electrochemical CO₂ reduction (CO₂RR) technology to produce hydrocarbons and oxygenates; however, Cu, as the predominant catalyst, often exhibits limited selectivity and activity towards a specific product, leading to low productivity and substantial post-reaction purification. Here, we present a single-atom Pb-alloyed Cu catalyst (Pb₁Cu) that can exclusively (~96% Faradaic efficiency) convert CO₂ into formate with high activity in excess of 1 A cm^-2. The Pb₁Cu electrocatalyst converts CO₂ into formate on the modulated Cu sites rather than on the isolated Pb. In situ spectroscopic evidence and theoretical calculations revealed that the activated Cu sites of the Pb₁Cu catalyst regulate the first protonation step of the CO₂RR and divert the CO₂RR towards a HCOO* path rather than a COOH* path, thus thwarting the possibility of other products. We further showcase the continuous production of a pure formic acid solution at 100 mA cm^-2 over 180 h using a solid electrolyte reactor and Pb₁Cu.

Activity and Mechanism Mapping of Photocatalytic NO2 Conversion on the Anatase TiO2(101) Surface

NO_x emission heavily affects our environment and human health. Photocatalytic denitrification (deNO_x) attracted much attention because it is low-cost and nonpolluting, but undesired nitrite and nitrate were produced in reality, instead of harmless N₂. Unveiling the active sites and the photocatalytic mechanism is very important to improve the process. Herein, we have employed a combinational scenario to investigate the reaction mechanism of NO₂ and H₂O on anatase TiO₂(101). On the one hand, a polaron-corrected GGA functional (GGA + Lany-Zunger) was applied to improve the description of electronic states in photoassisted processes. On the other hand, a reaction phase diagram (RPD) was established to understand the (quasi) activity trend over both perfect and defective surfaces. It was found that a perfect surface is more active via the Eley-Rideal mechanism without NO₂ adsorption, while the activity on defective surfaces is limited by the sluggish recombinative desorption. A photogenerated hole can weaken the OH* adsorption energies and circumvents the scaling relation of the dark reaction, eventually enhancing the deNO_x activity significantly. The insights gained from our work indicate that tuning the reactivity by illumination-induced localized charge and diverse reaction pathways are two methods for improving adsorption, dissociation, and desorption processes to go beyond the conventional activity volcano plot limit of dark conditions.

Unveiling Potential Dependence in NO Electroreduction to Ammonia

Recently, electrochemical NO reduction (eNORR) to ammonia has attracted enormous research interests due to the dual benefits in ammonia synthesis and denitrification fields. Herein, taking Ag as a model catalyst, we have developed a microkinetic model to rationalize the general selectivity trend of eNORR with varying potential, which has been observed widely in experiments, but not understood well. The model reproduces experiments well, quantitatively describing the selectivity turnover from N₂O to NH₃ and from NH₃ to H₂ with more negative potential. The first turnover of selectivity is due to the thermochemical coupling of two NO* limiting the N₂O production. The second turnover is attributed to the larger transfer coefficient (β) of HER than NH₃ production. This work reveals how electrode potential regulate the selectivity of eNORR, which is also beneficial to understand the commonly increasing HER selectivity with the decrease of potential in some other electroreduction reactions such as CO₂ reduction.

CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening

As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.

SSIA: A sensitivity-supervised interlock algorithm for high-performance microkinetic solving

Microkinetic modeling has drawn increasing attention for quantitatively analyzing catalytic networks in recent decades, in which the speed and stability of the solver play a crucial role. However, for the multi-step complex systems with a wide variation of rate constants, the often encountered stiff problem leads to the low success rate and high computational cost in the numerical solution. Here, we report a new efficient sensitivity-supervised interlock algorithm (SSIA), which enables us to solve the steady state of heterogeneous catalytic systems in the microkinetic modeling with a 100% success rate. In SSIA, we introduce the coverage sensitivity of surface intermediates to monitor the low-precision time-integration of ordinary differential equations, through which a quasi-steady-state is located. Further optimized by the high-precision damped Newton's method, this quasi-steady-state can converge with a low computational cost. Besides, to simulate the large differences (usually by orders of magnitude) among the practical coverages of different intermediates, we propose the initial coverages in SSIA to be generated in exponential space, which allows a larger and more realistic search scope. On examining three representative catalytic models, we demonstrate that SSIA is superior in both speed and robustness compared with its traditional counterparts. This efficient algorithm can be promisingly applied in existing microkinetic solvers to achieve large-scale modeling of stiff catalytic networks.

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.

A fast species redistribution approach to accelerate the kinetic Monte Carlo simulation for heterogeneous catalysis

The first-principles kinetic Monte Carlo (kMC) simulation has been demonstrated as a reliable multiscale modeling approach in silico to disclose the interplay among all the elementary steps in a complex reaction network for heterogeneous catalysis. Heterogeneous catalytic systems frequently contain fast surface diffusion processes of some adsorbates while the elementary steps in it would be much slower than those in fast diffusion. Consequently, the kMC simulation for these systems is easily trapped in the sub-basins of a super basin on a potential energy surface due to the continuous and repeated sampling of these fast processes, which would significantly increase the total accessible simulation time and even make it impossible to get the reasonable simulation results using the kMC simulation. In this work, we present an improved fast species redistribution (FSR) method for the kMC simulation to overcome the stiffness problem resulting from the low-barrier surface diffusion to accelerate the heterogeneous catalytic kMC simulation. Taking CO oxidations on Pt(111) and Pt(100) as examples, we demonstrate that the FSR approach can properly reproduce the results of an equivalent first-principles microkinetic model simulation with more reasonable reaction rates. The improved kMC simulation based on the FSR method can accurately incorporate the effect of the fast diffusion of species on the surface and provide several orders of magnitude of acceleration compared to the standard kMC simulation.

Understanding supported noble metal catalysts using first-principles calculations

Heterogeneous catalysis on supported and nonsupported nanoparticles is of fundamental importance in the energy and chemical conversion industries. Rather than laboratory analysis, first-principles calculations give us an atomic-level understanding of the structure and reactivity of nanoparticles and supports, greatly reducing the efforts of screening and design. However, unlike catalysis on low index single crystalline surfaces, nanoparticle catalysis relies on the tandem properties of a support material as well as the metal cluster itself, often with charge transfer processes being of key importance. In this perspective, we examine current state-of-the-art quantum-chemical research for the modeling of reactions that utilize small transition metal clusters on metal oxide supports. This should provide readers with useful insights when dealing with chemical reactions on such systems, before discussing the possibilities and challenges in the field.

Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes, Supersites, and the Supercluster Contraction

While lateral interaction models for reactions at surfaces have steadily gained popularity and grown in terms of complexity, their use in chemical kinetics has been impeded by the low performance of current kinetic Monte Carlo (KMC) algorithms. The origins of the additional computational cost in KMC simulations with lateral interactions are traced back to the more elaborate cluster expansion Hamiltonian, the more extensive rate updating, and to the impracticality of rate-catalog-based algorithms for interacting adsorbate systems. Favoring instead site-based algorithms, we propose three ways to reduce the cost of KMC simulations: (1) representing the lattice energy by a smaller Supercluster Hamiltonian without loss of accuracy, (2) employing the subtraction schemes for updating key quantities in the simulation that undergo only small, local changes during a reaction event, and (3) applying efficient search algorithms from a set of established methods (supersite approach). The cost of the resulting algorithm is fixed with respect to the number of lattice sites for practical lattice sizes and scales with the square of the range of lateral interactions. The overall added cost of including a complex lateral interaction model amounts to less than a factor 3. Practical issues in implementation due to finite numerical accuracy are discussed in detail, and further suggestions for treating long-range lateral interactions are made. We conclude that, while KMC simulations with complex lateral interaction models are challenging, these challenges can be overcome by modifying the established variable step-size method by employing the supercluster, subtraction, and supersite algorithms (SSS-VSSM). © 2019 Wiley Periodicals, Inc.