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Kinetic Monte Carlo simulations unveil synergic effects at work on bifunctional catalysts Hector Prats, Sergio Posada-Pérez, Jose A. Rodriguez, Ramon Sayos, and Francesc Illas ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.9b02813 • Publication Date (Web): 29 Aug 2019 Downloaded from pubs.acs.org on August 29, 2019

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Kinetic Monte Carlo simulations unveil synergic effects at work on bifunctional catalysts Hèctor Prats,† Sergio Posada-Pérez,† José A. Rodriguez,§ Ramón Sayós,† Francesc Illas*,† †Departament

de Ciència de Materials i Química Física & Institut de Química Teòrica i

Computacional (IQTCUB), Universitat de Barcelona, C/ Martí i Franquès 1-11, 08028 Barcelona, Spain. §Chemistry

Department, Brookhaven National Laboratory, Upton, New York 11973, United States of

America. *Corresponding

author: Francesc Illas ([email protected])

Keywords: kMC, synergic effects, bifunctional catalysts, WGSR, reaction mechanisms, transition metal carbides ABSTRACT The interaction between metal particles and the support in heterogeneous catalysis has been the subject of a large number of studies. While strong metal-support interactions can lead to deleterious catalyst deactivation and the underlying mechanism is well understood, in other cases the effect may beneficially enhance the catalytic activity and/or selectivity with no clear picture of the chemistry involved. Strong metal-support interactions make Au nanoparticles dispersed on MoC a highly active catalyst for the low-temperature water-gas shift reaction (WGSR). Here, by using kinetic Monte Carlo (kMC) simulations, we unravel the origin of the experimentally observed high WGSR activity of Au/MoC. The kMC simulations provide strong evidence for a cooperative effect between the different regions of the catalyst: the clean MoC regions are responsible for adsorbing and dissociating water molecules, and the vicinity of the Au adclusters contribute to COOH formation. The information thus obtained goes beyond that obtained solely from free-energy landscapes and constitutes a step forward towards the rational design of catalysts. Importantly, the simulations and analysis described here are general and can be applied to other complex systems involving different catalytic regions and a large number of surface processes.

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INTRODUCTION Heterogeneous catalysts often involve metallic nanoparticles that are highly dispersed on several types of supports, such as carbon or different types of oxides, carbides and sulfides.1 Recent studies have shown that strong metal-support interactions make Au nanoparticles dispersed on molybdenum carbide a unique catalyst for the low temperature water-gas shift reaction (WGSR, CO+H2O → CO2+H2).2,3 The pioneering work of Tauster et al.4,5 evidenced that the interaction between a metal and an oxide support may largely affect the performance of this type of catalysts leading to a loss of catalytic activity. However, more recent studies have shown useful situations where the overall activity and/or selectivity towards a given product is improved.2,3,6,7 These observations lead to the nowadays well-known concept of strong metal-support interactions (SMSI).4-8 For academic and practical reasons, it is important to fully understand how SMSI affect the overall reaction rates of catalytic processes.8,9 Studies of the WGSR catalyzed by gold particles dispersed on molybdenum carbide2,3 or platinum particles dispersed on ceria6 provide clear evidence about the role of support-induced electronic perturbations on the performance of the catalyst’s metal component. Recent work on CO oxidation on Au/TiO2 supports the direct involvement of SMSI in the overall chemistry.7 The case of metallic nanoparticles supported on transition metal carbides (TMCs) is a particularly interesting situation because there is direct experimental confirmation of the electronic polarization of the metallic nanoparticle caused by the support and evidenced by theoretical modelling.10 Moreover, some TMCs such as the different phases of molybdenum carbide cubic MoC (δ-MoC), orthorhombic (-Mo2C) and hexagonal (-Mo2C) exhibit an intrinsic catalytic activity, are active in hydrodesulfurization reactions,11 have been proposed as alternative to platinum for the hydrogen evolution reaction12 and, more recently, in the selective conversion of CO2 to CO.13 Molybdenum carbides are also active supports for some reactions: small Cu and Au particles supported on 𝛿-MoC exhibit a high activity for the CO2 conversion14 and, as mentioned above, systems involving Au particles supported on different types of MoCx are found to be very active for the low-temperature WGSR.2 In the study of Posada-Peréz et al. regarding the low temperature WGSR,2 the authors reported the critical role of the unique interactions between the supported metal and the carbide, which were responsible of the big increase in the catalytic activity of the Au/𝛿-MoC system. Specifically, Au/𝛿MoC was found to be the best catalyst due to its higher activityorders of magnitude higher than Cu(111), a typical benchmark in WGSR studies, selectivityCH4 is not detected and stabilityoxycarbide is not formed. Interestingly, in the case of the WGSR on Au/𝛿-MoC, a maximum

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in the production of H2 and CO2 was observed for a coverage of 0.15 ML of Au.2 After this point, there was a gradual decrease in the WGSR activity probably as a consequence of the growth of relatively large Au particles. Periodic DFT-based calculations suggested that the better performance of Au/𝛿MoC compared to the bare 𝛿-MoC catalyst could be due to the activation of the redox mechanism; this is CO2 formation by CO*+O* oxidation with O* produced from OH*+OH* recombination). While the CO*+O* step has an energy barrier of 1.56 eV in the bare 𝛿-MoC(001) surface, it becomes of 0.10 eV only in the Au4/𝛿-MoC(001) slab model used to represent the Au/𝛿-MoC(001) system. Moreover, the presence of Au clusters favours the desorption of the reaction products. However, it is clear that without a detailed analysis of the TOF taking into account the majority, if not all, possible elementary steps in the different active sites of different regions of a supported catalyst, it is not possible to firmly stablish the role of the underlying molecular mechanism in the final product distribution. Thus, athough electronic-structure calculations can provide crucial understanding of the elementary steps constituting the reaction mechanisms that underpin a chemical process catalyzed by metallic surfaces,15-17 such insight may not be enough to reach sound conclusions regarding the performance of a complex supported catalyst under operating conditions. To provide further support to the claims above it us worth pointing out that, over the past twenty years, most theoretical studies on the molecular mechanism of heterogeneously catalyzed reactions have relied on energy barriers and adsorption energies obtained from Density Functional Theory (DFT) calculations. Besides, predictions about the rate determining steps (RDS) are often exclusively made based on calculated energies or free-energy profiles, the latter accounting at least for pressure and temperature effects but ignoring adsorbate mobility and reaction dynamics, all of which can have a decisive effect on the catalytic performance. Therefore, except in few special cases exhibiting an unambiguous RDS, the information provided from the DFT free energy profiles is not sufficient to fully understand the experimental observations. A more accurate description of the overall catalytic cycles requires taking chemical kinetics into account. To this end, one can rely on LangmuirHinshelwood-type models, Sabatier analysis,18 mean-field micro-kinetic models,19-21 or more sophisticated kinetic Monte Carlo (kMC) simulations.22-24 Among these, kMC avoids the mean-field approximation by making use of a spatially resolved lattice model. In the kMC framework, the inclusion of lateral interactions between the adsorbates can be done by using either site-blocking rules25 or cluster expansion models.26 Nevertheless, kMC simulations are more time-consuming than the alternatives mentioned above, especially when the surface is heterogeneous and the number of processes is high. Not surprisingly, most kMC studies published over the last fifteen years have focused on simple chemical processes such as O2 adsorption/desorption,27 CO oxidation,28-3132,33 water formation34 or hydrogen diffusion35 involving a rather limited number of elementary processes only. 3


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However, one must point out that some of this previous studies used a quite complex lattice model representing supported metal catalysts.32,33 It is only since the last five years that the number of kMC studies involving complex reactions with dozens of surface processes (e.g., CO methanation,36 methanol partial oxidation (MPO),37 WGSR26,38,39 to mention just a few) started to grow. Most of those studies correspond to reactions occurring on ideal, low Miller index, metallic surfaces represented by slab models, which can be described using simple lattice models involving only one or two types of sites. However, even with such idealized catalyst surfaces, lateral interactions between adsorbates are often neglected.36-43 Only in a few cases the kMC method has been applied to simulate complex reactions in heterogeneous lattices involving supported metal clusters or doped surfaces, but almost always using a rather simplified lattice model. For instance, Yang et al.38 studied the WGSR on Cu/ZnO, assuming that the ZnO support is inert and does not adsorb any gas molecules. Rawal et al.37 studied the MPO on Pd/ZnO using a homogeneous lattice model with only one type of surface sites, and the same approach was used by Yang et al.44 when studying the ethanol synthesis on Mn- and Mo-doped Rh(111) surfaces. Similarly, Huang et al. 45 performed kMC simulations of vinyl acetate synthesis on Au/Pd(100) using a lattice model with three kinds of sites (top, bridge and hollow) but without explicitly describing the position of the Au adclusters. Finally, Turner et al.46,47 were the first to consider both the support and the metal particles as active sites in their study of the propylene epoxidation on Au/TiO2/SiO2 using a 3-site type lattice model that explicitly describes the position of the supported particles. Note that, with the exception of Yang et al.38 who included only pairwise CO-CO repulsions, none of the previous works included lateral interactions in their kMC simulations. To the best of our knowledge, the only kMC study involving a complex reaction over an heterogeneous lattice where nearest-neighbor lateral interactions have been taken into account is a very recent work by Jørgenen et al.48 In this work, the authors studied the acetylene hydrogenation over single-atom allow nanoparticles. In this article, we use a combination of periodic DFT calculations and kMC simulations to study in detail the role of SMSI on the water-gas shift reaction catalyzed by the Au/-MoC(001) system. To this end, we compare possible paths for the evolution of the WGSR on the clean 𝛿MoC(001) surface and on the composed Au/-MoC(001) system with 0.15 ML and 0.25 ML of gold. Our results unravel the origin of the synergistic effect between bare MoC regions and Au adclusters, responsible for the high catalytic activity observed experimentally at 0.15 ML of Au,2 as well as the individual role of each region of the catalyst. It is worth pointing out that these questions cannot be answered solely by relying on DFT calculations. Apart from the timely insight into such a relevant catalytic system, this article also presents one of the most comprehensive kMC studies to date, with

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five different lattice sites and 133 surface elementary processes considered on the Au/MoC lattice model. Moreover, we couple the kMC model with cluster expansion Hamiltonian to include a large number of lateral interactions between adsorbates. Finally, the calculated results for 𝛿-MoC (001) and Au/MoC are compared with experimental data, showing good agreement and SMSI synergistic effects in the metal-carbide system. LATTICE MODELS AND REACTION NETWORK The lattice model for clean -MoC(001) and systems involving Au supported on this welldefined surface with a given 𝜃 ML coverage hereafter denoted as Au(𝜃)/MoC are built so as to mimic the slab models used in a previous study.2 In short, these consist of Au4 nanoparticles supported on a repeated slab model approach using supercells of different size as described in detail in the Supporting Information. Nevertheless, it is worth pointing out that this type of model represent the most salient experimental features and its size is a compromise between realism and feasibility. In any case, the validity of this surface model has been sufficiently validated and the interested readers is referred to previous work.2 Specifically, the lattice model for the present systems consist of a square periodic grid of points, where each point represents one catalytically active surface site. Accordingly, only top sites are explicitly defined in our lattice model although this does not imply neglecting other sites. Species adsorbed at bridge sites, 𝑏𝑋𝑌, are labelled as occupying two neighboring top sites, 𝑡𝑋 and 𝑡𝑌. For instance, adsorbed OH species at a bridge C-Mo site are denoted 𝑂𝐻 ∗∗ (𝑡𝐶,𝑡𝑀𝑜). The same strategy is used for larger adsorbates occupying more than one adsorption site, such as COOH, which involves a top-C and a top-Mo site, hence denoted 𝐶𝑂𝑂𝐻 ∗∗ (𝑡𝐶,𝑡𝑀𝑜). Accordingly, the model unit cell for the clean MoC surface has just 𝑡𝐶 and 𝑡𝑀𝑜 sites. A third type of site (𝑡𝐴𝑢) has to be defined when modeling the Au(𝜃 ML)/MoC systems (see Figure 1). From the analysis of the calculated adsorption energies on clean MoC and Au(𝜃 ML)/MoC systems, it is evident that the supported Au adclusters have an important effect on the adsorption strength of species adsorbed at or near Au sites. For instance, the adsorption energy of CO at the 𝑡𝐶 site depends on whether this 𝑡𝐶 site is located next to a Au adcluster or not. Thus, one must distinguish between 𝑡𝐶 and 𝑡𝑀𝑜 sites away from Au sites, as those in the bare MoC or clean region, and 𝑡𝐶𝑖𝑛 and 𝑡𝑀𝑜𝑖𝑛 sites next to Au sites which constitutes the interface region. Hence, while the lattice model for clean MoC consists only of 𝑡𝐶 and 𝑡𝑀𝑜 sites, the lattice model for Au( ML)/MoC is built from 𝑡𝐶𝑖𝑛, 𝑡𝑀𝑜𝑖𝑛 and 𝑡𝐴𝑢 sites (see Figure 1). We have checked that the TOF and coverage are converged using a simulation cell size of (5 × 5) on clean 𝛿MoC, (3 × 3) on Au(0.15 ML)/MoC, and (3 × 3) on Au(0.25 ML)/MoC by performing kMC simulations at bigger lattice sizes. The total number of adsorption sites per unit cell on clean 𝛿-MoC,

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Au(0.15 ML)/MoC, and Au(0.25 ML)/MoC are 8, 32, and 48, respectively. Hence, the total number of adsorption sites in the used simulation cells are 200, 432, and 288, respectively. Experimentally, the maximum production of H2 and CO2 through the WGSR occurs at 𝜃𝐴𝑢~ 0.15 ML,2 a situation where the 𝛿-MoC(001) surface is covered with relatively small Au particles leaving large regions of clean 𝛿-MoC(001). This suggests that the synergy between Au particles and bare MoC regions is responsible for the enhanced H2 and CO2 production. Modelling of this optimum catalyst requires a rather large lattice model that may be obtained by adding two extra rows of 𝑡𝐶 and 𝑡𝑀𝑜 sites to the Au(0.25 ML)/MoC lattice model shown in Figure 1. The resulting lattice model for the Au(0.15 ML)/MoC system contains the five (𝑡𝐶, 𝑡𝑀𝑜, 𝑡𝐶𝑖𝑛, 𝑡𝑀𝑜𝑖𝑛 and 𝑡𝐴𝑢) different types of sites, as also shown in Figure 1. In addition, atomic H is adsorbed on a special hydrogen reservoir site (ℎ or ℎ𝑖𝑛), which has the same energetics as a 𝑡𝐶 (or 𝑡𝐶𝑖𝑛) site but is exclusively occupied by H in the kMC model; this is a standard procedure in kMC simulations as explained in the Supporting Note 1. Several reaction mechanisms have been proposed in the literature for the WGRS mediated by different substrates including metal surfaces,20,49,50 bare TiC51 and Au/TiC52 systems, and 𝛽-Mo2C.53 All of them share the common feature of having water dissociation as the initial step. Next, in the redox mechanism, atomic O is first produced by direct CO and/or OH dissociation or by OH recombination as in OH+OH→H2O+O. The CO2 product is then formed by CO oxidation (CO+O→ CO2) or via the formate route involving formation of HCO and HCOO intermediates and subsequent HCOO dissociation to produce CO2. Alternatively, the associative mechanism involves the formation of COOH intermediate, and CO2 is subsequently obtained by direct COOH dissociation or assisted by OH (COOH+OH→CO2+H2O). Note, however, that the number of possible elementary steps increases with the increasing complexity of the surface, as more sites are involved. In the present work, a set of 133 elementary processes have been considered for the reaction mechanism of the WGSR on the Au(0.15 ML)/MoC lattice model, which are listed in Tables S1 and S2. They correspond to 60 reversible steps (i.e., adsorption, desorption, surface chemical reaction or diffusion processes) which include both forward and reverse processes plus 10 irreversible desorption steps and 3 reversible but symmetric diffusion steps. These processes include the most important reaction routes that have been fully characterized by DFT calculations2 and also a set of 16 diffusion steps for the most mobile species, namely H, O, OH, H2O, and CO. The expressions used to calculate the transition probabilities (rate constants) of all elementary processes are described in the Supporting Methods. Also, note that our reaction model involves processes with very dissimilar energy barriers. To speed-up the kMC simulations avoiding an exceedingly large number purely diffusion or

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adsorption/desorption steps, the difference in time scales of all processes has been handled by manually scaling of the transition probabilities of fastest processes by some scaling factor 𝛼 < 1 whiles ensuring that this does not alter the final outcome of the simulation. All the details related to this scaling technique are described in the Supporting Methods. The overall macroscopic properties reported in the manuscript correspond to an average from 5 independent kMC simulations over the production stages. The kMC simulations differ from each other in the sequence of random numbers used only. The reaction model corresponding to the clean MoC system can be obtained from the full set by simply eliminating processes involving 𝑡𝐶𝑖𝑛, 𝑡𝑀𝑜𝑖𝑛 or 𝑡𝐴𝑢 sites. Likewise, the set of reactions included in the Au(0.25 ML)/MoC lattice model is obtained by removing processes involving 𝑡𝐶 or 𝑡𝑀𝑜 sites. As already mentioned, formation energies for adsorbates and transition states have been obtained from DFT calculations on the clean MoC and Au(0.25 ML)/MoC slab models. However, the Au(0.15 ML)/MoC lattice model has been built as a combination of the former two systems (see Figure 1). Details clarifying how the frontier line between both clean and interface regions is treated are summarized in Supporting Note 2. Finally, for the purpose of the present work, the cluster expansion models used in the three studied systems are limited to first nearest-neighbour pairwise interactions involving the most abundant species. Further details are given in Supporting Note 3. RESULTS AND DISCUSSION Clean -MoC (001). For the clean MoC surface, the kMC simulations involve a 5 × 5 lattice model including a total of 50 𝑡𝐶 and 50 𝑡𝑀𝑜 sites which produced at least 70.000 H2 molecules. This corresponds to ~56 s of total simulated time at PCO = 20 torr, PH2O = 10 torr, and T = 465 K, which is the highest temperature used in the experiments.2 The kMC simulations show that the amount of H2 and CO2 produced are essentially the same. This result is also observed for the systems containing 0.15 and 0.25 ML of Au, and is in agreement with the experimental results.2 At these conditions, the MoC surface is covered mainly by H2O and OH species, as shown in Table 1. A significant amount of atomic H is also observed, as well as traces (1 ― 3%) of CO, HCO and atomic O. A careful analysis shows that one atomic O is produced every several tens of millions of kMC steps, so that 𝜃𝑂 increases very slowly over time. This is a warning of possible slow surface poisoning. In fact, a minor amount of oxygen was found experimentally on the surface of the MoC-based catalysts, but its coverage reached a steady state.2 Although COOH plays a main role in the reaction mechanism (see below), it is such a reactive species that its coverage is almost insignificant, also in agreement with the experimental observations. Similarly, the surface coverage of the H2 and CO2 reaction products is almost zero, since their relatively small adsorption energies at the high temperature of reaction (465 K) imply fast 7


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desorption rates. Note that even if the desorption energy barriers at the zero-coverage limit are 0.45 and 0.82 eV for H2 and CO2, respectively, they are noticeably decreased due to repulsive lateral interactions with the neighbouring adsorbates. Finally, no production of atomic C or HCOO species has been observed, confirmed by the inspection of the process statistics. This is an expected result, because the energy barriers for CO dissociation to produce atomic C and for HCOO formation are quite high: 1.79 and 2.02 eV, respectively. In fact, molybdenum carbide is known to be resistant to carbon formation.54 The analysis of the net number of executions for all steps involving CO2 formation in the present kMC simulations suggests that, on clean MoC, the associative mechanism is responsible for the production of CO2. Concretely, all CO2 molecules have been produced through the direct COOH dissociation route (i.e., process 24 followed by 28 in Table S1). This result agrees with the predictions made from the potential energy surface, as it is the route with the lowest energy barrier (i.e., 0.27 eV) and it is also a unimolecular reaction. Surprisingly, the COOH+OH→CO2+H2O reaction, which is the second most plausible way to produce CO2 in view of its low energy barrier of 0.37 eV, acts in a direction opposite to the desired one. By looking at the process statistics, it is found that this process occurs on average 12% more times in the reverse direction than in the direct one. The reason is that the reverse energy barrier for this process is 0.14 eV only, therefore competing with the CO2 desorption process. Hence, the system invests a considerable amount of time in COOH dissociation cycles to form CO2, followed by recombination to COOH through reaction of CO2 with H2O. The positive part of these cycles is that, as a net result, they contribute to the dissociation of a water molecule, as schematically shown in Figure 2a. Finally, kMC simulations suggest that the redox mechanism does not participate in the process, as expected from the high energy barrier of 1.52 eV for CO oxidation. High Au coverage. For the Au(0.25 ML)/MoC surface, the kMC simulations used a 3 × 3 lattice model including a total of 45 𝑡𝐶𝑖𝑛, 63 𝑡𝑀𝑜𝑖𝑛 and 36 𝑡𝐴𝑢 sites producing at least 100.000 H2 molecules in ~6 s of total simulated time at PCO = 20 torr, PH2O = 10 torr, and T = 465 K. At these conditions, the Au(0.25 ML)/MoC surface is covered mainly by H2O and CO species, as shown in Table 1. Significant amounts of OH, COOH and atomic H are also observed. The coverage of the reaction products H2 and CO2 is zero, because the presence of Au atoms immediately expels the product molecules once they form. Finally, no production of atomic O is observed, confirmed by the inspection of the process statistics. Interestingly, about 50% of the adsorbed CO molecules are located on the Au cluster, occupying half of the available 𝑡𝐴𝑢 sites. The remaining half of 𝑡𝐴𝑢 sites correspond to ∗ 𝑡𝐴𝑢 free sites with only a small presence of 𝐻2𝑂 ∗ 𝑡𝐴𝑢 molecules. Unfortunately, calculated surface coverage values are very difficult to compare with experimental results, mainly because of the experimental difficulty

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to measure the coverage of surface species on a catalyst at work involving a high number of intermediate species and, also, of the intrinsic limitations of the catalyst models used. The analysis of the net number of executions for all steps involving CO2 formation in the present kMC simulations suggests that the associative mechanism is responsible for the production of CO2 also on the Au(0.25 ML)/MoC system. Concretely, only 0.2% of all CO2 molecules have been produced through the redox route (i.e., processes 20, 21 and 22 in Table S1). The remaining 99.8% followed the associative mechanism, being the direct COOH dissociation route (i.e., process 29) responsible for the 0.5% of them, and the OH-assisted route (i.e., processes 33 and 34) responsible for the remaining 99.5%. Despite the fact that DFT calculations predict a very low energy barrier for CO oxidation close to Au clusters (i.e., 0.09 eV, processes 20-22), the absence of atomic O at the surface makes the COOH+OH reaction the main source of CO2. This result also agrees with the predictions made from the calculated energy profile since the presence of the Au adcluster lowers the energy barrier for this process from 0.37 eV on the clean MoC surface to 0.22 eV in the Au(0.25 ML)/MoC system. This value is much lower than the corresponding energy barrier for the direct COOH dissociation in the vicinity of the Au cluster, which goes up to 0.97 eV. Water dissociation, which is the first step in all of the mechanisms, occurs only at the vicinity of the Au adcluster, but no directly on the Au atoms (i.e. through process 10 instead of 11; see Table S1). The same holds for the H2 formation. Optimum Au coverage. The 3 × 3 lattice model for the Au(0.15 ML)/MoC system includes a total of 36 𝑡𝐶, 45 𝑡𝑀𝑜, 45 𝑡𝐶𝑖𝑛, 54 𝑡𝑀𝑜𝑖𝑛 and 36 𝑡𝐴𝑢 sites. All kMC simulations have been run until at least 100.000 H2 molecules were produced, corresponding to ~2.4 s (~20 s) of total simulated time at T = 465 K (T = 410 K, the lowest temperature used in the experiments), PCO = 20 torr and PH2O = 10 torr. Note that kMC simulations at lower temperatures need a higher number of kMC steps due to the lower number of product molecules formed per unit area and unit time, also known as the turnover frequency (TOF). The large amount of processes and lateral interactions included in this model has involved running each kMC simulation at T = 410 K for almost a month of real time, using two cores on an Intel(R) Xeon(R) E5 ― 4620 0 @ 2.20GHz, and around 9 days for the kMC simulations at 465 K (the highest temperature). Table S5 lists the total number of kMC steps, lattice sites, integrated kMC time, total CPU time, and total number of H2 molecules produced per CPU day corresponding to the kMC simulations performed on the three studied systems at different reaction temperatures. For the temperature range considered here (410 ― 465 K), the Au(0.15 ML)/MoC surface is covered mainly by H2O, atomic H and CO species (see Table 1). Significant amounts of OH and COOH species are also observed, as well as traces (~1%) of HCO and atomic O. Interestingly, the 9


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coverage of atomic O, as well as those of all other species, appears to be constant throughout the kMC simulations, suggesting that the Au(0.15 ML)/MoC surface does not present any risk of poisoning by O. This finding nicely agrees with the experimental results, in which catalyst deactivation with time has not been observed either. Moreover, a small amount of oxygen has also been observed experimentally on the catalyst surface with post-reaction XPS characterization.2 Finally, there are no large variations in coverage with temperature, except for an increase in 𝜃𝐶𝑂𝑂𝐻 and 𝜃𝐻 when lowering the temperature from 465 to 410 K (see Figure S1). The present kMC simulations suggest that the associative mechanism is also responsible for the production of CO2 on the Au(0.15 ML)/MoC surface, with both routes (i.e., direct COOH dissociation and assisted by OH) participating (see Table 2). Although the latter is the main reaction route in the entire range of temperatures studied, the values in Table 2 show that its activity decreases for increased temperature, thus leaving the direct COOH dissociation as the main player. The most interesting feature of this system is, however, a much higher TOF compared to clean MoC or Au(0.25 ML)/MoC surfaces. Although this system is a combination of the previous two (Figure 1), the resulting TOF is far from being just an area-weighted average between the TOFs for the two previous systems. The synergy observed experimentally between MoC and Au adclusters is well reproduced by the kMC method. Results in Table 1 show the calculated TOF at 465 K, which is 7.7 and 4.3 times higher than the corresponding TOFs for clean MoC and Au(0.25 ML)/MoC surfaces, respectively. Importantly, we note that the synergy is not foreseen from the analysis of the DFT energy profiles, whereas it clearly emerges in the kMC simulations and in a rather quantitative way.2 In fact, the experimental TOF for the Au(0.15 ML)/MoC catalyst is between 6-7 and 3-5 times higher than the TOFs for the bare 𝛿MoC(001) and Au(0.25 ML)/MoC catalysts, respectively (see Figure 2 in Ref. 2). However, one must note that the kMC predicted TOFs are somehow higher than the experimental ones. For the Au(0.15 ML)/MoC system, Figure 3 in Ref. 2 report ln[TOF/molec·cm-2·s-1] ~ 35 at 465 K while the kMC calculated value for the same systems is 42.7. The absolute TOF obtained from kMC simulations should be scaled by a factor accounting for the catalyst’s active site area. Additional deviations may arise from the limitations of the kMC method itself,23 the truncation to two-body terms in the CE, errors in the computed energy barriers and lateral interactions from DFT, errors in the vast number of transition probabilities calculated from TST (which correspond to an upper limit of the real transition probabilities), as well as the differences between the real catalyst and the kMC lattice model. A sensitivity analysis indicates that small changes ( 𝑛𝑒𝑥,𝑖 ).

Figure 2c provides a visual summary of the results. It clearly shows that the interface region appears to be the main responsible for the adsorption of reactants (~75%), followed by the clean region (~25%), and finally that the Au region only participates marginally in CO adsorption (3%). It is worth pointing out that this result cannot be anticipated from the calculated adsorption energies of CO and H2O only. For instance, the CO adsorption energy is larger on the clean region (-1.90 eV, 𝑡𝐶 site) than on the interface (-1.13 eV, 𝑡𝐶𝑖𝑛) and Au (-1.16 eV, 𝑡𝐴𝑢) regions, but as shown in Figure 3a, there is indeed a net flow of CO adsorbates diffusing from the Au adcluster to the interface and from the interface to the clean region, where CO is adsorbed more strongly (recall that the absolute values of adsorption energies follow the trend 𝑡𝐶 > 𝑡𝑀𝑜 ≈ 𝑡𝐶𝑖𝑛 ≈ 𝑡𝐴𝑢 > 𝑡𝑀𝑜𝑖𝑛). Although the transition probability for CO adsorption (wads,CO) is the same for all sites and the lowest transition probability for CO desorption (wdes,CO) involves a desorption from 𝑡𝐶, the net number of executions for CO adsorption at 𝑡𝐶 is considerably lower than at 𝑡𝑀𝑜, 𝑡𝐶𝑖𝑛 or 𝑡𝑀𝑜𝑖𝑛 (Figure 3a). The key to understand this unexpected result lies in the repulsive lateral interactions between neighboring CO species. At the initial steps of the simulation, the first adsorbed CO molecules occupy the empty 𝑡𝐶 sites, until 𝜃𝐶𝑂 ∗ 𝑡𝐶 reaches a value of ~30%. At this point, the transition probability for CO desorption on the clean region 11


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has been significantly increased due to the strong repulsive interactions between the nearby CO molecules accounted by the cluster expansion approach. Therefore, the net number of executions for CO adsorption on the clean region becomes lower and the interface region becomes the main recipient of the newly adsorbed CO molecules. In the case of water, the diagram in Figure 2c suggests that the interface region is also responsible for 73% of the total water adsorption processes. However, this is the only special case in which the diagram fails to describe the real contribution of each region. The reason is a continuous flow of water molecules that adsorb on the interface, diffuse towards the Au cluster, and finally desorb, as shown in Figure 3a. This leads to a higher net number of step executions 𝑟𝑒𝑣 (𝑛𝑓𝑤𝑑 𝑒𝑥 ― 𝑛𝑒𝑥 ) at 𝑡𝑀𝑜𝑖𝑛 compared to that at 𝑡𝑀𝑜. However, the region that contributes to the adsorption

of water molecules that will subsequently dissociate is the clean region. Water dissociation, the first step of the WGSR after the adsorption of reactants, takes place mainly at the clean region (84%). The reason is that water adsorption at the interface region is so weak that most likely it will diffuse towards the Au cluster and end up desorbing, as already mentioned. Moreover, the energy barrier for dissociation at the clean region (0.53 eV) is lower than at the interface region (0.79 eV) or at the Au adcluster (1.23 eV). This is not surprising since Au surfaces are not good candidates to split water.55,56 Atomic H species produced after water dissociation may diffuse from the clean to the interface region, as indicated by the positive net flux shown in Figure 3, or alternatively may react with a neighbouring H to produce molecular H2. Due to the competition between diffusion and H2 formation, molecular hydrogen is formed in similar amounts both at clean and interface regions as well as at the frontier between them. Whenever H2 formation takes place at the frontier, the resulting molecular hydrogen ends up adsorbed in the interface region (see Figure 1 and processes 46 and 47 in Table S1). Thus, the desorption of H2 occurs mainly in the interface region, as indicated by the pie chart in Figure 2c. As stated above, the associative mechanism is responsible for CO2 production through both COOH→CO2+H and COOH+OH→CO2+H2O routes (Table 2). The mechanism begins with the formation of the COOH intermediate, which takes place mainly at the frontier region (67%), followed by the clean region (26%) and the interface region (7%). Indeed, 97% of COOH produced at the frontier corresponds to the reaction between 𝐶𝑂 ∗ 𝑡𝐶𝑖𝑛 and 𝑂𝐻 ∗∗ (𝑡𝐶,𝑡𝑀𝑜) (process 27 in Table S1), instead of 𝐶𝑂 ∗ 𝑡𝐶 and 𝑂𝐻 ∗ 𝑡𝑀𝑜𝑖𝑛 (process 26). The reason is that OH is mainly produced at the clean region, where water dissociates, and therefore 𝜃𝑂𝐻 ∗∗ (𝑡𝐶,𝑡𝑀𝑜)(~30%) > 𝜃𝑂𝐻 ∗ 𝑡𝑀𝑜𝑖𝑛(~4%). In short, the interface region acts as an attractor for CO molecules, which subsequently react with OH molecules produced in the clean region to form COOH. This is a clear example of metal-support synergy. Once COOH is formed, it can produce CO2 through direct dissociation or assisted by OH. The direct COOH

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dissociation route accounts for ~42% of the total CO2 molecules produced at 465 K (Table 2) and takes place mainly at the clean region (79%) followed by the frontier region (21%). Since only 26% of COOH species are formed at the clean region, it turns out that, whenever a COOH is formed in the clean region, it always follows the dissociative pathway. Moreover, the process statistics show that COOH dissociation-formation cycles occurring at the clean MoC surface also take place at the Au(0.15 ML)/MoC system (Figures 2a). Alternatively, whenever a COOH molecule is formed in the frontier or the interface regions, most likely it will follow the route reacting with OH to produce CO2 and H2O, as shown in Figure 2c. This result agrees with the predictions made from the calculated energy profiles since the presence of the Au cluster lowers the energy barrier of this process from 0.37 eV at the bare MoC surface to 0.29 and 0.22 eV at the frontier and interface region, respectively. At the same time, the presence of the supported Au clusters increases the energy barrier for the direct COOH dissociation route from 0.27 eV on the clean region to 0.62 and 0.97 eV on the frontier and the interface region, respectively. Figure 3b plots the net numbers of step executions for different processes belonging to the associative mechanism. The presence of the COOH dissociation-formation cycles is shown by the negative net number of step executions for process 32. Moreover, it also shows that the second route is dominated by processes 35 and 36 corresponding to the reaction between COOH from the frontier region with OH from the clean region. In summary, the present results show that the Au clusters contribute to COOH formation in their vicinity and thus enhance the associative mechanism by lowering the energy barrier for COOH+OH reaction. Furthermore, the kMC simulations show that the redox mechanism only accounts for ~0.2% of the total CO2 molecules produced at 465 K (Table 2), and follows the OH recombination route (OH+OH→H2O+O) rather than direct OH dissociation, as often assumed.20 This first step takes place both at the interface region (46%) and at the Au region (54%), where the energy barrier is 0.44 eV only, substantially lower than on the clean region (2.22 eV), the barrier of which is exceedingly high for the reaction to occur. The next step is CO oxidation by atomic O to produce CO2, which takes place at the interface region. The DFT calculations reported on Ref. 2 argued that Au clusters promote the redox mechanism by allowing the formation of O species, and suggested that this could be the explanation for the high catalytic activity observed. The present kMC simulations confirm that the presence of Au adclusters actually promotes the redox mechanism, but this is not responsible for the high catalytic activity, since it contributes very little to the overall TOF. Once CO2 has been formed by any reaction mechanism, the last step is desorption. Figure 2c shows identical pie charts for both COOH formation and CO2 desorption. This is justified by the fact that > 99.8% of all CO2 molecules produced come from the associative mechanism, with COOH as a precursor. Therefore, CO2 will

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always be formed in the same site where COOH was adsorbed prior to subsequent reaction and, once formed, it will go backwards or desorb. CONCLUSIONS. To the best of our knowledge, this work provides, for the first time, a univocal and detailed analysis of all the intricacies between metal particles and the underlying support which is based on the net number of executions in the kMC simulations. By considering the WGSR on Au/MoC as a case example, we unveil the synergic effects on bifunctional catalysts and quantify the contribution of each region to the different elementary steps thus paving the road for similar studies on other supported metal catalysts. In particular, the present analysis shows that the increased activity of Au(0.15ML)/MoC relative to clean MoC is due to a clear synergy between the metal and the support. At low Au coverage the cooperative work between the different regions of the catalyst emerges as the clue for the experimentally observed increase in TOF. This is a crucial feature that cannot be anticipated by using only the DFT-calculated energy profiles. Clean MoC regions are responsible for adsorbing and dissociating water molecules thus providing surface OH species. Au adclusters contribute to the COOH formation in their vicinity, lowering also the energy barrier for COOH reaction with OH. This further enhances the associative mechanism and allows for a faster desorption of reaction products. The present work shows that kMC simulations are able to provide a clear explanation for the positive SMSI in the WGSR, dealing with a complex system such as Au/MoC, and involving a very large number of elementary steps taking place simultaneously at different regions of the catalyst. This type of simulations and analysis are general enough to be applied to other catalytic systems of interest and lead to insight well beyond that obtained from free-energy landscapes only, especially once the DFT profile is at hand. Our approach will hopefully help experimentalists in the rational development of improved catalysts. CONFLICTS OF INTEREST The authors declare no conflict of interest.

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Table 1. Calculated TOFs and coverage of all adsorbed species at T = 465 K, PCO = 20 torr, PH2O = 10 torr for the three different model systems investigated. The standard errors of the average over five different kMC replicas is also shown. TOF (molec·s-1·Å-2) 𝜃𝐶𝑂(%) 𝜃𝐶𝑂2(%) 𝜃𝐻2(%) 𝜃𝑂𝐻(%) 𝜃𝑂(%) 𝜃𝐶𝑂𝑂𝐻(%) 𝜃𝐻𝐶𝑂(%) 𝜃𝐻𝐶𝑂𝑂(%) 𝜃𝐻2𝑂(%) 𝜃𝐻(%)

Clean MoC 45 ± 2 2.79 ± 0.08 0.01 ± 0.01 0.07 ± 0.03 21.21 ± 0.09 1.4 ± 0.4 0.29 ± 0.05 2.6 ± 0.1 0 25.7 ± 0.1 10.6 ± 0.4

Au(0.15 ML)/MoC 346.9 ± 0.6 16.25 ± 0.02 0.08 ± 0.01 0.18 ± 0.01 8.98 ± 0.01 0.73 ± 0.01 4.99 ± 0.02 1.48 ± 0.01 0 24.23 ± 0.02 17.26 ± 0.07

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Au(0.25 ML)/MoC 79.9 ± 0.1 24.05 ± 0.02 0 0 2.98 ± 0.03 0 8.04 ± 0.05 n/a n/a 18.98 ± 0.03 8.03 ± 0.03

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Table 2. Relative contribution of each reaction mechanism to the overall TOF at PCO = 20 torr, PH2O = 10 torr and several temperatures for the Au(0.15 ML)/MoC system. % CO2 molecules produced by mechanism T (K)

CO+O→CO2

COOH→CO2+H

COOH+OH→CO2+H2O

410

0.0

36.8

63.2

425

0.1

36.8

63.1

435

0.1

37.9

62.0

450

0.1

39.5

60.4

465

0.2

41.7

58.2

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Figure 1. Atomic structure of the simulated systems (top view, only the surface atoms are shown) and the corresponding lattice model for the three studied cases. Black lines show the unit cell

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Figure 2. Catalytic regions and cycles. a, Scheme of the COOH dissociation-formation cycles occurring in the clean MoC and Au(0.15 ML)/MoC surfaces. b, Map of the different regions for the Au(0.15 ML)/MoC catalyst. c, Relative contribution based on total net numbers of step executions of the different Au(0.15 ML)/MoC regions to the different steps of the WGSR. Reaction conditions are T = 465 K, PCO = 20 torr and PH2O = 10 torr.

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Figure 3. Total net numbers of step executions for a, all diffusion steps as well as all H2O and CO adsorption and desorption processes and b, COOH direct and assisted by OH dissociation processes occurring on the Au(0.15 ML)/MoC catalyst. Reaction conditions are T = 465 K, PCO = 20 torr and PH2O = 10 torr. The numbers in black refer to the process ID in Table S1

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ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: Surface coverage as function of reaction temperature, additional details on the DFT calculations and the kMC simulations, calculation of formation energies, calculation of transition probabilities, and values of formation energies and energy barriers. The input files for Zacros software have been made available on a public GitHub repository. Link: https://github.com/hprats/KMC_WGSR_Au_MoC.git ACKNOWLEDGEMENTS This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The research carried out at the Universitat de Barcelona was supported by the Spanish MINECO/FEDER CTQ2015-64618-R and, in part, by Generalitat de Catalunya (grants 2017SGR13 and XRQTC). H.P.G. acknowledges financial support from Generalitat de Catalunya predoctoral FI-DGR-2015 grant and F.I. acknowledges additional support from the 2015 ICREA Academia Award for Excellence in University Research. Financial support from Spanish MINECO through the Excellence María de Maeztu program (grant MDM-2017-0767) is also gratefully acknowledged. The authors are thankful for the computational time provided at Marenostrum-IV supercomputer of the Barcelona Supercomputing Centre (BSC) through grants awarded by the Red Española de Supercomputacion (RES). Finally, we thank Dr. Federico Calle-Vallejo for critically reading the manuscript.

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Kinetic Monte Carlo simulations unveil synergic ... - ACS Publications

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