Stability and Effects of Subsurface Oxygen in Oxide-Derived Cu

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Article

Stability and Effects of Subsurface Oxygen in Oxide-Derived Cu Catalyst for CO Reduction 2

Chang Liu, Maicon Pierre Lourenço, Svante Hedström, Filippo Cavalca, Oscar Diaz-Morales, Hélio Anderson Duarte, Anders Nilsson, and Lars G.M. Pettersson J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b08269 • Publication Date (Web): 10 Oct 2017 Downloaded from http://pubs.acs.org on October 11, 2017

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The Journal of Physical Chemistry

Stability and Effects of Subsurface Oxygen in Oxide-Derived Cu Catalyst for CO2 Reduction Chang Liu1, Maicon P. Lourenço2, Svante Hedström1, Filippo Cavalca1,3, Oscar Diaz-Morales1, Hélio A. Duarte2, Anders Nilsson1 and Lars G. M. Pettersson1,* 1 Department

of Physics, AlbaNova University Center, Stockholm University, S-10691

Stockholm, Sweden 2

GPQIT. Departamento de Química. ICEx. Universidade Federal de Minas Gerais. Belo Horizonte. 31.270-901. MG. Brazil

3

SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA

Abstract Oxide-derived copper (OD-Cu) catalysts are promising candidates for the electrochemical CO2 reduction reaction (CO2RR) due to the enhanced selectivity towards ethylene over methane evolution, which has been linked to the presence of subsurface oxygen (Osb). In this work, Osb is investigated with theoretical methods. Although Osb is unstable in slab models, it becomes stabilized within a “manually” reduced OD-Cu nanocube model which was calculated by self-consistent charge density functional tight binding (SCC-DFTB). The results obtained with SCC-DFTB for the full nanocube were confirmed with subcluster models extracted from the nanocube, calculated with both density functional theory (DFT) and SCC-DFTB. The higher stability of Osb in the nanocube is attributed to the disordered structure and greater flexibility. The adsorption strength of CO on Cu(100) is enhanced by Osb withdrawing electron density from the Cu atom resulting in reduction of the σ-repulsion. Hence, the coverage of CO may be increased, facilitating its dimerization.

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Introduction In recent decades, the electrochemical CO2 reduction reaction (CO2RR) has gained more and more attention as a viable method for fixating the CO2 produced in the combustion of fossil fuels. 1,2 Specifically, CO2RR provides a new way to store energy from intermittent renewable sources such as wind and solar in valuable commodity chemicals such as ethylene. The development of an electrocatalyst with low overpotential, high selectivity and Faradaic efficiency towards value-added products, and good stability is of utmost importance for the advancement of CO2RR. Among all transition-metal elements, copper is the only metal that can produce hydrocarbons, such as methane and ethylene, in aqueous solutions at room temperature and ambient pressure. 3 Oxide-derived Cu (OD-Cu) nanostructures were proven to have increased selectivity towards the economically valuable product ethylene compared to the pure metallic Cu facets. 4–11 Recently, the synthesis of a novel OD-Cu nanocube (CuCube) catalyst via successive oxidation–reduction cycles on a polycrystalline copper electrode was reported, showing high selectivity and low overpotential towards the production of ethylene with respect to methane. 7,12,13 It was further demonstrated that the CuCube can originate from reduction of Cu2O and that similar selectivity towards the formation of ethylene is observed from oxide-derived Cu. 13 Various hypotheses on the origin of the improved selectivity have been proposed and tested: grain boundaries, 14,15 pH effects, 10,16–19 ensemble effect, 20 hydrolysis of hydrated cations, 21 electric field effects, 22–24 surface morphology, 25,26 etc.. Meanwhile, a reaction pathway which only exists on the Cu(100) surface and leads to the production of ethylene, but not methane, with a low onset potential has been proposed: 27–29 first CO undergoes a dimerization and then the hydrogenation proceeds. This process occurs distinctly from other similar pathways on the Cu(111) surface, where methane is also formed. It has been found that Cu(100) exhibits a lower activation barrier for the OC–CO dimerization reaction, 22,23 and an electrode potential range in which ethylene is produced at a higher rate than methane. 7 However, the characteristics of the extended Cu(100) surface alone could not explain the extraordinary performance of CuCubes, namely the dramatically enhanced selectivity and the higher CO binding energy (which is also observed in other oxide-derived Cu catalysts). 30,31 Furthermore, subsurface oxygen (Osb) atoms have been found to influence chemical reactions significantly on various metal surfaces. 32– 36 The stability of non-oxidic Osb during CO2RR has been shown by means of in situ ambient-pressure X-ray photoelectron spectroscopy (APXPS), density functional theory (DFT) calculations and electron-energy loss spectroscopy (EELS), 37–40 and it was shown that Osb could increase the binding energy of CO on the Cu(100) surface. Further experimental information on the distribution and properties of Osb in the catalytic nanoparticles is given in an accompanying paper. 41 Here, we investigate the stability of “dopant” Osb atoms in two model Cu structures: a Cu(100) slab, as it is the more active facet, and a nanoparticle. Furthermore, the activity and stability of Osb are compared among different 2


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configurations in the studied nanocube structures. Finally, the effect of Osb atom on the binding energy of CO adsorbed on Cu as a function of dopant depth from the Cu surface is investigated. We find a significantly increased CO binding energy, which is explained by Osb reducing the repulsion in the σ-system while leaving the CO-metal π-bond unaffected.

Computational Details The stability of Osb was tested by calculating the diffusion minimum energy path (MEPs) with the nudged elastic band (NEB) method, 42–44 using the grid-based projector-augmented wave (GPAW) DFT code. 45,46 The slab size in this part is 2×2×6, and Osb is placed at the octahedral site in the 2nd or 3rd layer. The MEPs are calculated: from the 3rd layer to the 2nd layer, and from the 2nd layer to the surface. The convergence threshold for the NEB is 0.1 eV/Å, the optimization algorithm is BFGS and the climbing image method is used. The plane wave model of GPAW is employed, with a kinetic energy cutoff for the wave function of 500 eV, and the exchange– correlation functional PBE. 47 The smearing is 0.1 eV with Fermi–Dirac distribution and extrapolated to zero. The k-point sampling in the 1st Brillouin zone is 4×4×1 using the Monkhorst–Pack scheme. For the initial and final states, the convergence threshold is 0.01 eV/Å. The lattice constant used for the unit cell is taken from experiment. To test whether Osb is more stable inside a Cu nanocube or on its surface, a cube model similar to the actual catalyst is investigated using the self-consistent charge density functional tight binding (SCC-DFTB) 48 approximate method, implemented in the package DFTB+. The Slater–Koster files containing the necessary parameters are developed from DFT calculations on Cu dimer, Cu and Cu2O solids, and give good agreement with DFT in terms of band structures and structural properties (more details in Supporting Information (SI)). Initially, the nanocube lateral width is approximately 1.7 nm, containing 500 Cu atoms and 250 O atoms, i.e. a 5×5×5 repetition of the Cu2O primitive unit cell. The lattice constant for Cu2O is obtained from optimization of a bulk model using SCC-DFTB. To emulate the electrochemical reduction process, the oxygen atoms on the cube surface and between the two outermost layers are removed ten at a time and a structure optimization was performed after each removal. Such oxygen atoms are chosen because they are directly exposed to the particle surroundings. The choice of oxygen atoms to remove for each batch is random, but the surface oxygen atoms are removed before the lower layers. This process is repeated until all exposed oxygen atoms are removed, after which the cube size still has a side length of around 1.7 nm. The optimization method is steepest descent and the convergence threshold was 0.05 eV/Å. At the end of this process, minima hopping is employed for global optimization on the nanocube obtained in the last step. 49 The initial NVE molecular dynamics temperature was 500 K, the initial energy acceptance threshold is 0.25 eV, the local 3



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optimizer was Quasi Newton with a convergence threshold of 0.05 eV/Å, the time step is 1 fs, and the total number of cycles is 50 (more details in SI). After the manual reduction process, the oxygen forms a distorted 3×3×3 body-centered cube amongst Cu atoms. Subsequently, the stability of subsurface O atoms with respect to adsorbate O atoms is tested. First, a subsurface O atom is taken to the closest surface adsorption site and then the structure is optimized to the nearest minimum using Quasi-Newton. Six near-facet, one near-edge and one near-corner Osb positions are tested. Additionally, to benchmark the different methods, a slab model is tested with both GPAW and SCC-DFTB. The slab is a Cu(100) 2×2×5 surface model, with the two bottom layers fixed. An oxygen atom is placed in either the octahedral interstitial site of the second layer or the hollow site of the surface, in order to compare the relative stability using auxiliary DFT 50 and SCC-DFTB, respectively. The lattice constant in each case is calculated from a bulk model resulting in 3.67 Å for GPAW and 3.76 Å for SCC-DFTB. In both cases, the oxygen is found to be more stable at the surface, with the correct trend reproduced by SCC-DFTB but the energy difference is underestimated (i.e. 1.10 versus 2.37 eV). Although significantly smaller than for full DFT, the driving force to segregate to the surface in the slab model is still large using SCC-DFTB and deemed sufficient for an evaluation of the subsurface stability of oxygen in the nanocube model. This is also supported by SCC-DFTB underestimating the subsurface stability. In the investigation of the influence of Osb on the adsorption of CO on Cu(100), slab models are employed in full DFT calculations. Six different sizes of unit cells are tested: 2×2×4, 2×2×5, 2×2×6, 2×2×7, 4×4×5, and 4×4×6. The two bottom layers of Cu atoms in the slabs are fixed and the other atoms are relaxed with either Quasi-Newton or MDMin (a modification of the usual velocity-Verlet molecular dynamics algorithm), the convergence threshold being 0.05 Å/eV. The lattice constant used is the calculated value 3.67 Å. The adsorption site of CO is atop, and the Osb is introduced as an interstitial dopant in an octahedral site (or “hollow”). The adsorption energies of CO on different sites on Cu(100) were tested and the atop site is the most favorable (see SI). The slabs with and without adsorbate are optimized separately, together with the gas phase CO molecule as a reference. One adsorbate and one dopant in each supercell are used. The calculations are performed with GPAW 45,46 using the RPBE exchange–correlation functional. 51 The finite difference mode is employed with 0.2 Å grid spacing. The smearing scheme is Fermi-Dirac with a width of 0.1 eV and extrapolated to zero. The k-point sampling in the 1st Brillouin zone is 4×4×1 (Monkhorst–Pack scheme). For all the slab models, there is a vacuum of 14 Å to avoid the spurious interactions. Two typical slabs are shown in Figure 1. Two smaller, finite, model systems were prepared as subclusters extracted from the surface of the larger DFTB-optimized nanocube: one Cu34O8 subcluster and one corresponding pure Cu42 subcluster. At the DFT level of theory with the PBE 4


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exchange–correlation functional and a TZVP basis set, a stepwise optimization procedure was carried out in order to retain a reasonably flat surface. First, the edge atoms were frozen and the rest allowed to relax, followed by full relaxation with and without attachment of one CO. Single-point calculations with the RPBE functional were then performed for more reliable adsorption energies. To investigate the origin of the enhancement of the CO binding energy, we consider a simple R–CO gas phase molecular chain model – where R is either the two-atom group OCu or a Cu atom, as shown in Figure S9 – and investigate the influence of the oxygen atom on both the π- and the σ-interactions. The chains are doublets with one unpaired electron and zero net charge. For these molecular tests, StoBe–deMon 52 was used with spin polarization taken into account. The calculations done with StoBe–deMon employ the RPBE functional, and a TZVP basis set for O, C and Cu. The polarization and bonding/donation effects of both σ- and π-symmetries are disentangled in a sequential way in a constrained space orbital variation (CSOV) 53 procedure similar to that reported by Nyberg et al.. 54 This contains seven steps: (I) R and CO are placed very far away from each other (100 Å distance) and their orbitals are fully relaxed, which allows a clear definition of orbitals as belonging either to CO or R. (II) With both occupied and unoccupied orbitals frozen separately for R and CO, R and CO are brought to the bonding distance and the energy is calculated to obtain the initial repulsion. (III) Mixing between occupied and unoccupied orbitals in the π-symmetry is allowed, but only when belonging to the same species (R or CO). This excludes charge transfer and covalency, and hence the π-polarization energy is obtained.(IV) The full remaining interaction in the π-symmetry is allowed, i.e. charge transfer and covalency, providing the π-bonding energy. (V) Starting from the results of step IV, the relaxation between occupied and unoccupied orbitals of the same species is allowed for the σ-symmetry, in order to get the σ-polarization energy. (VI) All Cu orbitals and the unoccupied CO orbitals of the σ-symmetry are allowed to mix, giving the energy for the Cu σ-donation. (VII) The occupied orbitals on CO are allowed to mix with unoccupied orbitals on R, hence the CO σ-donation energy is attained. In earlier CSOV studies of NiCO and LiF, 55 the order of CSOV calculations was shown to hardly make any differences.

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(a)

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(b)

Figure 1: Typical slab models investigated: (a) 2×2×5 and (b) 4×4×6.

Results and Discussion Stability of Subsurface Oxygen It is experimentally shown that Osb is stable for at least an hour under the reducing electrocatalytic conditions found during CO2RR. 37,41 However, for the slab model of the Cu(100) surface with subsurface oxygen we find an energy gain of 2.37 (1.10) eV using DFT (SCC-DFTB) for the oxygen migrating to the surface versus remaining in the bulk. Therefore, it is important to investigate the barrier for Osb to diffuse from deeper layers to the surface to determine if it can be kinetically stable subsurface in the slab. Using full DFT we find a barrier for diffusion from the 3rd layer to the 2nd of about 0.21 eV, while the barrier for diffusion from the 2nd layer to the surface is merely 96 meV. Additionally, it is exothermic for Osb to diffuse from the 2nd layer to the surface by 2.54 eV. In the diffusion study the experimental lattice constant (3.61 Å) was used which might bias the result. The stability was thus also investigated for a 4×4×6 slab with the DFT optimized lattice constant (3.67 Å using RPBE), for which the oxygen atom is also more unstable at the subsurface sites by 2.42 eV (the 2nd layer), 2.05 eV(the 3rd layer), and 2.19 eV (the 4th layer) compared to the case when the oxygen atom is at the hollow site of the surface. Apparently, Osb is thermodynamically unstable in this model, too. Hence, with the slab model, the experimentally observed stability of Osb in the catalyst cannot be explained. To better model the actual catalyst, a nanocube with ca. 1.7 nm side and containing 500 Cu atoms and 91 O atoms was built from a Cu2O template, by 6


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manually removing all exposed oxygens as described in the Computational Details. The calculation method is SCC-DFTB. The initial and final geometries of the manual reduction process are shown in Figures 2a and 2b, respectively.

(a) (b) Figure 2: The nanocube (a) before and (b) after the manual reduction process. The cube initially contains 500 Cu atoms and 250 O atoms and after the manual reduction, it contains 500 Cu atoms and 91 O atoms. Contrary to what was found for the slab model, using SCC-DFTB on the nanocube model, Osb under the facets is found to be stable, as shown in Table 1. After moving Osb to the surface site and concurrent vacancy formation, the nanocube structure was reoptimized with Quasi Newton. Three kinds of Osb initial positions near three different surface sites (facet, edge, and corner) were tested. The system energy for the cube without moving Osb was taken as reference, as expressed in where , , and represent the energy difference, the energy before and after moving Osb to the surface, as calculated by SCC-DFTB. The values of are shown in Table 1 (Row 1).

Table 1: The Energy Change for Moving an Osb from Its Subsurface Site to the Nearest Surface Sitea site facet-1 facet-2 facet-3 facet-4 facet-5 facet-6 edge corner , (eV) 0.68 0.26 0.56 0.18 0.23 0.71 -0.21 -1.30 0.20 0.77 0.86 0.03 0.63 0.44 -0.65 -1.12 DFT (eV) 1.22 1.14 0.19 1.04 1.10 -0.27 -0.95 DFTB (eV) 1.58 a The 6 near-facet sites are from the 6 different facets of the nanocube. The initial position of Osb is between the 2nd and 3rd layers. Erel are the SCC-DFTB data from the 7



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nanocube models, and DFT and SCC-DFTB indicate data from the corresponding subcluster models extracted from the nanocube. A positive value means oxygen is more stable below the surface. Our results show that subsurface oxygen may be stable near the facet but not at edge or corner of the nanocube. This poses the question whether it is possible that after the near-corner Osb (site A in Figure S7 (f)) diffuses to the surface and desorbs upon hydrogenation, a neighboring near-facet Osb can diffuse to site A and thus open a path for depletion of oxygen from the nanocube. To investigate this possibility, the corner oxygen atom in Figure S7 (f) was removed, the structure was reoptimized with Quasi Newton and compared to the case where each of the three neighboring near-facet Osb with respect to the marked near-corner Osb in Figure S7 (e) was moved to site A in Figure S7 (f), simulating diffusion to this site. The associated energy changes are listed in Table 2. We conclude that after the near-corner Osb diffuses out of the nanocube, the neighboring near-facet Osb atoms are unlikely to diffuse to either the facet or the corner site, therefore oxygen depletion does not occur to a significant extent. Table 2: The Energy Change for Moving the Three Near-facet Osb Nearest Neighbors of the Corner Atom in Figure S7 (e) to Site Aa atom neighbor 1 neighbor 2 neighbor 3 0.89 1.07 0.46 a The calculation is done with SCC-DFTB and positive values indicate that this process is unfavorable. To ensure that the SCC-DFTB predictions from the nanocluster are reliable, subclusters cut from the whole nanocube model are calculated with both SCC-DFTB and DFT, as follows. The clusters were constructed by retaining all atoms located less than 6.5 Å or 7 Å from an investigated Osb atom, either before or after moving the Osb atom, while the rest are removed. Since the SCC-DFTB parameters were trained with spin-paired systems, the total number of valence electrons in the subclusters was chosen to be even. In addition, singly-coordinated atoms in the cut-out clusters were removed to ensure the valence states of the atoms in the subclusters resemble those in the whole nanocube. The subcluster structures are shown in Figure S8. The energy difference between the cases when Osb is on the surface and subsurface was calculated for the subcluster models with SCC-DFTB and DFT without further structure optimization. The calculation details for SCC-DFTB were the same as before. The spin-paired DFT calculations were done with the deMon2k code, 56 which is more suitable for these non-periodic models. The exchange–correlation functional PBE 47 and DZVP basis sets were used, and the results are shown in rows 2 and 3 of Table 1. PBE was selected as it was used for the parameterization of SCC-DFTB (see SI). First, the difference between SCC-DFTB and DFT is less than 1.38 eV and both methods return energies with the same sign (see Table 1). Second, for all near-facet cases within the subcluster model, as well as for the nanocube, O is significantly 8


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more stable subsurface. Therefore we conclude that, in agreement with experimental results, 41 Osb is stable inside the nanocube. Although the under-coordinated atoms on the edge or corner are typically the active sites for nanoparticle catalysts, 57 in the case of CuCubes the terrace sites where Osb is present may thus be the more catalytically relevant. This different behavior of the nanocube (obtained with SCC-DFTB) compared to the slab model is due to the fact that the former is not geometrically constrained, while the latter has a relatively small and fixed unit cell, as required by the connection to the bulk metal below. This implies fewer degrees of freedom to accommodate Osb in bulk copper. Reducing the Cu2O nanocube results in a rather disordered outer layer, i.e. with a wide range of Cu–Cu distances, as seen in the Cu– Cu pair-distribution functions in Figure 3.

(a) (b) Figure 3: Cu–Cu radial distribution functions for (a) Cu(100) 2x2x5 slab with Osb in the 2nd layer and (b) the nanocube model. Both are obtained with SCC-DFTB.

Influence of Subsurface Oxygen on CO Chemisorption In the tests on the influence of Osb on CO adsorption, the adsorption energy is calculated as where , , and are the total energies of the slab without adsorbate, with adsorbate, and of the gas phase CO molecule, respectively. The adsorption energy data for on-top adsorption, which was found to be most favorable in all cases, using full DFT and slab models are shown in Table 3 (bridge and hollow adsorption are given in the SI). Table 3: The Adsorption Energy Data for Slabs with Different Sizes of Unit Cella Energy(eV) 2×2×4 2×2×5 2×2×6 2×2×7 4×4×5 4×4×6 -0.52 -0.61 -0.57 -0.55 -0.56 -0.58 clean nd -1.08 -1.01 -1.04 -1.19 -1.22 -1.20 2 rd -0.64 -0.68 -0.61 -0.67 -0.67 3 9



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4th a Obtained with full DFT.

-

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-0.54

-

-0.51

We find a clear enhancement of the CO binding energy induced by the presence of subsurface oxygen with the effect lasting down to the 3rd layer. We also investigate whether this carries over to a structure extracted from the nanocube model and reoptimized as described in Computational Details. The DFT-optimized subcluster results also point toward a clear strengthening of the CO–Cu bond in systems with subsurface oxygen. The calculated binding energy with RPBE is −1.02 eV for the pure Cu42 system and −1.88 eV for the Cu34O8 cluster with subsurface oxygen, the respective structures are shown in Figure 4. With PBE, the corresponding energies are −1.47 eV and −2.11 eV. Somewhat surprisingly, the small Cu34O8 cluster prefers to bind CO in the hollow position.

(a)

(b)

Figure 4: The geometries for CO adsorption on (a) Cu42 and (b) Cu34O8 subclusters. The increased CO adsorption energy may increase the coverage of CO on the Cu surface, thus increasing the probability of OC–CO coupling, which is an important step towards the production of multi-C products such as ethylene. 58 The changes in the molecular orbitals upon adsorption of CO on metals have been investigated in detail using X-ray emission spectroscopy (XES) in combination with DFT. 59–62 The π-interaction is well described by the original Blyholder model 63 with three involved orbitals, CO π, CO π*, and metal 3dπ, forming, respectively, bonding, non-bonding and antibonding combinations in an allylic configuration. 61 Contrary to the textbook picture of σ-donation/π*-backdonation, the interaction in the σ-symmetry is found to be repulsive and the net adsorption strength is a balance between the π-bonding and σ-repulsion. The presence of an electronegative atom enhances the bonding by reducing the σ-repulsion via the removal of electron density from the surface atoms, as demonstrated by Xin et al.. 64 10


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The results from the molecular R–CO (R= Cu or OCu) chain models are consistent with this explanation. As shown in Table 4, given all orbitals frozen as obtained for R and CO separated, when CO and R are brought to the bonding distance, an initial Pauli repulsion appears. However, we can see that the presence of an extra oxygen atom greatly decreases this initial repulsion (by 1.59 eV). Since the sums of π-polarization (only allowing mixing of occupied and unoccupied on R and CO separately) and π-bonding (mixing between R and CO) for both systems are very close (-0.99 eV and -0.97 eV for Cu–CO and OCu–CO, respectively), it is concluded that the attraction in the π-symmetry is not influenced by the presence of the extra oxygen atom. For the σ-symmetry, since the energy of the CO 6σ* orbital is ca. 20 eV above the Fermi level, charge transfer from the Cu occupied orbitals to the CO 6σ* is difficult and allowing R to mix with unoccupied CO orbitals should be regarded as reducing repulsion. The sum of σ-polarization and Cu “σ-donation” (-0.81 eV and -0.55 eV for Cu–CO and OCu–CO, respectively) thus is consistent with less σ-repulsion for OCu compared to Cu. Finally, the CO σ-donation contains the energy change for the charge transfer from the CO σ-system to R in addition to additional relaxation of the σ-repulsion for Cu–CO, which explains the 0.35 eV greater energy lowering compared to OCu–CO. This is consistent with the picture where the σ-system has more charge withdrawn by the O atom in R. It is thus apparent that the presence of the extra oxygen atom reduces the σ-repulsion (including that in the initial repulsion) without influencing the π-attraction, resulting in a net enhancement of CO binding energy on the Cu surface. As shown in Table 3 we obtain somewhat lower resulting binding energies compared to the R-CO molecular chain since the charge withdrawal is distributed over more than one Cu, but the effect is still significant. Table 4: The Initial Repulsion Energy of R–CO (R=OCu or Cu) with Respect to the Energy in Step I and the Stepwise Contributions of Different Components. ∆E(eV) Cu–CO OCu–CO initial repulsion 2.27 0.68 π-polarization -0.06 -0.13 π-bonding -0.93 -0.84 -polarization -0.96 -0.70 Cu -donation -0.13 -0.22 CO -donation -0.68 -0.33 binding energy -0.50 -1.55

Conclusion Albeit Osb is not stable below the Cu surface in the slab model, it is stable below facets of a manually “reduced” Cu nanocube model which is consistent with experiment. 37,41 The subcluster models calculated with both DFT and SCC-DFTB 11



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confirm that the near-facet O can be significantly more stable below the surface than in the surface-adsorbed configuration, hence facets may have a catalytically important role in CO2 reduction using oxide-derived Cu-based catalysts. Besides, the internal structure of the nanocube is more disordered than the slab with Osb, which results in the better capacity of the nanocube model in accommodating Osb. Such disordered structure and the stability of subsurface oxygen in it is consistent with in situ experimental observations of the OD-Cu catalyst during CO2RR.37,41 With the help of DFT calculations, we demonstrated that the presence of Osb enhances the adsorption energy of CO on Cu(100), with effects lasting down to at least the 3rd layer. Thus, since the coverage depends on the balance between adsorbate-adsorbate repulsive interactions and the adsorption energy, the coverage of adsorbed CO on the Cu catalyst may be correspondingly increased, along with the probability of CO dimerization, which is a rate-determining step towards the production of ethylene.58

Author Information

Corresponding Author *Phone: +46-8-553 787 12. Fax: +46-8-553 786 01. E-mail: [email protected].

Notes The authors declare no competing financial interest.

Associated Content

Supporting Information Details of comparison between the adsorption energies of CO on different sites on Cu(100), SCC-DFTB parameterization, minima hopping, nanocube and subcluster structures, and the molecular chain structure are available.

Acknowledgement Funding from the Knut and Alice Wallenberg foundation (Grant No. KAW-2013.0020) , from the Swedish Research Council through the Swedish Research Links program (Grant No. 348-2013-6723) and from Energimyndigheten (Project 42024-1) is gratefully acknowledged. The calculations were performed using resources provided by the Swedish National Infrastructure for Computing (SNIC) at the HP2CN center. 12


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Stability and Effects of Subsurface Oxygen in Oxide-Derived Cu

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