A Theoretical Study of Methanol Oxidation on RuO2(110): Bridging the

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A Theoretical Study of Methanol Oxidation on RuO2(110): Bridging the Pressure Gap Allegra A Latimer, Frank Abild-Pedersen, and Jens K. Norskov ACS Catal., Just Accepted Manuscript • Publication Date (Web): 26 May 2017 Downloaded from http://pubs.acs.org on May 26, 2017

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A Theoretical Study of Methanol Oxidation on RuO2(110): Bridging the Pressure Gap Allegra A. Latimer,1 Frank Abild-Pedersen,1,2 Jens K. Nørskov1,2,*

1

SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 450 Serra Mall Stanford, California 94305, USA

2

SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States

* corresponding author: [email protected]

Keywords: methanol, ruthenium, selective oxidation, formaldehyde, pressure gap, density functional theory

Partial oxidation catalysis is often fraught with selectivity problems, largely due to the tendency of oxidation products to be more reactive than the starting material. One industrial process that has successfully overcome this problem is partial oxidation of methanol to formaldehyde. This process has become a global success, with an annual production of 30 million tons1. While ruthenium catalysts have not shown activity as high as the current molybdena or silver-based industrial standards, the study of ruthenium systems has the potential to elucidate which catalyst properties facilitate the desired partial oxidation reaction as opposed to deep combustion due to a pressure-dependent selectivity “switch” that has been observed in ruthenium-based catalysts. In this work, we find that we are able to successfully rationalize this “pressure gap” using near-ab ACS Paragon Plus Environment

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initio steady-state microkinetic modeling on RuO2(110). We obtain molecular desorption prefactors from experiment and determine all other energetics using density functional theory. We show that, under ambient pressure conditions, formaldehyde production is favored on RuO2(110), while under ultra-high vacuum pressure conditions, full combustion to CO2 takes place. We glean from our model several insights regarding how coverage effects, oxygen activity, and rate-determining steps influence selectivity and activity. We believe the understanding gained in this work might advise and inspire the greater partial oxidation community and be applied to other catalytic processes which have not yet found industrial success.

One of the foremost challenges in catalysis is to obtain partial oxidation selectivity for reactions in which the partially oxidized products are more reactive than the reactants2–4. One heterogeneous catalytic process that has successfully overcome this difficulty is partial methanol oxidation to formaldehyde. The success of this industrial process is highlighted by the 30 million tons of formaldehyde that were produced worldwide in 20121,5–7. A deeper understanding of the catalyst properties that facilitate high partial oxidation selectivity in these processes has the potential to inform research throughout the field of partial oxidation catalysis. Most commonly, formaldehyde is produced through partial oxidation of methanol on silver or molybdenum oxide catalysts5. Ruthenium oxide has also been shown to be a successful catalyst for this process8,9. While it is not as active as the industrially preferred materials, it is an intriguing case study because it seems to possess a selectivity “switch” when catalyzing oxidation reactions in either ambient or UHV pressure regimes.

At low pressures, complete

oxidation of methanol to CO2 is observed10, while at ambient pressures, formaldehyde and its derivatives are produced selectively10. One hypothesis put forward to explain this discrepancy is

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that a kinetically trapped surface oxide (RuOx) might be responsible for the desired partial oxidation observed under ambient pressures10. Herein, we provide an alternative explanation for this phenomenon. Employing ab initio mean-field microkinetic modeling, we find that the selectivity of RuO2(110) towards methanol oxidation is pressure-dependent. We show that, at ambient pressures, methanol is selectively oxidized to formaldehyde, but under UHV conditions methanol is fully combusted, providing a possible explanation for experimental observations. We employ energies derived from firstprinciples and experimentally measured desorption rate prefactors to demonstrate that the two previously observed product distributions can be united in a single reaction model. Additionally, further analysis of our results provides insight into which conditions favor partial oxidation. We find that, of the two types of surface oxygen, the coverage of the most reactive oxygens should be limited, while the coverage of less reactive oxygens should be kept high to achieve selective partial oxidation. While these less reactive oxygens are able to dehydrogenate methanol, they are unable to participate in coupling reactions to form CO2 precursor species as the more reactive oxygens readily do. Additionally, we show the change in coverage of active oxygen can be correlated to a change in rate-limiting step. In addition to unifying several previous studies, this work aims to aid in catalyst improvement and discovery by providing a detailed view of the elementary steps that govern methanol oxidation selectivity on RuO2(110). The first demonstration of partial oxidation of methanol to formaldehyde on ruthenium oxide was provided by Madhavaran et al. in 200112, whose primary focus was to show that methanol oxidation on single crystal ruthenium oxide under UHV pressure conditions and powder ruthenium oxide under ambient pressure conditions was qualitatively similar. In both instances, a low formaldehyde selectivity of only ~3% was observed. However, the nature of their


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temperature-programmed desorption experiments was such that a single monolayer of methanol was adsorbed and reacted, meaning that the products being measured were not the result of a catalytic cycle and are not appropriate for comparison to a differential reaction model. Later, in one of the first studies of catalytic methanol oxidation on ruthenium oxide, Liu and Iglesia found high selectivity towards formaldehyde and its derivatives (~90% selectivity; 20% conversion) at low temperatures and ambient pressures8. They attributed the high selectivity of their catalysts to a RuO2 active phase that allowed consumed lattice oxygens to be quickly replenished in a Mars-van Krevelen type redox cycle. This idea that fast redox cycles was beneficial for methanol partial oxidation over RuO2 was substantiated further in later work by Carr et al13. A density functional theory (DFT) study conducted by Lopez et al. seemed to agree with the high partial oxidation selectivity observed in these studies14. They demonstrated that removing hydrogen from OCH2c to form OCHc on RuO2(110) is highly unfavorable, suggesting that OCH2c is unlikely to be dehydrogenated further. However, this insight was challenged by landmark UHV experiments conducted by Blume et al10. These experiments, which ranged in temperature from 300 to 720 K and pressure from 10-9 to 10-4 bar, provided a comprehensive account of methanol oxidation on ruthenium catalysts under low to near-ambient pressure conditions, relating product selectivity and catalyst oxidation state to temperature, pressure, and oxygen concentration. Blume et al. were able to map out the activity and oxidation phase space of ruthenium catalysts, explicitly linking temperature, pressure, reactant partial pressure, and catalytic activity to the existence of a full oxide, surface oxide, or metal (Figure S1). Intriguingly, Blume et al. showed that, under conditions where RuO2 was present, methanol was fully combusted. Significant formaldehyde production was only observed at pressures of 10-4 bar, which also corresponded to the existence of a surface oxide, termed RuOx(1 < ‫ < ݔ‬2).


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They concluded that RuO2 was active for full combustion, and that RuOx was responsible for partial oxidation, and suggested that the active phase in Liu’s experiments was RuOx rather than RuO2. While RuO2 is thermodynamically stable to reduction under Liu’s reaction conditions, it has been shown that RuOx can coexist with the full oxide, especially at lower temperatures, hypothetically due to kinetic limitations of defect reoxidation15. However, Blume’s work does not necessarily rule out the possibility that RuO2 is active for selective oxidation in Liu’s ambient pressure experiments. In fact, Blume et al. demonstrate a clear pressure dependence on the selectivity of RuOx, showing that, while it is formed in a wide temperature and pressure range, it only becomes catalytically active for selective oxidation at 104

bar10. It is a distinct possibility that this pressure dependence is true for the RuO2 catalyst as

well, potentially achieving selective oxidation only at pressures higher than those measured under UHV (below 10-4 bar). While we acknowledge the likely coexistence of RuOx alongside of RuO2 and the distinct possibility that it is the active phase for methanol oxidation in Liu’s experiment, we propose an alternative explanation for the pressure gap discrepancy. Namely, that the bulk oxide, RuO2 could be responsible for both of the observed product distributions, exhibiting selectivity to CO2 under UHV conditions, and switching to partial oxidation selectivity under ambient pressures, which is in line with Blume et al. observations of the pressure-dependence of partial oxidation selectivity on RuOx. Unfortunately, the idea that RuO2 might exhibit a selectivity switch akin to that of RuOx at higher pressures is difficult to test experimentally. Only recently have nearambient pressure X-ray photoelectron spectroscopy (XPS) techniques been demonstrated that successfully characterize materials at intermediate pressures up to 10-2 bar16–20. Therefore, while Blume and coworkers were able to successfully link the different Ru oxidation state to their


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corresponding product distributions under pressures as high as 10-4 bar, their conclusions cannot necessarily be translated to reactions occurring under higher pressure conditions. Given the difficulties associated with ambient pressure characterization techniques, computational methods can play an important role in helping to bridge the gap between low and ambient pressure experiments in situations like these. Herein, using an ab initio microkinetic model, we map out the full pressure-temperature selectivity map of RuO2(110). We demonstrate that the pressuredependence observed in methanol oxidation selectivity may not be due to a change of catalyst oxidation state, but rather to a change in dominant adsorbate coverage on RuO2(110). Our model obtains reaction rates by using a mean-field, steady-state approximation at 1% conversion. As RuO2(110) is a monodimensional system in which diffusion barriers and lateral interactions may be significant21, kinetic Monte Carlo (kMC) modelling might constitute a more rigorous approach. However, the large number of elementary steps involved in methanol oxidation renders such an approach highly computationally intensive. As we will demonstrate, the kinetics relevant to our purposes correspond to high adsorbate coverages, where diffusion will be negligible. Additionally, in agreement with previous work22, we find lateral interactions are minimal between cus adsorbates (Table S1). Given these conditions, as well as previous demonstrations on RuO2(110) showing kMC and mean-field to provide qualitatively similar results21,23, we believe the mean-field approximation provides a sufficient foundational framework for understanding selective methanol oxidation. The adsorption and transition state energies used in the model are obtained from ab initio density functional theory (DFT) calculations. Energies are calculated on RuO2(110), as this surface has previously been shown to have the most thermodynamically favorable free energy under the high temperature conditions that most syntheses require,24,25 and is also the preferred growth orientation on hcp(0001) and


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fcc(111), which is particularly relevant for single crystal surface science experiments26,27. While there has been recent work suggesting RuO2(111) to be more stable under many relevant reaction conditions, we do not see any evidence for nanoparticle reconstruction in the work we are comparing to and therefore assume RuO2(110) remains intact25.

The stoichiometric (110)

surface consists of rows of coordinatively unsaturated (cus) ruthenium atoms separated by rows of bridging oxygen atoms (Fig 1a). Fig. 1b details the intermediates and pathways considered in our microkinetic model, with binding at either bridge or cus sites denoted by superscript “b” or “c.” For the sake of simplicity, our microkinetic model considers only two products of methanol oxidation: formaldehyde and carbon dioxide. While other products, such as methyl formate and dimethoxymethane, have been observed experimentally, they are believed to be products of formaldehyde coupling8. Therefore, any selectivity toward formaldehyde in the model can be considered to include all possible formaldehyde derivatives. A complete list of the elementary steps included in the model and corresponding energies can be found in the supplementary information (Table S2).

(A)


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(B) Figure 1. Surface geometry and schematic of methanol oxidation pathways. (a) RuO2(110) with bridge oxygens and coordinatively unsaturated (cus) metal sites labeled. (b) Intermediates and pathways in methanol oxidation. A superscript “c” denotes adsorption at a cus site, while “b” refers to adsorption at a bridge site. Bidentate adsorbates bound to two neighboring cus sites are labeled “cc”.

We consider many of the same steps as were analyzed by Lopez et al. in their 2010 study14, with a key difference being the inclusion of the OCH2Occ dehydrogenation steps. In the previous publication, which forwent a kinetic analysis, it was assumed that any OCH2Occ formed on the surface could not be further oxidized and would equilibrate with OCH2c and Oc, facilitating formaldehyde desorption. This suggested that, while CO2 was the thermodynamically preferred product, there existed no channel for it to be formed on RuO2(110). Lopez et al. were led to conclude that RuO2(110) would selectively produce formaldehyde as opposed to CO2. However, in our analysis, we find that OCH2Occ and OCHOcc may be further oxidized with reasonable barriers via dehydrogenation by a bridging oxygen (Figure 2a,b). The consideration of these additional elementary reactions opens a viable channel to CO2 production on RuO2(110).


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Interestingly, we also find that it is favorable by -0.3 eV for OCH2c to combine with a bridging oxygen to form OCH2Ocb. However, oxidation of OCH2Ocb is much more kinetically hindered than that of OCH2Occ. The first and second dehydrogenations of OCH2Ocb to form CO2 possess barriers of 1.4 and 2.2 eV, respectively (Figure 2c,d), compared to the 0.8 and 1.4 eV barriers to dehydrogenate OCH2Occ (Figure 2a,b). Therefore, under relevant temperatures, CO2 formation from OCH2Ocb can be assumed to be negligible. Given that these barriers are high enough to ignore at low temperatures, combined with the difficulties associated with including bidentate molecules bound to different types of sites in our microkinetic model, this dehydrogenation pathway is not explicitly included.


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Figure 2: Dehydrogenation of OCHxO intermediates to form CO2. Dehydrogenations of OCH2Occ (a) and OCHOcc (b) have barriers of 0.8 and 1.4 eV, while dehydrogenation of OCH2Ocb (c) and OCHOcb (d) have barriers of 1.4 and 2.2 eV.

In addition to the inclusion of a CO2 formation channel, we determined that a second key to identifying the correct product distributions on RuO2(110) was the inclusion of desorption barriers of gas-phase species. In systems where the binding energies of fully saturated gas-phase species are small, such as metals, desorption barriers are not often considered because they are rarely rate-determining. However, in materials where the binding energies of these species is significant (for example, RuO2(110) binds fully coordinated molecules such as formaldehyde, water and methanol with enthalpies of ~1.1 eV, 1.2 eV, and 1.3 eV, respectively), desorption barriers have been measured experimentally and have the potential to drastically change the kinetic picture28. While it is not trivial to determine these desorption free energy barriers from first principles, Campbell et al. have compiled an extensive list of desorption activation energies and prefactors for a range of molecules and surfaces29.

We therefore employ these

experimentally determined desorption prefactors29 in our model, and we confirm that the experimentally measured desorption activation enthalpies are equivalent to our calculated binding energies. Details for this approach can be found in the SI. These are the only parameters in our model not derived from first principles. A consequence of using an experimental value for the desorption prefactor is that the prefactor lacks any pressure dependence, which may be physically inaccurate if the desorption transition state possesses a significant amount of gasphase character. Additionally, any error cancellation that might fortuitously occur within DFT calculations is sacrificed. For these reasons, we have included a discussion in the SI exploring


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the dependence of partial oxidation selectivity on the choice of prefactor. Perhaps unsurprisingly, the model is moderately sensitive to the methanol desorption prefactor (Figure S2), but we find that the ability of the model to match existing experimental data and the main conclusions drawn are not compromised when the prefactor is changed slightly. The rates of product turnover obtained from solving a mean field microkinetic model to steady state are shown in Figure 3. As previously mentioned, we have considered only two possible products, CO2 and CH2O. The production rates of these two species is shown as a function of temperature and pressure in the form of a color map, with red corresponding to higher turnovers and blue to lower turnovers. Additionally, results for oxygen partial pressures of 0.3 and 0.7 are included to compare to experiments done in both regimes.


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Figure 3. Turnover frequencies of CH2O and CO2 at a

௉ೀమ ௉೟೚೟

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of 0.3 and 0.7. The experimental data

represented by the shapes are outlined in Table 1.

There are three main experimental conditions in the Blume and Iglesia publications that can be compared to the model (Table 1). Conditions (a) and (b) are from the Blume publication, and here the catalyst is known to be RuO2(110) from XPS data conducted in situ. The third condition (c) is from the Iglesia publication, and, as characterization experiments were not conducted, the oxidation state of the catalyst is not known with certainty.

Table 1. Experimental conditions for methanol oxidation

As shown in figure 3, the model predicts formaldehyde selectivity under ambient pressure conditions and CO2 selectivity under UHV conditions. All three experiments seem to be in good agreement with the model. In case (a), low conversion of 8% is observed and CO2 is the only product. The model generally agrees as it predicts low CO2 turnovers on the order of 10-3 under these conditions and negligible formaldehyde production. In case (b), a higher conversion of 41% is observed. The product distribution consists mostly of CO2, but there is a small amount of formaldehyde present as well. Here, the model shows higher turnovers, approximately 10-1, for CO2, and turnovers of 10-3 for formaldehyde. Finally, under experimental conditions where


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formaldehyde is the main product (case (c)), the model predicts formaldehyde turnovers of 10-3 and negligible CO2 production. Although characterization experiments were not performed on the catalyst in case (c), these results suggest the active phase may in fact be RuO2(110). This model also points to a potential reason formaldehyde selectivity was not observed in any of Blume’s experimental conditions: at the tested pressures below 10-4 bar, the selectivity “switch” was not yet accessible. The model’s ability to successfully predict experimental data collected under different temperatures, total pressures, and reactant partial pressures suggests that it can be a useful tool in guiding further catalyst discovery in this area. To gain a deeper understanding of how and why a selectivity “switch” occurs under different experimental conditions, we compare product selectivities to dominant adsorbate coverages (Figure 4). Regions that facilitate partial oxidation are blue, whereas those that lead to total combustion are red. It is observed that changes in adsorbate coverages correspond strikingly well to changes in selectivity. Namely, a surface poisoned with OCHOcc seems to produce formaldehyde, while a surface with high Oc coverage produces carbon dioxide. Figure 4a also shows that at higher temperatures and lower pressures, the cus sites are empty. We postulate that this region may correspond to the temperature and pressure conditions where RuO2 will begin to reduce. It should be noted that, if this is the case, the model would cease to be relevant under these conditions. Notably, this is not the first instance of the pressure-dependent selectivity on RuO2(110) being due to changes in coverage. Another reaction that exhibits a selectivity “pressure gap” between “real” catalysis and surface science measurements is ammonia oxidation, the dominant products being NO under UHV conditions and N2 under ambient pressures22,30–32. A 2010 publication authored by Perez-Ramirez et al. found that the differences in selectivity could be linked to an


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additional path to N2 production opening at higher pressures where sizeable coverages appeared22.

(A)

(B) Figure 4. Relationships between coverage, rate-determining step, and selectivity (shown for ௉ೀమ ௉೟೚೟

=0.7). (a) Dominant cus adsorbate coverages, and (b) selectivity towards CH2O or CO2.

A potential limitation of the model is that it seems to suggest high formaldehyde production rates and selectivities at high temperatures and ambient pressures. However, this is likely because CO2 production via OCHxOcb intermediates was excluded from the model. While this


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approximation is certainly appropriate at the lower temperatures where all the included experimental data was collected, at higher temperatures, the barriers for OCHxOcb dehydrogenation will become surmountable at significant rates and CO2 production will likely increase dramatically. Another insight provided by this model is that the nature of the bridging sites seems to play an important role in activity (Figure 5). At low temperatures, OHb coverage is high and activity is low. However, at higher temperatures Ob coverage begins to dominate, and activity seems to increase as the coverage of OHb decreases and more bridging oxygens are available to dehydrogenate methanol. These observations suggest that the state of the bridging oxygens plays a critical role in partial oxidation selectivity, as has been noticed in several previous studies exploring oxidation reactions on RuO2(110)14,33,34. Namely, hydroxyl poisoning of the bridge sites hinders dehydrogenation. A caveat of our model lies in the significant stabilizing effect bridging hydroxyl groups seem to have on neighboring adsorbates, stabilizing the binding energies of their cus neighbors up to 0.6 eV (Table S4). Such an effect has the potential to significantly alter the free energy landscape of the surface. However, we do not include this stabilizing effect in our model because the local environment for any dehydrogenation should be bridging oxygen rather than hydroxyl, as dehydrogenation by a hydroxyl group to form H2Ob is highly unfavorable.


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(A)

(B) Figure 5. Relationship between state of bridging oxygens and activity (shown for

௉ೀమ ௉೟೚೟

=0.7). (a)

Coverage of bridge sites. Red corresponds to OHb; blue corresponds to Ob, and (b) CH2O production rates.

The important impact that cus oxygen coverage has on selectivity can be understood by examining the free energy diagrams for this reaction, as there seems to be a change in ratelimiting step when going from ambient to UHV pressures. Under ambient pressure conditions (Figure 6a) OCHOcc dehydrogenation is rate-limiting, leading to a surface “poisoned” by OCHOcc. The high coverage of OCHOcc limits the buildup of atomic oxygen at the cus sites,


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which in turn creates a higher “effective barrier” for OCH2Occ formation due to the low probability of OCH2c finding an available Oc with which to react. Because OCH2Occ formation is blocked, formaldehyde desorption is favored and the partial oxidation product is slowly and selectively produced.

However, under UHV pressure conditions (Figure 6b), the chemical

potential of gas-phase methanol drops substantially, and the rate-limiting step switches to methanol adsorption. Now, any OCHOcc on the surface will be reduced more quickly than it can be formed, allowing oxygen coverage to build up and opening the channel for CO2 formation through OCH2c and Oc coupling. This seems to imply that, at ambient pressures, a high coverage of OCHOcc is effectively transforming RuO2(110) into a pseudo-single-site catalyst, impeding full oxidation to the thermodynamically favored product by limiting the availability of oxygen. This observation is especially interesting given the success of “true” single-site catalysts such as zeolites for selective oxidation chemistry35–38.

(A)


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(B)

(C)

Figure 6. Free energy diagrams for methanol oxidation with PO2=0.7. In (a), T=400K, P=10-1 bar and methanol oxidation is selective to formaldehyde production. In (b), T=500K, P=10-9 bar and methanol is selectively converted to CO2. The rate limiting step under both sets of conditions is marked by a star. In (c), selectivity toward CH2O or CO2 is shown as blue or red, and white points mark where the activation free energy for methanol adsorption is equal to the activation free energy for CO2 formation.

The hypothesis that a change in rate limiting step accompanies the coverage change and resultant switch in selectivity can be explored by plotting the conditions where the barriers for


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CH3OH adsorption and OCHOcc oxidation are equal (white points, Figure 6c). While this metric is not a precise predictor of selectivity, the points do roughly correspond to the change in product distribution. The approximate agreement suggests that these barriers may be useful descriptors in the screening of materials to be used as selective partial oxidation catalysts. These observations seem to hint at an important criterion for selective oxidation: a successful catalyst should have a high coverage of oxygens able to dehydrogenate methanol (in this case, the bridging oxygens), but a low coverage of oxygens that might couple with OCH2 to form OCHxO species (the cus oxygens). The concept of tuning oxygen reactivity to achieve high selectivity in hydrocarbon oxidation is well-known and has been studied extensively to date39–44. It has been shown that the reactive oxygens adsorbed on oxide surfaces at non-stoichiometric sites (here, cus) are often more electrophilic, driven to insert themselves into C-H bonds. Lattice oxygens (here, bridge), however, can be thought of as more nucleophilic—they are able to deprotonate neighboring species, but their abstraction from the lattice is usually strongly uphill in energy. In the case of RuO2 specifically, it is fortuitous that the OCH2Ocb species formed by OCH2 coupling with a bridging oxygen is so energetically difficult to dehydrogenate (Figure 3). The high barriers associated with dehydrogenating OCHxOcb facilitate partial oxidation selectivity— if bridging oxygens could couple to OCH2c and be easily dehydrogenated, conditions where CH2O is formed selectively and at significant rates would not likely exist. Perhaps counterintuitively, the distance between a bridging oxygen and the H to be abstracted in OCHxOcb is ~0.4 Å smaller than it is for OCHxOcc, meaning distance is not the inhibiting factor. In designing improved catalysts for the partial oxidation of methanol, the present work supports previous claims that an appropriate design parameter might be to look for systems lacking


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electrophilic (cus) oxygen on the surface39–44. In these systems, dehydrogenation would be facile while coupling to form OCHxO species would be prevented. Herein, we have presented a microkinetic model for methanol oxidation on RuO2(110) that successfully rationalizes all available empirical data and provides a platform for further optimization of partial oxidation processes via fundamental insights into the reaction mechanism. We have shown that oxygen coverage plays an instrumental role in selectivity to the desired, partially oxidized product. Additionally, we have corroborated what others have found previously39–44 by finding that certain surface oxygen species are more conducive to partial oxidation than others. In the case of RuO2(110), reactive cus oxygens are undesirable, as they are able to couple with OCHx species to form intermediates on the path to CO2. Less reactive bridging oxygens, however, are able to dehydrogenate methanol without facilitating coupling reactions, leading to partial oxidation selectivity. We believe that these insights can not only be extended to other catalysts for methanol partial oxidation, but also more generally to the field of partial oxidation as a whole.

Supporting Information Coverage-dependent adsorbate and transition state energies and vibrational frequencies, a full list of elementary reaction steps, a plot of experimental catalyst stability from previously published data, and a discussion of the sensitivity of the model to the methanol desorption prefactor are all available in the supporting information. This material is available free of charge via the internet at http://pubs.acs.org.

Author Information


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Corresponding author: Jens Norskov, [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding Sources Support from the U.S. Department of Energy Office of Basic Energy Science to the SUNCAT Center for Interface Science and Catalysis is gratefully acknowledged. The research of AAL was conducted with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a.

Acknowledgement Support from the U.S. Department of Energy Office of Basic Energy Science to the SUNCAT Center for Interface Science and Catalysis is gratefully acknowledged. The research of AAL was conducted with Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a.

Abbreviations CUS, coordinatively unsaturated; TS, transition state; DFT, density functional theory; UHV, ultra-high vacuum; XPS, x-ray photoelectron spectroscopy;

Methods: The plane wave QuantumESPRESSO code45 and Bayesian Error Estimation Functional with Van der Waals corrections (BEEF-vdw) functional46 was used for the Density Functional Theory (DFT) calculations. The plane-wave cutoff and density cutoff were 550 eV and 5500 eV, respectively. Forces on


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all atoms were minimized to 0.05 eV Å-1. A (6,6,1) k-point sampling was employed on a 2x1 expansion of the rutile surface unit cell. Slabs were composed of four stoichiometric layers separated by 15 Å vaccum; the lowest 2 layers were kept fixed to simulate the bulk. Climbing-Image Nudged Elastic Band (CI-NEB)47 calculations were performed to determine the location of the transition state (TS). The accuracy of these TSs was verified by finding exactly one imaginary mode corresponding to the TS reaction coordinate in a vibrational analysis. Dal Corso pseudopotentials were employed for all elements except ruthenium, as it was found that this pseudopotential did not accurately describe oxygen binding on RuO2 cus sites. Therefore, the 2014 Vanderbilt pseudopotential was used instead to describe ruthenium. Microkinetic modeling was done with CatMAP48, a self-consistent mean field model.

Adsorbate-

adsorbate interactions were not included. Free energies were found by summing the electronic energies, ZPE, and entropy determined from a harmonic adsorbate approximation (vibrational frequencies given in Table S2). Gas-phase species were assumed to behave ideally.


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