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Adsorption Energy Correlations at the Metal-Support Boundary Prateek Mehta, Jeffrey Greeley, W. Nicholas Delgass, and William F. Schneider ACS Catal., Just Accepted Manuscript • Publication Date (Web): 09 Jun 2017 Downloaded from http://pubs.acs.org on June 9, 2017

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Adsorption Energy Correlations at the Metal-Support Boundary Prateek Mehta,† Jeffrey Greeley,‡ W. Nicholas Delgass,‡ and William F. Schneider∗,† †Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States ‡School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, United States E-mail: [email protected] Abstract The emergence of supercell density functional theory (DFT) over the past few decades has enabled great progress in rational catalyst design for extended surfaces of transition metals. However, insights from such metal-only models may not translate directly to metal nanoparticles dispersed on high surface area supporting phases, where metal and support may both participate in catalysis. To quantify these differences, we investigate the adsorption behavior of common catalytic intermediates at the boundaries of late transition metals supported on MgO(100). We show that the oxide can either strengthen or weaken adsorption at the metal-oxide boundary, depending on the metal-adsorbate combination. Using a thermodynamic cycle, we trace the origins of these stabilization/destabilization effects to a combination of multiple structural and electronic perturbations, including strain and ligand effects, geometric reorientation and charging of the adsorbate. These perturbations in some cases result in departures from the linear scaling relations developed on metal-only models. Computational

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screening studies based on typical scaling relations may thus miss potential catalyst materials where bifunctional gains are possible.

Keywords: bifunctional catalysis, support effects, metal-oxide interface, density functional theory, scaling relations, strain and ligand effects, charge transfer

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Introduction

Ever since the pioneering works of Irving Langmuir, 1 plane surfaces have been used as models to simplify the description of complex catalytic materials. Such a surface-science-based approach has been central to the development of modern theories of catalysis. 2,3 However, it is often the complexity of the catalyst that holds the key to catalytic performance. For instance, in the case of metal nanoparticles on oxide supports, it is now widely accepted that the support is more than just an inert base on which the nanoparticles are dispersed— interactions between the metal and the oxide can dramatically influence catalytic activity. 4–15 The enhancement of activity is often attributed to special sites along the metal-oxide boundary, where the adsorbed species interact with both the metal and the support. 16 Indeed, such ‘dual’ reaction sites have been implicated as the active sites for many chemical reactions, notably CO oxidation 8,13 and water-gas shift. 6,10 Because the traditional single-crystal approach generally ignores the chemistry of the support, the underlying interactions that contribute to the unique catalytic behavior of the metal-oxide boundary are poorly understood at an atomic level. Thus, while it has been recognized that the strategic engineering of bifunctional metal-oxide reaction environments can potentially circumvent the limitations of metal-only active sites, 17,18 opportunities for methodically doing so have remained elusive. The basic question that needs to be answered is then, how does the inclusion of the support lead to deviations from the insights obtained using a metal-only model? In this work, we address this question using density functional theory (DFT) calculations to investigate the reactivity of simple adsorbates at the boundary of several late transition metals (M=Rh, Ir, 2


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Pd, Pt, Ag, Au) supported on MgO(100). Specifically, we consider how the oxide impacts the adsorption properties of atomic species (C, N, O, F) and their hydrogenated forms (CHx , NHx , and OH) as could be relevant in common chemical transformations. Our approach focuses on metal particles in the nanometer size range, which we have modeled as quasione-dimensional nanowires, similar to previous approaches in the literature. 8,19–24 We note that previous studies on such nanowire models have predominantly focused on supported Au catalysts. Here we report the first comparison of the role of the support as a function of metal. While the nanowire is not an exact representation of a metal nanoparticle (no corner sites), the approximation allows us to incorporate the metal within the periodic boundaries employed within supercell DFT, thus maintaining manageable computational expense while being free from the finite-size effects seen in small metal clusters. 25,26 MgO is an ideal model support. It has relatively low reactivity on its own, 27 is structurally simple, and has a reasonably good lattice match with the modeled metals. It is non-reducible, and its electronic structure is much less complex than modestly or strongly reducible oxides like TiO2 or CeO2 . Moreover, it has been found to have some promotional effects on the activity of the supported metal. 5,28 We show in the following paragraphs how the role of the oxide changes depending on the metal that it supports and the properties of the reactant adsorbed at the metal-oxide interface.

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Computational Details

Supercell DFT calculations were performed using the Vienna ab-initio Simulation Package (VASP). 29,30 Exchange and correlation were treated within the PBE generalized gradient approximation, 31,32 atomic core regions described within the projector augmented wave (PAW) framework, 33 and all calculations were spin-polarized. The planewave cutoff used for adsorption calculations was 400 eV, while the lattice parameters were computed at a higher cutoff of 520 eV. The Brilloiun zone was sampled using Monkhorst-Pack k-point grids. The sizes of the

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k-point grids were chosen according to calculation type. For the supported metal-on-oxide model (shown in Fig. 1), a 1×3×1 k-point mesh was used to maintain reasonable computational cost. Calculations for bulk metal slabs and metal-only nanowires were performed with finer k-point meshes of 5×5×1 and 1×9×1 respectively. Geometries were relaxed using the conjugate gradient algorithm implemented in VASP until the forces on the atoms were less than 0.03 eV/˚ A. Charges on the adsorbates were computed using Bader analysis 34–36 and are reported as the number of electrons gained or lost compared to the neutral atom. Extended details of the calculations, including atomic positions, all input parameters, and key output data, are provided as an Atomic Simulation Environment 37 (ASE) database in the supporting information (SI), following the reproducibility guidelines for computational catalysis provided by Bligaard et al. 38 We used a tetragonal supercell that included three layers of rocksalt MgO and three layers of face-centered cubic metal stacked in the (100) direction and separated from periodic images √ √ by at least 15 ˚ A of vacuum. The metal and oxide were repeated 6×2 in the 2× 2 direction, and the metal portion pruned in the long direction to expose a (111) facet, as illustrated in Fig. 1 for Pd/MgO. The PBE metal and MgO lattice constants are summarized in the Table S1. Most calculations were performed at the MgO lattice constants with metal atoms initially placed in epitaxy above oxygen ions. During relaxations, the nanowires contract in the nonperiodic direction to bond distances closer to their bulk values (Fig. 1(a)). Similar relaxation is not possible in the short direction because of the imposed periodicity. Test calculations show that our general conclusions are largely insensitive to the choice of the lattice parameter. Fig. S2 shows a direct comparison with models in which in-plane dimensions are set by the metal lattice constant. We focus on the boundary region, which presents an atop-Mg site near two metal atoms and a hollow site near one metal atom, indicated as sites B1 and B2 (Fig. 1(b)) respectively. Test relaxations show that adsorbates generally prefer site B1, and all adsorption energies at the boundary refer to this site. The nanowire model is an idealization meant to represent

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the boundary of a large particle. Due to the finite size of the nanowire, in some cases the nanowire can restructure in ways that obscure the comparisons we wish to draw and that may not be representative of real particles. To emphasize interfacial effects on adsorption, in the reported calculations metal atoms were constrained in the lateral directions and relaxed in the normal direction (see Sec. S2.2 for comparison and justification of the selected constraints). The MgO atoms were fully relaxed in all calculations. Results from the metal-on-oxide models were compared with adsorption energies calculated on three metal-only models, including conventional (111) and (211) slabs and the perimeter sites on the fully relaxed nanowire in the absence of the MgO. We calculated the adsorption energy as, ∆Eads = Esurf + A − Esurf − EA

(1)

Here EA is the gas-phase energy of the adsorbate, Esurf is the energy of the surface (nanowire or slab), and Esurf + A is the energy of the system with the adsorbate bound on the surface. To isolate interfacial effects from adsorbate-induced restructuring of the metastable, free -standing nanowires, adsorption energies on the free nanowire were computed with all metal atoms fixed and adsorbates fully relaxed. Adsorption energies on (111) and (211) surfaces were computed using four layer slabs, with the bottom two metal layers held fixed in their bulk positions. No constraints were applied on the top two metal layers.

3 3.1

Results and Discussion Comparison of adsorption energies at the metal-oxide boundary to metal-only models

We initialized adsorbates at site B1 and relaxed the geometries. Most of the relaxations converged to an energy minimum with the adsorbate still at this site. Polyatomic adsorbates like CH2 and NH2 showed multiple energy minima at B1 depending on their orientation

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Figure 1: (a) Side and (b) top views of the Pd nanowire supported on MgO(100). The unit cell is indicated in the top view. The gray shading highlights the metal-oxide boundary region. Candidate boundary adsorption sites are marked B1 and B2, and R indicates an atop-Mg site remote from the metal.

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with respect to the adsorption site. The lowest energy configuration was considered in our analysis. In a few instances, e.g. C and CH on some metal-oxide combinations, the adsorbate spontaneously migrated away from the boundary to a threefold site on the (111) metal facet. The energetics of these combinations were excluded from our analysis to ensure consistent comparison. Similarly, CH3 was excluded because it tends to prefer one-fold adsorption. A small elongation of the interfacial bonds between the surface oxygens and the metal edge atoms was observed when the adsorbate was added, ranging between 0-0.3 ˚ A depending on the adsorbate. Compared to the other metals, this elongation was consistently higher for Au/MgO by about 0.1 ˚ A. We first compare the calculated adsorption energetics at the metal-oxide boundary to slab models commonly used in the computational literature. The fcc(111) surface is the most common, and a comparison of the adsorption energies on the fcc(111) threefold site and site B1 on the nanowire-on-oxide boundary is presented as a parity plot in Fig. 2(a). Differences between the two models can be greater than 1 eV, and in a majority of cases adsorbates are found to be more stable at the boundary site than on the (111) terrace. Test calculations show that adsorption energies on the (111) face of the supported nanowire are nearly identical to those on the (111) slab. From Fig. 2(a), then, adsorbates below the parity line prefer adsorption at the boundary to adsorption further up the (111) face of the nanowire. For many metals, carbon- and nitrogen-containing adsorbates are exceptions to this trend. These adsorbates, if generated at the boundary, will thus experience a driving force away from the boundary region onto the metal nanowire itself. The differences in adsorption energies between boundary and terrace could arise from the structural dissimilarity between the fcc terrace site and the edge site on the nanowire. (211) steps are commonly used to model edge atoms. 25,26 Accordingly, we compare the adsorption energies at the metal-oxide boundary to the (211) stepped surface in Fig. 2(b). Adsorption energies at the (211) step edge show somewhat better agreement to those at the metal-oxide boundary, but large disparities are still present. Adsorption energies on a

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fourfold site inside the (211) step also failed to recover those at the M/MgO boundary (see Sec S2.4). However, the adsorption energies on the (211) edge show remarkable agreement with those computed by relaxing the adsorbates at the perimeter of unsupported nanowires (see Fig. 2(c)), confirming that the stepped slab is indeed a good model for metal-only edge sites. Finally, in Fig. 2(d), we compare the adsorption energies at the supported nanowire boundary to the unsupported nanowire edge. This comparison confirms that the oxide can play a significant role in modifying the adsorption energetics, and depending on the metal and the adsorbate, the support can either enhance or reduce the adsorption energy. For instance, the adsorption of almost all species is strengthened at the Ag/MgO boundary, sometimes by around 1 eV. On the other hand, binding of many intermediates is diminished at the boundary of Rh, Ir, Pd, and Pt when the oxide is included, most prominently NH2 .

Figure 2: Parity plot comparisons of adsorption energies on (a) fcc(111) surface and nanowire-on-oxide boundary, (b) fcc(211) steps and nanowire-on-oxide boundary, (c) fcc(211) steps and unsupported nanowire perimeter sites, and (d) unsupported nanowire perimeter sites and nanowire-on-oxide boundary. The insets in each plot show representative atomic configurations of the two compared models.

3.2

Impact of oxide on metal reactivity

We now investigate the origins of the deviations from the metal-only models, starting with how the oxide influences the properties of the metal atoms along the boundary. The creation 8


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of the metal-oxide interface can be thought of as a two stage process involving ‘strain’ and ‘ligand’ effects (see Fig. S4), similar to the description by Kitchin et al. for bimetallic alloys. 39 The strain effect arises from a change in the interatomic distances of the metal atoms due to epitaxy with the support, while the ligand effect is a consequence of bonding interactions between the metal and the support. Different metals are strained differently by the oxide— while Au and Ag have practically no lattice mismatch with MgO (∼ 2.5 percent), the lattice constants of the other metals are considerably smaller than that of the oxide, by 7, 7, 9 and 10 percent for Pt, Pd, Ir, and Rh respectively. We measured the strain effect as the shift in d -band center of the perimeter metal atoms in going from an unsupported nanowire at the natural metal lattice constant to the strained configuration it attains when placed in epitaxy on the oxide, ∆ǫstrain = ǫstrained . To obtain the strained configuration, we first −ǫunsupp d d d relaxed the system with the metal nanowire on the oxide and then deleted the support atoms. − ǫstrained = ǫsupp Similarly, we quantified the ligand effect as ∆ǫligand , ǫstrained . Here ǫunsupp , d d d d d refer to the d -band centers of the perimeter metal atoms in the unsupported, and ǫsupp d strained, and MgO supported configurations, respectively. The calculated values of ∆ǫstrain , d , and the net shift in the d -band center, ∆ǫd , are reported in Table 1. Positive values ∆ǫligand d of ∆ǫstrain for all metals show that the tensile strain shifts the d -band centers of the metal d atoms along the boundary to higher energies, consistent with prior works on strained metal surfaces. 39,40 However, bonding interactions between the metal and the support result in a ). Thus shift of the d -band center to lower energies (reflected in the negative values of ∆ǫligand d for the M/MgO systems, the strain and ligand effect counter each other. The net result is a small shift of the d -band center to lower energies, see Table 1. According to the d -band model, 41 these findings suggest that the metal-adsorbate bond should be modestly weakened in the presence of the support. However, Fig. 2(d) shows that adsorbate binding is often strengthened at the boundary. This observation suggests that factors in addition to changes in the metal d -band properties modify the adsorption energy at the metal-oxide boundary.

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Table 1: Lattice mismatch of the metals with MgO and shifts in perimeter metal atom d -band centers due to strain, interaction with MgO support, and the sum of the two contributions Metal Rh Ir Pd Pt Au Ag

3.3

lattice mismatch (%) 10.0 8.8 7.2 6.6 2.4 2.2

∆ǫstrain (eV) d 0.22 0.28 0.22 0.31 0.16 0.07

∆ǫligand (eV) d −0.29 −0.34 −0.36 −0.51 −0.33 −0.14

∆ǫd (eV) −0.07 −0.06 −0.14 −0.20 −0.17 −0.07

Origins of adsorption energy perturbations

To probe the adsorption energy perturbations in more detail, we constructed the cycle shown in Fig. 3, illustrated for an oxygen atom adsorbed at the boundary of a Pd nanowire. The overall cycle measures the difference between the binding energy of an adsorbate fully relaxed at the edge of an unstrained and unsupported nanowire (configuration 1 ) and at the boundary of a supported nanowire (configuration 4 ). That is, ∆Eads = Eads4 − Eads1 , where Eads4 and Eads1 are obtained using equation (1). ∆Eads is thus the departure from parity observed in Fig. 2(d). We re-plot ∆Eads as squares in Fig. 4(d). As noted in Sec. 3.1, ∆Eads follows no obvious trend. At the Ag/MgO boundary, an adsorption energy enhancement of 0.4 eV or more is observed for all adsorbates other than CH2 and NH2 . The stabilization is less pronounced on Au/MgO—only O, OH, and F show significant binding energy enhancement. For other metals, adsorption is generally weaker in configuration 4 relative to configuration 1. NH2 on Pt/MgO has the largest destabilization, of about 1.3 eV. As noted above, the 4-valent C and 3-valent CH were unstable in configuration 4 for most group 9 and 10 metals. strain The first step of the cycle measures the adsorption energy change due to strain, ∆Eads =

Eads2 − Eads1 , plotted in Fig. 4(a). Here Eads2 , the adsorption energy in configuration 2, is obtained by removing the oxide atoms from configuration 4 and re-relaxing the adsorbate while keeping the metal atoms fixed. Relaxed adsorbate geometries in configurations 1 and 2 are similar in most cases. Strain generally enhances the metal-adsorbate binding, consistent 10


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1

3 4 G0 G0J

Figure 3: Thermodynamic cycle decomposition of the adsorption energy perturbations at the metal/oxide boundary into strain, reorientation, and bonding effects.

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strain reorient bond Figure 4: Comparison of (a) ∆Eads , (b) ∆Eads , (c) ∆Eads , and (d) ∆Eads for different adsorbates at the boundary of the M/MgO systems.

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with the d -band center shifts discussed above (see Table 1). The binding energy enhancement can be as great as 0.8 eV for some systems (C on Ir) and is generally greatest for the atomic strain adsorbates. ∆Eads decreases with increasing hydrogenation of the parent atom, consistent

with a decrease in the number of bonds between adsorbate and metal d states. 42 OH on Rh experiences a positive strain effect associated with a change in adsorption geometry from the unstrained to the strained wire. To capture the steric influence of the support, we compare configuration 2 with 3, the latter obtained by removing the support atoms from configuration 4 while fixing both the metal and adsorbate atoms. The presence of the oxide support causes adsorbates to rotate away from the preferred ‘equatorial’ plane of the nanowire and to move further from the reorient metal, as illustrated in Fig. 3. The reorientation energy , ∆Eads = Eads3 − Eads2 , is neces-

sarily positive and is reported in Fig. 4(b). The reorientation energies for F are notably large. They tend to be smaller for most atomic adsorbates and to increase with hydrogenation, to greater than 0.5 eV for NH2 and OH. Polyatomic adsorbates thus appear to be the most sensitive to steric influences of the support. The reorientation energies also show some metal dependence—they are generally smallest on Ag, and largest on Pt. The last step in the cycle, from configuration 3 to 4, captures the explicit support bond influence on adsorbate binding. ∆Eads , the associated change in binding energy, is plotted

as diamonds in Fig. 4(c). This term takes both positive and negative values ranging between −1.3 and +1.0 eV depending on the metal and adsorbate combination. The support stabilizes F, O, OH, and NH in most M/MgO combinations. On the other hand, C, CH, CH2 , N, and NH2 are destabilized on all metals other than Ag. We postulate that this dichotomy reflects the competing contributions of the destabilizing ligand effect highlighted above and a stabilizing binding of the adsorbate to the support itself. Based on the similarity in magnitude and difference in sign of the ligand- and strain-induced d -band shifts reported in Table 1, we can expect the ligand effect to destabilize binding to an extent comparable to the stabilizing influence of strain. Fig. 4(c) then suggests that the magnitude of the stabilizing

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contribution is greater than 1 eV for certain metal-adsorbate combinations. A simple rationalization of the support-induced stabilization is that the adsorbate-support bond supplements or even replaces the adsorbate-metal bond. In none of the cases here do we find the adsorbate to relax away from the metal onto the support alone. Nonetheless, the support may provide some supplementary bonding to the adsorbate. To test this idea, we computed adsorption energies of all adsorbates atop Mg sites on the bare MgO(100) surface (consistent with the selection of site B1, results reported in Table S2). We find that binding to MgO(100) is uniformly several eV weaker than binding to metal-only perimeter sites. In many cases, the stabilization at the metal-oxide boundary is greater than binding to MgO bond alone. For instance, ∆Eads for O on Ag/MgO is around −1.3 eV, even though its calcu-

lated binding energy on MgO(100) is only −0.9 eV. Thus, factors beyond the MgO-adsorbate bonding likely contribute to the binding energy enhancement. Interfacial charge transfer has been implicated in enhanced binding at the Au-metal oxide boundary. For example, charged peroxo-type species have been suggested to be intermediates in CO oxidation on MgO- and TiO2 -supported Au catalysts. 8,19,20,24,43,44 To probe for charging effects, we compared adsorbate Bader charges in configurations 3 and 4. In both these configurations, the direction of charge transfer is to the adsorbate, and can be greater than 1 e (see Table S4). Representative results for the charge difference (∆qB1 = q4 − q3 ) are shown in Table 2 (all metals are reported in Table S4). Charge transfer is enhanced in the presence of the support in all cases other than CH2 on Ir and Pt, and enhancements range from −0.01 up to −0.38e. The charge enrichment is generally insensitive to the metal. We also find that the adsorbate charges on M/MgO are uniformly greater than the MgO alone (Table S3). It is difficult to straightforwardly isolate the energetic contribution of the adsorbate chargbond ing to ∆Eads at site B1 since the adsorbate simultaneously coordinates with both metal and

oxide atoms. We describe here an indirect approach to understand qualitative trends in these redox energy contributions. The results above suggest that the metal-oxide combination is a

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more effective charge donor than the metal (or oxide) itself. Such a charging process closely resembles ‘cooperative’ redox pairing of co-adsorbates on oxide surfaces, 45–47 adsorption on doped metal oxides, 47–49 and on ultrathin oxide films supported on metal substrates. 27,50–55 In these systems, polarization of the oxide mediates charge transfer between an acceptor-donor pair, which decays slowly with distance between the pair. 54 We expect a similar effect here when the LUMO level of the adsorbate is below the Fermi-level of the metal oxide interface (shown schematically in Fig. S5). Indeed, the charge transfer is present even when the adsorbate is located remote from the metal-oxide boundary (site R in Fig. 1(b)). We computed the charge enrichment at this site, ∆qR , as the charge difference between an adsorbate at site R and on metal-free MgO (results for all metals in Table S5). Similarly to ∆qB1 , the charge enrichment at site R is generally insensitive to the metal. In Table 2, we compare ∆qR with ∆qB1 for an abbreviated set of metals. While these two charge enrichment terms are not completely analogous, our results suggest that they are comparable in magnitude when the adsorbate geometry is similar at sites B1 and R. For the atomic adsorbates, the charge enrichment at both sites is nearly identical. Polyatomic adsorbates are more configurationally constrained at site B1, and consequently ∆qB1 tends to be lower in magnitude compared to ∆qR . CH2 and NH2 represent the extreme of negligible charge transfer at B1. These planar, sp2 adsorbates bind anisotropically to the interface. Rotation of NH2 about the M−N axis at site B1 (such that the adsorption geometry resembles the one at site R) increases charge transfer substantially (see Fig. S6 for a structural comparison and NH∗2 entry in Table 2). The difference in the overall adsorption energy for the two rotational conformers is small (between −0.1 to +0.3 eV depending on the M/MgO combination), indicating that they can readily reorient themselves during reactions. We can now calculate the redox energy at site R (Eredox = EadsR − EadsMgO ) as the difference in binding energy at a remote Mg site in the presence and absence of the nanowire (plotted in Fig. 5). Eredox < 0 for all adsorbates, reflecting a charge transfer enhancement of binding. The magnitude of Eredox is sensitive to adsorbate and metal. The adsorbate

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Table 2: Bader charge enrichment on the adsorbing atom at sites B1 and R for representative M/MgO systems. Note: ∆qR is not reported for CH on Ag because we were unable to converge the electronic ground state. Adsorbate C CH CH2 CH2 N N NH NH2 NH*2 O OH F F

Metal Au Ag Pt Ag Pt Ag Ir Pt Pt Pt Pt Pt Ag

∆qB1 −0.25 −0.25 0.01 −0.09 −0.24 −0.38 −0.26 −0.06 −0.21 −0.23 −0.19 −0.16 −0.08

∆qR −0.23 – −0.12 −0.20 −0.25 −0.42 −0.24 −0.32 −0.32 −0.29 −0.31 −0.17 −0.17

dependence is linked to the position of their LUMO levels, or roughly their electron affinities 27,45,54,56 (see Fig. S5). Adsorbates with a high electron affinity 56 are substantially stabilized by the redox process—around 2 eV for F, 1.5 eV for OH, and 1.1 eV for O on Ag/MgO. C- and N-containing species have lower electron affinities and corresponding lower Eredox . Fig. 5 indicates that redox stabilization generally increases with hydrogenation. The metal effect is of order 0.5 eV. Studies of adsorbate charging on metal supported MgO thin films have found that the redox energy tends to be higher for metals with lower work functions, though this correlation is very approximate. 54 Here we find that the redox energy is highest at the Ag/MgO interface, consistent with its lower work function (4.3 eV) compared to the other metals (> 5 eV). 57 However, Eredox is lowest in magnitude for adsorbates on Au/MgO, even though the experimental work function of Au is lower than those of Pt, Pd, and Ir. The trends in Eredox at site R are generally consistent with the support binding enhancebond ment at site B1, ∆Eads , shown in Fig. 4(c). Enhancements are greatest for F, O, and

OH, the most redox-active adsorbates, while for other adsorbates the redox effect is counterbalanced by the destabilizing ligand effects. The absence of charge transfer to CH2 and NH2 16


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Figure 5: Redox stabilization energy for different adsorbates at site R.

bond bond at site B1 results in a destabilizing ∆Eads . Variations in ∆Eads with metal are dictated by bond the balance of the ligand and redox effects. ∆Eads is most negative for Ag/MgO, consistent bond with its high redox stabilization, while Pt has the highest ∆Eads , due to its large ligand

effect (Table 1). In all cases the adsorption at site B1 is always favored over site R; none of these adsorbates experience a driving force away from the interface to the support, as shown in Fig. S7. We do not expect this to be a rule—for other species, adsorption might become competitive between these two sites, especially at interfaces of relatively inert metals like Au and Ag. For example, Zhao et al. reported that H2 O prefers to bind on oxide sites at the Au/MgO interface, while H only has a small energy penalty to migrate between dual and remote oxide sites. Additional evidence for the competing influence of ligand and charge transfer effects bond on ∆Eads is provided by computing bond lengths. Fig. 6(a) shows that the distance of

the adsorbate from the nanowire, dM , is inversely correlated with its distance from the support, dM g (See configuration 4 in Fig. 3). In general, enhancement of binding is correlated with closer approach to the support (Fig. 6(b)) and anticorrelated with distance from the nanowire (Fig. 6(c)). Atomic adsorbates, which approach the metal more closely than their hydrogenated forms, are more sensitive to the ligand effect and have generally more positive bond ∆Eads values.

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bond reorient ∆Eads and ∆Eads are not independent of each other. Attractive adsorbate-oxide

interaction and weakened metal-adsorbate binding results in an elongation of the metaladsorbate bond, which manifests itself as a contribution to the reorientation energy. In Fig. 6(d), we plot the difference in metal-adsorbate bond length between configurations reorient 2 and 3 (∆dM = dM3 − dM2 ) vs ∆Eads . The figure shows that a larger increase in

∆dM corresponds to a greater reorientation energy penalty. This bond elongation is most pronounced for the strongly redox-active adsorbates, F, OH, and O.

Figure 6: Correlations between (a) the metal-adsorbate distance, dM , and the oxidebond bond reorient adsorbate distance, dM g (b) ∆Eads and dM g (c) ∆Eads and dM , and (d) the ∆Eads and the change in metal-adsorbate distance, ∆dM . CH2 and NH2 , which are heavily influenced by steric effects, are not included in these correlations.

Before concluding this section, we note that while the approach described here is well suited for analyzing trends across different metals, caution must be exercised in applying the results quantitatively to any particular system. The exact magnitude of the strain in a 18


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real supported particle may not correspond to that in any particular nanowire model. The work here highlights the role of strain in nanowire models and the transferability of strain concepts developed for metal-only systems to the metal-oxide interface. Because these results are transferable, corrections can readily be applied to a particular model to remove or adjust the strain contribution to the final energy, using the d-band model. 40 Further, while the GGA has proved quite robust for predicting trends in binding energies at metal surfaces, it is known to poorly describe oxide band gaps and positions of energy states within the gap. Further refinements with more expensive functionals that incorporate exact exchange may thus be necessary when charge transfer significantly influences the adsorption energetics (see Ref. 28 for an example). In related systems, GGA functionals have been found to qualitatively capture the same electronic structure phenomena as hybrid functionals, 58,59 but the redox pairing energies tend to be underpredicted. 59 To explore this functional dependence, we performed test single point calculations with the HSE06 hybrid functional 60 (see Sec. S2.8). F adsorption at site R was chosen as the test system becuase it has the highest redox stabilization and thus is likely to show the most sensitivity to the choice of functional. We find that PBE systematically overbinds the adsorbate (by about 0.3 eV), but underpredicts the redox stabilization energy (by around 1 eV). This underestimation of the redox energy by PBE is unsurprising, because it underestimates the band gap of the clean MgO surface (3.6 eV), compared to HSE06 (5.0 eV). However, the relative differences in the redox energies across metals are nearly identical with both functionals.

3.4

Impact on scaling relations

The data collected above allow the scaling relationship 42 between a parent atom A and its hydrogenated form AHx to be compared on an unsupported and supported nanowire. Fig. 7, plots in blue adsorption energies of AHx against those of A on the unsupported wire as well as the best fit line through the data. Correlations are generally linear. The best fit lines have slopes and intercepts that agree well with those reported for adsorbates at step sites, 42 19


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again highlighting the structural similarity between the nanowire boundary and the (211) steps. Plotted in red are the corresponding correlations at the interface. Equivalent metal/adsorbate points are connected by arrows. While some points move quite far, in general similar linear correlations are observed, with some greater scatter. Best fit lines to the interface adsorption have slopes quite close to the metal-only values, while intercepts tend to move further.

Figure 7: Scaling relationships between the adsorption energies of O and OH (left), N and NHx (center), and C and CHx (right) on unsupported (blue) and MgO supported (red) nanowires. Circles and triangles represent plots of A vs. AH and A vs. AH2 respectively.

For OH vs O, the interface generally causes points to move along the same scaling line. Notable exceptions are Au and Ag, where redox effects are large. In fact, Ag goes from being one of the weakest binders of OH to one of the strongest in the presence of the support. Similar behavior is observed for NH vs N, with a corresponding stabilization of NH on Au and Ag. NH2 is systematicaly destabilized by the interface due to steric effects, 20


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shifting the scaling line up nearly 1 eV. Many of the CH adsorbates move away from the interface, making it impossible to establish useful correlations. CH2 vs C follows patterns similar to NH2 vs N. Departures from metal-only scaling relations thus do occur at boundary sites even with the simple MgO support. The extent of this deviation is quite variable, ranging from virtually non-existent to quite substantial. In a few cases, e.g. Ag/MgO, the adsorption energies are perturbed by the oxide to the extent that they lie far outside the scaling relation. Metalonly scaling relations can thus potentially misidentify optimal catalysts when the support affects the adsorption energies or miss promising candidates where bifunctional gains enhance activity. For example, the oxygen adsorption energy has been used as a descriptor in computational screening of CO oxidation 61 and water gas shift 62,63 catalysts. The oxygen binding energy enhancements of 0.5-1 eV on Au/MgO and Ag/MgO found here could increase the Sabatier rates of these reactions dramatically compared to bulk Ag and Au surfaces. Indeed, oxygen/hydroxyl species activated at the metal-oxide interface are considered to play an important role in both these reactions. 8,10,13,28 On the other hand, the systematic weaker binding of adsorbates like NH2 and CH2 at the interface may become important in situations where these species otherwise poison the catalyst. The determination of the components of ∆Eads provides guidance towards identifying situations where a deviation from a metal-only scaling relation may be expected. The d -band shifts from strain and ligand effects largely cancel, and only result in small displacements of the adsorption energies along the metal-only line. Depending on the strain and the strength of the metal-oxide interaction, these displacements may be more pronounced for some metals (e.g. Pt) compared to others (e.g. Ag). Larger offsets from the metal-only scaling relation are dictated by the balance of the reorientation energy and the oxide-adsorbate interaction. The effects cancel each other when they are of similar magnitude, and a support effect will only be observed when one of the effects is dominant. Our results thus indicate that flexible adsorbates with higher electron affinities will prefer to adsorb at the metal-oxide boundary,

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especially when the work function of the interface is low. On the other hand, the adsorption of structure sensitive adsorbates, and those with low electron affinity would be weakened by the oxide compared to an unsupported edge site. We believe that it should be possible to tune both the oxide-adsorbate interaction and the reorientation energy. For instance, the redox energies could be altered by modifying the donor properties of the oxide using dopants or defects. 27,49,55 The reorientation energies are specific to the structure of the metal-oxide interface; other interfaces may impose different steric penalties.

4

Conclusions

The metal-support interface provides adsorption sites that are geometrically distinct from those on the terraces of a metal particle. In this work we have presented a systematic analysis of the factors controlling the adsorption of small molecules at these sites, using a well defined model of the metal/MgO boundary. Some these factors transfer directly from understandings of adsorption on metals and some are modified by and/or specific to the interface. The metal atoms at the interface are under-coordinated to other metal atoms and are geometrically distinct from those on metal terraces. The reactivity of these atoms is further modified by strain and ligand effects arising from the influence of the support on intermetallic distances and electronic structure. The support further impacts reactivity by limiting steric access of adsorbates to metal atoms and by creating a heterojunction that participates in adsorbate charge transfer. These individual factors vary systematically on their own, but when combined at an interface non-systematic behavior emerges. For some systems, the factors cancel each other and and the support has little to no influence on adsorption energetics compared to a metalonly edge. On the other hand, if one of the contributions is dominant, the support can have a large stabilizing or destabilizing influence on adsorption. We find that adsorbates with high electron affinity are stabilized at the interface, while structure sensitive polyatomic

22


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adsorbates prefer to bind on metal terraces or edges away from the interface. As a consequence of the variable influence of the support, the scaling relations between the binding of a parent atom and its hydrogenated forms, which are widely used for understanding reactivity trends in metal catalysts, may not necessarily hold at the metal-oxide interface. In many cases a scaling behavior is maintained, although the locations of particular metals are shifted non-systematically relative to their positions in the absence of the support, occasionally becoming outliers. The influence of the interface on transition state scaling relations (or Brønsted-Evans-Polyani relations) remains to be explored, but we anticipate that the support will similarly perturb the transition state energies. Lastly, we note that while these results were obtained for a non-reducible support (MgO), we expect the same concepts to translate to other metal/support combinations, modified by the electronic structure of the oxide and the dimension/shape of the metal. Oxides less inert than MgO may contribute more directly to adsorbate binding or even compete directly with the metal for adsorbates. For small metal clusters, electronic and geometric perturbations arising from finite-size effects may also affect reactivity, 25,26,64 especially on reducible supports. 14 Further, the small adsorbates used here do not necessarily probe the full range of phenomena that could influence interfacial binding. In particular, larger absorbates may simultaneously coordinate to more distant metal and support sites, as has been reported for COOH. 28

5

Acknowledgments

This work was partially funded by the US Department of Energy Chemical Sciences Program Grant No. DE-FG02-03ER15466. Computational resources were provided by the Notre Dame Center for Research Computing. PM acknowledges support through the Patrick and Jana Eilers Graduate Student Fellowship for Energy Related Research. We thank Paulami Majumdar and Tej Choksi for valuable discussions.

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6

Supporting Information

Lattice constants used; justification of constraints applied; model sensitivity analysis to choice of lattice constant and DFT functional; further discussion and schematic representation of interfacial charge transfer; ASE database containing full details of all DFT calculations; examples of using the database to reproduce all plots and tables in this work.

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