Research Article Cite This: ACS Catal. 2018, 8, 3447−3453
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Selectivity of Synthesis Gas Conversion to C2+ Oxygenates on fcc(111) Transition-Metal Surfaces Julia Schumann,†,‡ Andrew J. Medford,†,‡,§ Jong Suk Yoo,†,∥ Zhi-Jian Zhao,†,‡,⊥ Pallavi Bothra,†,‡ Ang Cao,† Felix Studt,‡,# Frank Abild-Pedersen,‡ and Jens K. Nørskov*,†,‡ †
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SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States ‡ SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States S Supporting Information *
ABSTRACT: Using a combined density functional theory and descriptor based microkinetic model approach, we predict production rate volcanos for higher oxygenate formation on (111) transition-metal surfaces. Despite their lower activity for CO conversion compared to stepped surfaces, (111) transition metal surfaces bring the potential for selectivity toward C2+ oxygenates. The volcano plots can be used to rationalize and predict activity and selectivity trends for transition-metal-based catalysts.
KEYWORDS: density functional theory, higher alcohol synthesis, syngas conversion, heterogeneous catalysis, microkinetic modeling, probability, catalyst design
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INTRODUCTION The conversion of synthesis gas directly to higher-valued chemicals and fuels like ethanol is an extremely desirable process route.1−3 The advantage compared to “bioethanol” produced by fermentation of sugars is that a heterogeneous thermal reaction can make use of all biomass or natural gas instead of competing with food resources.1 Another attractive feature is that if we have a way to produce renewable hydrogen, carbon-neutral fuels can be produced by CO2 recycling.4 The field of research has focused mainly on the development of a catalyst with an acceptable activity and selectivity toward C2+ oxygenates, and the only elemental metal found to exhibit some selectivity toward the desired product is Rh.1−3,5 Previous theoretical studies have shown that under-coordinated sites such as steps on transition metals suffer from low selectivity toward C2+ oxygenates, and experimental evidence that this is correct has been found.5 In the present paper we have focused on the (less active) terrace sites, where an increase in C2+ oxygenate production is believed to occur through a compromise between the lower activity of terrace sites and an enhanced selectivity combined with a higher number of active sites. To realize this, we need to understand the pattern of selectivity on different surface orientations and tie that to our modeling framework, which will then guide our catalyst design efforts and help predict better catalysts for the synthesis of higher oxygenates. © 2018 American Chemical Society
In the last two decades, density functional theory (DFT) has become one of the most widely used computational methods to study heterogeneously catalyzed processes.6 The atomic level information provided by DFT calculations offers insights into the mechanistic aspect of catalytic reactions to a degree that currently is inaccessible experimentally. In addition, recent developments of new and more accurate exchange correlation functionals, combined with an exponential increase in computational speed, have enhanced the quality and the quantity of studied systems, resulting in significant progress in screening studies for new catalytic materials based on DFT.7−9 The accuracy of modern DFT functionals is still not satisfactory. A simple estimation for a barrier with an ±0.2 eV uncertainty would correspond to a four-order-of-magnitude uncertainty in the rate constant at 523 K for this single step. The success of DFT calculations for catalysis comes from the ability to reliably predict trends. For a complex reaction network, it is difficult to predict how the energy uncertainties propagate through multiple layers of simulations, from DFT calculated barriers to the rate estimated by, e.g., microkinetic modeling. As shown in a previous study by Medford et al.,10 although the rate and selectivity volcano plots for the synthesis Received: January 16, 2018 Revised: March 7, 2018 Published: March 13, 2018 3447
DOI: 10.1021/acscatal.8b00201 ACS Catal. 2018, 8, 3447−3453
Research Article
ACS Catalysis
Scheme 1. Considered Reaction Pathway for the Formation of Ethanol, Acetaldehyde, Methanol, and Methanea
a
Blue arrows represent hydrogenation reactions, green arrows C−O bond dissociation reactions, and red arrows C−C coupling reactions.
higher alcohol formation10 (see Scheme 1). All 37 intermediates and transition states were calculated on five or six fcc(111) transition metal surfaces. To describe the thermodynamic and kinetic properties of the reaction pathways, we use that adsorption energies of surface species on a given surface orientation can be scaled with certain reaction intermediates or descriptors.15 These so-called scaling relations were already previously successfully used to describe the reactions for methane and methanol formation8,16 and also more complex reaction networks as higher alcohol synthesis on (211) transition-metal surfaces.10 We identified the binding energies of CO and OH as good descriptors for all adsorption energies (due to scaling relations) with a reasonable mean average error (MAE) of 0.15 eV. Thus, a two-descriptor-based model was chosen to build a microkinetic model of the CO hydrogenation on (111) transition metal surfaces (see Figure 1 for scaling relations). The uncertainty on the calculated adsorption energies and the effect on the scaling relations is estimated using the BEEF− vdW error estimation ensemble.11,13 Due to error correlations, the deviation in the slope of the individual scaling lines become small. The average standard deviation of the slope is 0.07 and the standard deviation of the intercept is 0.25 eV (Figure 1). It was shown previously that including adsorbate−adsorbate interactions is important for surfaces that bind intermediates strongly.16 In our model, we specifically included adsorbate− adsorbate effects from coadsorbed CO on the Rh(111) surface.5 Including adsorbate−adsorbate interaction limits the CO coverage to a maximum of ∼0.5 ML at typical reaction conditions (see the Supporting Information for coverage plots). By preventing CO poisoning on strongly adsorbing surfaces, empty sites are available which enable CO dissociation. Additionally, adsorbate−adsorbate self-interaction was included for CHO*, CHOH*, CH*, and OH* adsorbates. Those effects are not relevant for the maxima of the rate volcano for acetaldehyde production but lead to changes of the strong binding side of the methane volcano.
gas conversion to higher alcohols were successfully constructed, the error bars on the calculated descriptor properties for different transition metals can be significant. This challenge was addressed in a recent study by Medford et al.11 It was shown based on a Bayesian error estimation functional (BEEF) scheme that the predicted error on calculated rates can be reduced because the errors of DFTcalculated energies are correlated with each other. In other words, a more accurate prediction on the catalytic rate, as well as the selectivity, can be made if one takes the correlation between the errors in DFT calculations into account. Based on this idea, we can combine the Bayesian error estimation ensemble analysis with the widely used volcano plot procedure for catalyst screening. Unlike the descriptor-rate or descriptorselectivity volcano plot, a new and more reliable descriptorprobability volcano plot can be generated, in which the correlation between the errors in DFT calculations are considered.
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METHODS DFT calculations are performed with the plane-wave based Quantum ESPRESSO code.12 We employ the generalizedgradient approximation applying the van der Waals corrected exchange-correlation functional BEEF-vdW.13 Details about the calculations and methods used here can be found in the Supporting Information. The microkinetic modeling was carried out using the CatMAP code14 that applies a mean-field approach and the steady-state approximation including a self-consistent description of adsorbate−adsorbate interactions. Two surface sites are included in the simulation: a “hydrogen reservoir” site and a site for all other intermediates.5 The reasoning for the two site model is that hydrogen only interacts weakly with other adsorbates, and we assume that it does not have to compete for adsorption sites with any of the other adsorbates. We adopt the reaction mechanism as suggested for a Rh(111) surface by Yang et al.5 In this model, C2+ oxygenates are formed through the coupling of CO and CH, the latter of which is formed by splitting the C−O bond in CHOH (see Scheme 1). To widen the applicability of our model, two pathways for methanol formation were added. Additionally, a pathway was added where CO bond splitting occurs from CH3O and C−C coupling proceeds via CO insertion to CH3 as it was shown for (211) surfaces that it is a relevant pathway for
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RESULTS AND DISCUSSION In qualitative agreement with what has previously been observed on the more active stepped fcc(211) transitionmetal surfaces,10 we see a maximum in the rate of methane formation at strong CO and OH binding energies (see Figure 2). We note that the maximum is shifted to stronger CO 3448
DOI: 10.1021/acscatal.8b00201 ACS Catal. 2018, 8, 3447−3453
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Figure 1. Formation energies of intermediates and transition states are plotted as a function of CO binding energy (ΔECO*) and/or OH binding energy (ΔEOH*). Scaling parameters are given in the insets. Errorbars and uncertainties of scaling parameters represent one standard deviation of the DFT binding energy determined from the BEEF ensemble.
binding energies compared to the stepped sites to compensate for the lower CO dissociation activity of terrace sites. The dependence on OH binding energy is less strong close to the maximum of the methane rate volcano. The volcano for the methanol production rate shows two maxima. The maximum located in the region of weak hydroxyl and strong carbon monoxide binding corresponds to a pathway on which C−O bond dissociation is slow and methanol is formed via hydrogenation of CHOH to CH2OH. This is in agreement with the reaction pathway that has been found for Pd(111).17
The second maximum, which is more relevant for the currently applied copper-based methanol synthesis catalysts, is located at weaker CO and stronger OH binding energies. Here, methanol formation proceeds via hydrogenation of CHO to CH2O and further to methanol as was reported previously for stepped surfaces.10 Of course, in the industrial-like copper-based catalyst, the activity toward methanol will be dominated by the steps. However, the first pathway has not been considered before for the reaction network on (211) transition-metal surfaces.10 New candidates for highly active methanol synthesis 3449
DOI: 10.1021/acscatal.8b00201 ACS Catal. 2018, 8, 3447−3453
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Figure 2. Rate volcano plots for (a) ethanol, (b) acetaldehyde, (c) methanol, and (d) methane production on (111) transition metal surfaces as a function of the descriptors CO binding energy (ΔECO*) and OH binding energy (ΔEOH*) at 523 K and 20 bar (CO:H2 1:2, pCH3OH = pCH4 = pCH3CH2OH = pCH3CHO = pH2O = 10−19 bar). The included error bars show the uncertainty in the DFT binding energies as determined from one standard deviation of the BEEF ensemble calculations. CO and OH binding energies are formation energies of the adsorbed species relative to CO, H2, and H2O gas-phase energies.
Figure 4. Acetaldehyde selectivity volcano for fcc (111) surfaces at increasing product pressures corresponding to simulated increase of CO conversion. Figure 3. Selectivity toward acetaldehyde for fcc (111) transitionmetal surfaces as a function of the descriptors CO binding energy and OH binding energy at the same conditions as in Figure 2. Points with white labels were used to construct the scaling relations, while orange labels belong to metals and binary alloys for which the CO and OH descriptor energies only were calculated.
As can be seen from Figure 3, the region of increased acetaldehyde selectivity is a narrow window between the regions with high methane and high methanol selectivity. In contrast to the previous volcano plot developed for the (211) transition metal surfaces, this narrow region is now shifted to stronger CO-binding energies and closer to pure transitionmetal surfaces. This provides a possible explanation for the observed selectivity toward higher C2+ oxygenates on Rh.19 Although the turnover frequency (TOF) is several orders of magnitude higher on step sites, the selectivity toward higher oxygenates suffers from the fast C−O bond dissociation. On terraces the rates are much lower, but due to the more difficult C−O bond splitting, higher oxygenate production can compete with methane formation. This structure sensitivity of Rh was previously reported and explains the observed inverse relationship of rate vs higher oxygenate selectivity.5 Additionally, our calculations show that Co(111) may be a promising candidate with selectivity toward higher oxygenates. As for Rh, we propose that if the steps can be blocked, Co should show
catalysts might be found in this region of the descriptor space. However, the challenge will be to identify new materials with strong CO, but weak OH binding energy, compared to, e.g., Pd, Ir, or Rh. Many oxide promoters result in the opposite, namely strengthening of oxygen binding, with only a weak effect on CO binding.18 The unusual, sharp change of the rate toward the strong binding side of the volcanos in Figure 2b−d) is a result of the inclusion of adsorbate−adsorbate interactions. In those regions of the volcanos, there is a sharp increase in OH*, CH*, or CH2* coverage which leads to a noncontinuous change in adsorption energies and consequently rates. 3450
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Figure 5. Probability plots for fcc (111) surfaces. Plotted is the probability that the TOF toward (a) acetaldehyde, (b) methanol, and (c) methane is higher than 10−7 s−1. The second row shows the probability that the selectivity toward (d) acetaldehyde, (e) methanol, and (f) methane is higher than 10%.
similar or higher selectivity toward higher oxygenates. Although pure Co catalysts behave as a traditional Fischer−Tropsch catalyst associated with high alkane selectivity, addition of, e.g., sodium leads to an increase of higher oxygenate selectivity.20 Also for CuCo-based21,22 or PtCo-based23 catalysts the selectivity is tuned toward oxygenated products, according to the literature. The activity is most likely dominated by the alloyed step, but another possible explanation for the role of Cu or Pt, respectively, could be to block the hydrocarbon-selective Co step sites, such that Co terraces are responsible for the observed higher oxygenate productivity. For certain Co-rich alloys, such as Co3Pt and Co3Fe, we predict that also the (111) terrace could show some selectivity toward C2+ oxygenates. However, the more important role must be blocking of the Co steps to prevent the much faster hydrocarbon formation there. Most Cu-based alloy terraces are not active enough for higher oxygenate formation (see Figure 3). This will most likely be different on the more active Cu-alloy step, in agreement with our earlier work on stepped surfaces.10 One Cu-based alloy terrace, CuNi (111), seems promising in terms of higher oxygenate selectivity. Our microkinetic model does estimate a very low rate for ethanol formation (max TOF = 2.0 * 10−6 s−1), while the formation of acetaldehyde is appreciable (max TOF = 0.02 s−1). This is in qualitative agreement with previous results on Rh(111).5 One reason for the low ethanol rate is that the simulated conditions correspond to 0% conversion, with extremely small background pressures of the products. That leads to a very low free energy of acetaldehyde in the gas phase, thus the desorption of acetaldehyde is favorable vs further hydrogenation toward ethanol. At higher product pressures we see an extremely fast equilibration between acetaldehyde and ethanol on surfaces with a strong CO adsorption energy. The equilibrium pressures between ethanol and acetaldehyde differ by about an order of magnitude, depending on the hydrogen
gas pressure. Thus, it is likely that the alcohol will dominate over the aldehyde in the product distribution of the oxygenated species. The volcano maximum for the acetaldehyde selectivity from CO hydrogenation on the other hand does not shift with increased conversion (see Figure 4). Furthermore, experimental results have shown that for Rh-based catalysts Fe-based impurities or promoters are responsible for a higher ethanol/ acetaldehyde ratio.5,24 That means that the intrinsic Rh(111) surface is selective toward acetaldehyde. Other experimental studies have found a particle size dependency for the ethanol/ acetaldehyde ratio.25 However, the most important and difficult step for higher oxygenates is the C−C coupling while maintaining the oxygen functionality. Hydrogenation of acetaldehyde to the alcohol is comparably easy and not the limiting factor for the reaction. When doing this kind of analysis, it is important to keep in mind the uncertainties and possible errors introduced into the modeling.26−28 Especially when evaluating selectivity, small differences in the transition state energies can result in very large differences in the product selectivity. On one hand, there are errors that come from the DFT-calculated formation energies, typically around ∼0.2 eV; on the other hand, there are deviations from the scaling relations that can lead to differences between predictions from the volcano plot and single surface calculations.27 The uncertainties associated with the DFT energy have been evaluated using the BEEF ensemble averages. By solving the microkinetic model for an ensemble of 350 formation energies for each adsorbate and gas-phase species included in the model, we checked how robust the predictions are. More specifically, with this procedure we assessed the error associated with the exchange-correlation part of the DFT energies. All other possible sources of error, due to the surface model, reaction mechanism, and descriptor-based dimensionality reduction, are not included in this uncertainty estimation.11 3451
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We are analyzing the ensemble of solved microkinetic models by plotting the probability that a certain point on the map has a rate higher than a threshold TOF, or selectivity, respectively. The results are shown in Figure 5. We find that the maxima of the production rate and the selectivity volcanos agree with our original prediction. This is mainly due to the correlation of DFT errors and as a result provides confidence in the outcome of our predictions.
(F.S.) Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-HelmholtzPlatz 1, 76344 Eggenstein-Leopoldshafen, Germany, and Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstr. 18, 76131 Karlsruhe, Germany. Author Contributions
All authors have given approval to the final version of the manuscript.
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CONCLUSION Our results demonstrate the structure sensitivity of transitionmetal catalysts for higher oxygenate synthesis. Despite the lower activity of (111) transition-metal surfaces for CO conversion, different step-to-terrace ratios can not only influence the overall turnover frequency but also lead to a significant change in selectivity for a given material. The C2+ oxygenate volcano maximum for stepped (211) surfaces was not close to a pure transition-metal surface; however, for (111) surfaces the maximum is close to Rh, a well-known catalyst with some selectivity toward oxygenates. Furthermore, we predict that a step-free Co surface also should exhibit an increased selectivity toward higher oxygenates, although we note that even a few defects could change this picture since Co(211) is highly active for the production of methane and other hydrocarbons. This new insight into the activity and selectivity pattern for transition-metal terraces can lead to a better understanding of existing catalysts for higher oxygenate synthesis and hence a knowledge-based design of better catalysts in the future. We propose that step blocking by metal oxides on transition metals such as Rh and Co will result in more selective catalysts for higher oxygenate synthesis, and our results help to explain one of the most important functions of promoter oxides added to Fischer−Tropsch and Rh-based catalysts1,20,29,30
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Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We acknowledge generous computing resources at Carbon High-Performance Computing Cluster at Argonne National Laboratory. We gratefully acknowledge support from the U.S. Department of Energy, Office of Basic Energy Sciences to the SUNCAT Center for Interface Science and Catalysis through the SUNCAT-FWP. J.S. thanks the German Academic Exchange Service (DAAD) for a postdoc fellowship.
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ASSOCIATED CONTENT
* Supporting Information S
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acscatal.8b00201. Detailed information about the computational methods as well as DFT energies used as input for the microkinetic modeling (PDF) xyz files (ZIP)
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Julia Schumann: 0000-0002-4041-0165 Andrew J. Medford: 0000-0001-8311-9581 Jong Suk Yoo: 0000-0001-6472-7004 Frank Abild-Pedersen: 0000-0002-1911-074X Present Addresses §
(A.J.M.) Georgia Institute of Technology, Atlanta, GA 30316. (J.S.Y.) Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139. ⊥ (Z.-J.Z.) Key Laboratory for Green Chemical Technology of the Ministry of Education, School of Chemical Engineering, Tianjin University, Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, China. ∥
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