Predicting Promoter-Induced Bond Activation on Solid Catalysts Using

Letter pubs.acs.org/JPCL

Predicting Promoter-Induced Bond Activation on Solid Catalysts Using Elementary Bond Orders Charlie Tsai, Allegra A. Latimer, Jong Suk Yoo, Felix Studt, and Frank Abild-Pedersen* SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States Downloaded by UNIV OF CALIFORNIA SAN DIEGO on September 13, 2015 | http://pubs.acs.org Publication Date (Web): September 4, 2015 | doi: 10.1021/acs.jpclett.5b01792

S Supporting Information *

ABSTRACT: In this Letter, we examine bond activation induced by nonmetal surface promoters in the context of dehydrogenation reactions. We use C−H bond activation in methane dehydrogenation on transition metals as an example to understand the origin of the promoting or poisoning effect of nonmetals. The electronic structure of the surface and the bond order of the promoter are found to establish all trends in bond activation. On the basis of these results, we develop a predictive model that successfully describes the energetics of C−H, O−H, and N−H bond activation across a range of reactions. For a given reaction step, a single data point determines whether a nonmetal will promote bond activation or poison the surface and by how much. We show how our model leads to general insights that can be directly used to predict bond activation energetics on transition metal sulfides and oxides, which can be perceived as promoted surfaces. These results can then be directly used in studies on full catalytic pathways.

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In this Letter, we seek to elucidate promoter-induced bond activation in dehydrogenation reactions by (1) Using density functional theory (DFT) to examine promoted C−H activation in CH4 on transition metals and to understand the origins of the promoting or poisoning effect; (2) Developing a general predictive model and validating it through comparisons with directly calculated results for C−H, O−H, and N−H bond activation in a range of reactions; (3) Demonstrating how promoter-induced bond activation on transition metals leads to general insights that are transferrable to complex catalytic materials. We find that variations in the promoting effect are entirely determined by the electronic structure of the transition metal surfaces. A simple model based on elementary bond orders is developed for rapidly determining both final state and transition state energies. A small database of atomic binding energies (determined experimentally or theoretically) is sufficient for establishing all trends and determining whether a nonmetal will promote bond activation or poison the surface. These insights are then shown to be general, as we successfully use our model to predict the energetics of bond activation on transition metal sulfides and oxides.

he bond activation of hydrogenated species is critical for a wide range of chemical processes in energy transformation, including the functionalization of hydrocarbons and the partial oxidation of fossil fuels.1−3 With a growing demand for energy, increasingly efficient means of enabling these processes are needed. Heterogeneous catalysts are a promising class of materials for dehydrogenation, where nanoparticulate transition metals are often used on the industrial scale. However, highly active catalytic systems are still needed to enable dehydrogenation at moderate operating conditions to further reduce costs. The addition of nonmetal promoters is one ubiquitously employed strategy for enhancing bond activation on metal catalysts.4−8 Adsorbed oxygen species, for example, have been shown to increase the reactivity of CH4 activation on certain noble metal catalysts.5−11 There, the promoting effect was found to originate from the dehydrogenated hydrogen adsorbing onto the oxygen species rather than the metal surface.12 On the other hand, nonmetals such as sulfur are generally found to poison the surface.13 Detailed explanations of the trends in promoted bond activation are still lacking, and it is not generally known which species will promote bond activation or poison the surface. A model that rapidly predicts the energetics of promoted bond activation could lead to the rational design of next-generation promoted catalysts. Furthermore, transition metal surfaces promoted by nonmetals could serve as prototypes for understanding active sites in transition metal compounds (oxides, sulfides, etc.), which are increasingly being considered as earth-abundant alternatives for dehydrogenation reactions.14,15 © XXXX American Chemical Society

Received: August 15, 2015 Accepted: September 3, 2015

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DOI: 10.1021/acs.jpclett.5b01792 J. Phys. Chem. Lett. 2015, 6, 3670−3674

Letter

The Journal of Physical Chemistry Letters As a guiding example, we begin with the activation of the first C−H bond in CH4 induced by adsorbed surface promoters on (111) metal surfaces: CH4 (g) + X* → CH3* + HX* (Scheme 1). Here * indicates an adsorbed species, while X = {B, C, N, P,

results could not be anticipated from the reactivity of the clean surfaces alone. To understand the trends for CH4 activation, we decompose ΔEFS for C−H activation into the adsorption energy of a methyl group ΔECH3 (i.e., CH4(g) + * → CH3* + 1/2 H2(g)) and the hydrogenation energy of the surface promoter, ΔEH−X (i.e., X* + 1/2 H2(g) → HX*). Hence, ΔEFS = ΔECH3 + ΔEH−X (SI for more details). More reactive surfaces necessarily lead to a more negative ΔECH3,22 but variations in ΔEH−X must be further explained to understand the trends in ΔEFS. In Figure 2a, ΔEH−X scales negatively with the adsorption energy of the

Downloaded by UNIV OF CALIFORNIA SAN DIEGO on September 13, 2015 | http://pubs.acs.org Publication Date (Web): September 4, 2015 | doi: 10.1021/acs.jpclett.5b01792

Scheme 1. Potential Energy Diagram for Nonmetal Promoter-Induced Methane Activationa

Gray = C, white = H, red = promoter X, gold = metal. ΔETS and ΔEFS are taken relative to the energies of gas phase CH4 and an X* covered surface. ΔETS = ETS − EI and ΔEFS = EF − EI. a

O, S, Se} is a nonmetal surface promoter. All energies are determined relative to clean or X* covered surfaces and gas phase CH4 (Scheme 1). Comparisons between the bond activation reaction energy for direct (ΔEdirect FS ) and promoted (ΔEFS) dehydrogenation (Figure 1a) indicate that, for noble

Figure 2. (a) Hydrogenation energy of the promoter ΔEH−X as a function of the adsorption energy of the promoter ΔEX where X = {B, C, N, O, P, S, Se}. The magnitudes of the slopes and their errors are shown. The associated R2 values for the fitted lines are 0.92, 0.94, 0.95, 0.74, 0.97, 0.91, and 0.91 for X = B, C, N, O, P, S, and Se, respectively. (b) ΔEX, ΔEXH, ΔEH−X, and ΔECH3 as a function of ϵd. For clarity, only X = N is shown (see SI for other promoters). The associated R2 values for the fitted lines are 0.94, 0.91, 0.92, and 0.81 for ϵd vs ΔEX, ΔEXH, ΔEH−X, and ΔECH3, respectively. Figure 1. Final state energies for the dehydrogenation of CH4. For = E(CH3* + H*), with surface direct dehydrogenation ΔEdirect FS promoters ΔEFS = E(CH3* + HX*), where X = {B, C, N, P, O, S, Se}. (a) Changes in final state energy as a result of nonmetal species. Negative indicates improvement; (b) ΔEFS grouped by promoter. More negative ΔEFS indicates more reactivity.

promoter atom ΔEX (defined as EX+surface − EX − Esurface), indicating that a strongly/weakly bound promoter leads to a less/more stable adsorption of H on the promoter. This begins to explain why reactive surfaces tend to be poisoned while noble surfaces tend to be promoted: a strongly bound nonmetal promoter will also be more difficult to hydrogenate, so direct bond activation is favored. The slopes of ΔEH−X vs ΔEX are given by the negative of the promoter’s bond order η, where η = 1/xmax and xmax is the maximum number of H atoms the central atom X can bond to (e.g., η = 1/3 for N and η = 1/2 for O). For P, the lower than expected bond order is likely due to a preferred pentavalency (η = 1/5 = 0.2). The origin of the slopes can be explained using the d-band model,22,23 where the weighted center of the dstates, ϵd, is a general descriptor for a surface’s reactivity.22,24 In Figure 2b, ΔEX, ΔEH−X, the adsorption energy of the hydrogenated promoter ΔEXH (i.e., HyX + * → HX* + (y− 1)/2 H2 (g)), and ΔECH3 are shown as a function of ϵd. Only ΔEH−X scales positively with ϵd, whereas ΔEX, ΔEXH, and ΔECH3 scale negatively. The negative scaling lines are explained

metals (Ag, Au, Cu), almost all promoters enhance bond activation significantly (up to 2 eV stabilization). For more reactive metals (Pd, Pt, Rh), P, S, and Se are found to poison the surfaces because dehydrogenation is more favorable directly on the metal (ΔEFS > ΔEdirect FS ). This is in agreement with the well-known observation that sulfur is a poison for reactive metal catalysts.13 Adsorbed B, C, N, and O lead to the most reactive systems (Figure 1b), with C/Pt, N/Au, N/Ag, B/Pt, and C/Rh having the largest stabilization for CH4 activation. In agreement with experimental observations on oxygen promoted surfaces,16−18 noble metal catalysts such as Au19−21 experience the largest enhancement in activity. No consistent pattern emerges among the magnitudes of ΔEFS (Figure 1b) so far, and the 3671


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The Journal of Physical Chemistry Letters

independent βH. Any change of ±δE in ΔEX is compensated by a ∓ηδE in ΔEH−X. The linear scaling thus dictates that a more strongly/weakly bound X will lead to more weakly/strongly bound H (or other constituent atoms). Since ΔEFS = ΔECH3 + ΔEH−X, stronger H binding arising from weakly bound X will be offset by weaker CH3 binding. This further explains why variations in ΔEFS between metals are reduced in the presence of surface promoters (Figure 1). Having explained and predicted the trends in ΔEH−X, the values of ΔEFS are easily predicted by adding ΔECH3. By analysis from the d-band model,22,25,28 the adsorption energy of any hydrogenated species AHx on a transition metal surface is ΔEAHx = γΔEA + ξ where γ = 1−η is the valency parameter and ξ is the intercept. ΔEFS for any promoted dehydrogenation of AHx is then ΔEFS = ΔEAHx + ΔEH−X and can be written as


using the standard d-band model, where increased binding strength results from a reduced filling of the antibonding states as ϵd moves up in energy.22 Since ΔEH−X = ΔEXH − ΔEX, the positive scaling of ΔEH−X results from the differences in the slopes of ΔEXH vs ϵd and ΔEX vs ϵd. It is known that the reduced slope of ΔEXH vs ϵd is given by the differences in bond order, where H−X binding leads to a slope that is (1 − η) times the slope of ΔEX vs ϵd.25 ΔEX vs ϵd thus necessarily has a steeper slope compared to ΔEXH vs ϵd. By subtraction, the slope for ΔEH−X vs ϵd is − η times that of ΔEX vs ϵd and the slope of ΔEH−X vs ΔEX is equal to − η, in agreement with fitted models. Variations in ΔEH−X are thus explained by bond order differences of the promoter (η) and the electronic structure of the surface (ϵd). ΔEH−X can therefore be written in terms of the adsorption energy of the promoter atom ΔEX as ΔE H − X = −ηΔE X + βH

(1)

ΔE FS = γ ΔEA − ηΔE X + βFS

where βH is the intercept, recovering the scaling from Figure 2a. ΔEH−X and ΔEX need only be known for one surface to determine βH. Experimental or theoretical values of ΔEX could then be used to determine the corresponding ΔEH−X, using the bond order of X. Using eq 1, ΔEH−X of all promoters can be predicted with a mean absolute error (MAE) of 0.12 eV (Figure 3a), equal to the average estimated DFT error of 0.12 eV (using BEEF-vdW26,27). The largest absolute error in the predictions is 0.5 eV.

(2)

using the equations for ΔEAHx and ΔEH−X, where βFS = βH + ξ. All trends in ΔEFS are established by the elemental bond orders of the adsorbed atoms (through γ and η) and their corresponding atomic adsorption energies, ΔEA and ΔEX. A data point for a single surface determines βFS, and experimental or theoretical values of ΔEX could be used to predict the magnitude and trends of ΔEFS. Whether or not ΔEFS from eq 2 is an improvement over direct bond activation will determine if the nonmetal promotes the reaction step or poisons the surface. Knowledge of how ΔEA and ΔEX differ is thus sufficient for anticipating the variations in ΔEFS. For CH4 activation (AHx = CH4), ΔEFS can be predicted with a MAE of 0.16 eV (Figure 3b), well below the average estimated DFT error of 0.22 eV (using BEEF-vdW). The maximum difference between the model and calculated DFT values is 0.7 eV. In eq 2, ΔEFS scales positively with ΔEA and negatively with ΔEX eq 2, indicating that the greatest reactivity is achieved when X-binding is weakened relative to A-binding. In Figure 3c, filled contour plots of ΔEFS for CH4 activation as a function of ΔEC and ΔEX are shown. The fact that ΔEC and ΔEX are correlated in most cases limits how ΔEFS can be varied using each promoter. To verify the generality of our model eq 2, we go beyond the first bond activation step for CH4 and predict ΔEFS for C−H, N−H, and O−H activation in all dehydrogenation steps of CH4, NH3, and CH3OH, respectively (reference data using an O* promoter taken from ref 12, while the rest was calculated in this study). Since the data from ref 12 was calculated using the RPBE functional,29 the corresponding ΔEX used in eq 2 were also calculated using RPBE. Predicted ΔEFS are compared with direct calculations (Figure 4a), where the MAE for all reactions is 0.18 eV, well within the estimated DFT error using BEEFvdW and the general accuracy of DFT-GGA energetics. The maximum deviation of the predictions from the calculated DFT values is 0.81 eV. A closer indicator of activity is the transition state energy ΔETS, which can be determined from ΔEFS using a universal linear transition state scaling (TSS) relationship.18

Figure 3. Parity plots for the relevant energetics of promoter-induced CH4 dehydrogenation on all promoters. The black line indicates x = y. (a) ΔEH−X and (b) ΔEFS predicted by the model plotted against the directly calculated values from DFT. (c) Colored contour plots for the final state energy ΔEFS of promoter-induced CH4 activation, as a function of the adsorption energy of the central atoms of the promoter (ΔEX) and CH4 (ΔEC).

Equation 1 formalizes why some nonmetals promote bond activation while others are poisons: if the nonmetal is highly stable on the surface (ΔEX is very negative) and βH is highly positive, then ΔEH−X will also be highly positive, and hydrogen will not be stable on the nonmetals. S, Se, and P will thus tend to be poisons, where direct dehydrogenation will be favored. Rearranging eq 1 yields ΔEH−X + ηΔEX = βH, which indicates that the sum of ΔEH−X and ηΔEX must equal the surface-

ΔE TS = αTSSΔE FS + βTSS = αTSS(γ ΔEA − ηΔE X + βFS) + βTSS

(3)

where αTSS and βTSS are the universal TSS slope and intercept from ref 18 and ΔEFS from eq 2 has been used. With a single TSS, eq 3 accurately predicts ΔETS for all reactions (Figure 4b) 3672


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Figure 5. Parity plots for predicted and directly calculated DFT results for the (a) final state energies and (b) transition state energies for CH4 activation on TM sulfides and oxides.

In summary, we have rationalized the trends in nonmetal promoted bond activation in terms of variations in surface reactivity. We developed a general model based on simple bond order formalism and verified it for a variety of bond activation processes in several dehydrogenation reactions. The associated compensation effects serve as powerful concepts for understanding the behavior of promoted catalytic systems. As with other linear scaling relations, these can be easily included in studies of full reaction pathways.31 In principle, any reaction of the form R1−A*Hx + R2−B*Hy → R1−A*Hx−1 + R2−B*Hy+1 can be described by eqs 1 through 3, where R1 and R2 are residues and A and B are surface-bound atoms connected to the residues. Since our model is based on bond orders, it has the potential to describe other complex bond activation processes, such as C−O activation in Fischer−Tropsch. By revealing the central role of bond orders in determining reactivity, our results allow for the rapid prediction of the trends and energetics of promoter-induced bond activation processes.

Figure 4. Parity plots for model predictions and directly calculated DFT results, showing the x = y line. (a) ΔEFS for C−H, N−H, and O− H bond activation (O* promoted results from ref 12). (b) Transition state energies using the parameters αTSS = 0.86 and βTSS = 1.14 from the universal TSS for dehydrogenation reactions (ref 18); (c) transition state energies determined using the individual TSS for each reaction.

except for NH3 → NH2 + H, leading to a larger MAE. This results from the inherent inaccuracies in the universal TSS model for dehydrogenation,18,30 where the TSS for NH3 has a much smaller intercept and slope due to differences in the gas phase references for the initial state.18 By adjusting αTSS and βTSS for each reaction using individual TSS relations (Figure 4c), predictions for ΔEindividual have a MAE of 0.22 eV, around TS the expected accuracy of DFT-GGA energetics. The maximum deviation of the predictions from the calculated values is 0.54 eV. Equations 2 and 3 indicate that the energetics of nonmetal promoter-induced bond activation on metallic systems are determined by the reactivity of the surface and the bond orders of the nonmetal. This suggests a more general principle that could be useful for understanding bond activation processes on transition metal compounds, where the active site could be a nonmetal, e.g., in transition metal chalcogenides. These systems are usually comprised of a mixed metal and nonmetalterminated surface. We therefore expect that the same conceptual framework can be used to describe bond activation on these materials. As an example, we use C−H bond activation in CH4 activation on layered transition metal sulfides and rutile oxides (see Supporting Information for more details on the computational setup), with ΔE FS = 2( −ηΔE X ) + βFS = −ΔE X + βFS

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COMPUTATIONAL METHODS All DFT calculations were performed using Quantum ESPRESSO,32 employing ultra soft pseudopotentials. The Bayesian error-estimation functional with van der Waals interactions (BEEF-vdW) was used for the exchangecorrelation functional and for estimating the error. All surfaces were (111)-terminated and Pd(111) was used to determine βH and βFS because it has an intermediate binding strength (Figure 1). Further details are in the Supporting Information.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpclett.5b01792. Computational details and a summary of the calculated results (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

(4)

Notes

where X = {S,O}, and CH3 and H adsorb onto two sulfur or oxygen “promoters” of η = 1/2, and γΔEA = 0 since there are no species directly adsorbing to the “surface”. ΔEFS for CH4 activation on MoS2(101̅0), WS2(101̅0), Ni-doped MoS2(1̅010), Co-doped MoS 2 (1̅ 0 10), IrO 2 (110), and RuO 2 (110) is determined with a MAE of only 0.13 eV (Figure 5a), well within the expected DFT error. Using a single TSS for the sulfides and oxides, ΔETS can be predicted using eq 3 with a MAE of only 0.11 eV (Figure 5b), again within the expected accuracy of DFT-GGA.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge financial support from the U.S. Department of Energy, Office of Basic Energy Sciences to the SUNCAT Center for Interface Science and Catalysis (F.S. and F.A.-P.), the National Science Foundation GRFP Grant DGE114747 (C.T.), the U.S. Department of Defense NDSEG Program (A.A.L.), and the U.S. Department of State Interna3673


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(21) Hughes, M. D.; Xu, Y.-J.; Jenkins, P.; McMorn, P.; Landon, P.; Enache, D. I.; Carley, A. F.; Attard, G. A.; Hutchings, G. J.; King, F.; Stitt, E. H.; Johnston, P.; Griffin, K.; Kiely, C. J. Tunable Gold Catalysts for Selective Hydrocarbon Oxidation Under Mild Conditions. Nature 2005, 437 (7062), 1132−1135. (22) Hammer, B.; Nørskov, J. K. Electronic Factors Determining the Reactivity of Metal Surfaces. Surf. Sci. 1995, 343 (3), 211−220. (23) Hammer, B.; Norskov, J. K. Why Gold Is the Noblest of All the Metals. Nature 1995, 376 (6537), 238−240. (24) Nørskov, J. K.; Abild-Pedersen, F.; Studt, F.; Bligaard, T. Density Functional Theory in Surface Chemistry and Catalysis. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (3), 937−943. (25) Abild-Pedersen, F.; Greeley, J.; Studt, F.; Rossmeisl, J.; Munter, T. R.; Moses, P. G.; Skúlason, E.; Bligaard, T.; Nørskov, J. K. Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces. Phys. Rev. Lett. 2007, 99 (1), 016105. (26) Wellendorff, J.; Lundgaard, K. T.; Møgelhøj, A.; Petzold, V.; Landis, D. D.; Nørskov, J. K.; Bligaard, T.; Jacobsen, K. W. Density Functionals for Surface Science: Exchange-Correlation Model Development with Bayesian Error Estimation. Phys. Rev. B: Condens. Matter Mater. Phys. 2012, 85 (23), 235149. (27) Medford, A. J.; Wellendorff, J.; Vojvodic, A.; Studt, F.; AbildPedersen, F.; Jacobsen, K. W.; Bligaard, T.; Norskov, J. K. Assessing the Reliability of Calculated Catalytic Ammonia Synthesis Rates. Science 2014, 345 (6193), 197−200. (28) Jones, G.; Studt, F.; Abild-Pedersen, F.; Nørskov, J. K.; Bligaard, T. Scaling Relationships for Adsorption Energies of C2 Hydrocarbons on Transition Metal Surfaces. Chem. Eng. Sci. 2011, 66 (24), 6318− 6323. (29) Hammer, B.; Hansen, L. B.; Nørskov, J. K. Improved Adsorption Energetics Within Density-Functional Theory Using Revised Perdew-Burke-Ernzerhof Functionals. Phys. Rev. B: Condens. Matter Mater. Phys. 1999, 59, 7413. (30) Wang, S.; Temel, B.; Shen, J.; Jones, G.; Grabow, L. C.; Studt, F.; Bligaard, T.; Abild-Pedersen, F.; Christensen, C. H.; Nørskov, J. K. Universal Brønsted-Evans-Polanyi Relations for C−C, C−O, C−N, N−O, N−N, and O−O Dissociation Reactions. Catal. Lett. 2011, 141 (3), 370−373. (31) Jones, G.; Bligaard, T.; Abild-Pedersen, F.; Nørskov, J. K. Using Scaling Relations to Understand Trends in the Catalytic Activity of Transition Metals. J. Phys.: Condens. Matter 2008, 20 (6), 064239. (32) Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G. L.; Cococcioni, M.; Dabo, I.; Dal Corso, A.; de Gironcoli, S.; Fabris, S.; Fratesi, G.; Gebauer, R.; Gerstmann, U.; Gougoussis, C.; Kokalj, A.; Lazzeri, M.; Martin-Samos, L.; Marzari, N.; Mauri, F.; Mazzarello, R.; Paolini, S.; Pasquarello, A.; Paulatto, L.; Sbraccia, C.; Scandolo, S.; Sclauzero, G.; Seitsonen, A. P.; Smogunov, A.; Umari, P.; Wentzcovitch, R. M. QUANTUM ESPRESSO: a Modular and Open-Source Software Project for Quantum Simulations of Materials. J. Phys.: Condens. Matter 2009, 21 (39), 395502.

tional Fulbright Science and Technology Award program (J.S.Y.).


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Predicting Promoter-Induced Bond Activation on Solid Catalysts Using

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