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Mechanistic Insights for Low-Overpotential Electroreduction of CO2 to CO on Copper Nanowires David Raciti, Liang Cao, Chenyang Li, Kenneth J. T. Livi, Paul F. Rottmann, Kevin J. Hemker, Tim Mueller, and Chao Wang ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.7b03107 • Publication Date (Web): 09 Nov 2017 Downloaded from http://pubs.acs.org on November 9, 2017

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Mechanistic Insights for Low-Overpotential Electroreduction of CO2 to CO on Copper Nanowires David Raciti,1,† Liang Cao,2,3, † Chenyang Li,2 Kenneth J. T. Livi,2 Paul F. Rottmann,4 Kevin J. Hemker,4 Tim Mueller,2,* Chao Wang1,* 1Department

of Chemical and Biomolecular Engineering, 2Department of Materials Science and

Engineering and

3Department

of Physics and Astronomy,

4Department

of Mechanical

Engineering, Johns Hopkins University, Baltimore, Maryland 21218 †Equal

contribution.

*Email: [email protected]; [email protected]

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Abstract Recent developments of Copper (Cu)-based nanomaterials have enabled the electroreduction of CO2 at low overpotentials. The mechanism of low-overpotential CO2 reduction on these nanocatalysts however largely remains elusive. We report here a systematic investigation of CO2 reduction on highly dense Cu nanowires, with the focus placed on understanding the surface structure effects on the formation of *CO (* denotes an adsorption site on the catalyst surface) and the evolution of gas-phase CO product (CO(g)) at low overpotentials (more positive than −0.5 V). Cu nanowires of distinct nanocrystalline and surface structures are studied comparatively to build up the structure-property relationships, which are further interpreted by performing density functional theory (DFT) calculations of the reaction pathway on the various facets of Cu. A kinetic model reveals competition between CO(g) evolution and *CO poisoning depending on the electrode potential and surface structures. Open and metastable facets such as (110) and reconstructed (110) are found to be likely the active sites for the electroreduction of CO2 to CO at the low overpotentials.

Keywords: copper nanowires, electrocatalysis, carbon dioxide reduction, density functional theory


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INTRODUCTION Electrochemical reduction of CO2 represents a promising approach toward artificial carbon recycling and solar-fuel conversion.1–3 It was conventionally considered that high overpotentials are required to activate the stable CO2 molecules and make the CO2 reduction kinetics competitive against the evolution of hydrogen in aqueous solutions.4–6 Albeit being viewed as the most promising material for catalysing this reaction, most of the early studies on copper (Cu) extended surfaces were carried out at potentials around (or more negative than) ‒1.0 V versus reversible hydrogen electrode (RHE; the same potential scale is used in the following discussion unless otherwise specified) in order to draw significant product fluxes.4,7–9 However, recent reports on nanostructured Cu electrocatalysts have demonstrated the selective reduction of CO2 (e.g., >50% Faradaic efficiency (FE)) at potentials more positive than ‒0.5 V.10,11 CO (and formate, the hydration product of CO) is typically found to be the main product at such low overpotentials, which is a desirable product for the generation of syngas2 and also considered to be the key intermediate toward the formation of more valuable products (hydrocarbons and oxygenates) at higher overpotentials4, 9-13. It is thus important to understand the active sites and corresponding catalytic mechanisms for the low-overpotential reduction of CO2 to CO on these Cu nanocatalysts. In their studies of nanocrystalline Cu electrodes derived from cuprite (Cu2O), Kanan et al. proposed that grain boundaries that bind CO more strongly than terrace (e.g., (111) and (100)) and step (e.g. (211)) sites account for the selective reduction of CO to ethanol and acetic acid in KOH.12–14 However, it is unclear whether these grain boundary-associated sites are also the active sites for the production of CO from CO2 reduction in bicarbonate electrolytes.10 Nørskov et al. applied the computational hydrogen electrode (CHE) model15 to calculate the limiting potentials required for CO2 reduction on Cu facets, including (111), (100) and (211).16,17 Their calculations indicated that Cu(211) is the most active among these facets for the electroreduction of CO2 to CO and also to CH4, owing to the stronger binding of the reaction intermediates (*COOH, *CO, *CHO, etc., where * denotes an adsorption site on the catalyst surface) on the more open facets. 3 ACS Paragon Plus Environment

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Understanding the surface structure effects in Cu nanocatalysts remains a challenge, largely due to the lack of synthetic control and the difficulty in characterizing the atomic structures of the nanoscale surfaces. We have previously reported the synthesis of highly dense Cu nanowires and demonstrated their high catalytic activity for CO2 reduction.11 More recently, we have shown that these Cu nanowires are also capable of selectively reducing CO to ethanol and acetic acid in KOH.18 In this study, we aim to further examine these high-surface-area electrocatalysts to better understand the CO2 reduction mechanisms at low overpotentials (i.e., more positive than 0.5 V). Cu nanowires of distinct nanocrystalline structures were prepared by tuning the conditions of preparation and subsequently studied as electrocatalysts for the CO2 reduction reaction. The bulk and surface structures of these nanowires were characterized by using microscopic crystal orientation mapping and voltammetry studies of oxygen electrosorption, respectively. The derived structural information and electrocatalytic performances were then combined to establish structure-property relationships. Density functional theory (DFT)19,20 calculations were further performed to elucidate the active sites and catalytic mechanisms of these catalysts for reduction of CO2 to CO at the low overpotentials.

METHODS Synthesis CuO nanowires were grown on Cu mesh (McMaster-Carr, 100x100 mesh) following the method reported by Xia et al.21 Electrochemically reduced (ECR) Cu nanowires were produced by applying a cathodic potential (−0.4 V) on the oxidized Cu mesh in 0.1 M KHCO3 (Sigma-Aldrich, 99.95% trace metals basis) at room temperature (Figure S1). Alternatively, reduction was also obtained by annealing the oxidized Cu mesh at 150 °C for 15h, 200 °C for 1h, 300 °C for 1h and 300 °C for 15h in a flow of forming gas (5% H2/Ar, 20 sccm), which are denoted as HR-150, HR200, HR-300 (1h) and HR-300 (15h) in the discussion, respectively. Following the thermal 4 ACS Paragon Plus Environment

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treatment, the obtained Cu nanowires were cooled down to room temperature in the forming gas flow at a temperature-ramping rate of 30°C/min. Material Characterization Scanning electron microscopy (SEM) images were collected on a JEOL JSM-6700F fieldemission scanning electron microscope. A field-emission Phillips CM300-FEG (300 kV) was used for TEM imaging and collecting the electron diffraction patterns for crystal orientation analysis. Xray diffraction (XRD) patterns were collected on a PANalytical X’Pert3 Powder X-ray diffractometer equipped with a Cu Kα source (λ=0.15406). Electrochemical Studies The electrocatalytic activities of the Cu nanowires were measured using a customized gas-tight electrochemical cell and an Autolab 302 potentiostat (Metrohm). A Ag/AgCl electrode and a Pt mesh were used as the reference and counter electrode, respectively. A solution of 0.1 M KHCO3 was used as the electrolyte (the pH value was measured to be 6.8 when it was saturated with CO2). CO2 was delivered to the cathode compartment at a constant rate of 20 sccm and was allowed to purge for 10 minutes prior to each measurement. The cathode and anode compartments were separated with an anion exchange membrane (Selemion Inc.). All the catalytic results presented in the discussion are the average of the measurements over a duration of 1 hour and reported versus RHE with 85% iR drop correction. Gas- and liquid-phase products were analyzed by using GC-MS and NMR, respectively. Computational Methods All density functional theory calculations were performed with the Vienna Ab initio Simulation Package (VASP).22,23 The revised Perdew-Burke-Ernzerhof (RPBE)24 exchange-correlation functional was used for all DFT calculations unless otherwise noted. Spin polarization was taken into account in the calculations and the Methfessel–Paxton method25 of order 2 with a smearing parameter of 0.2 eV was employed to determine the electron occupancies. The Brillouin zone was sampled using grids generated by the k-point grid server with a minimum distance of 46.5 Å 5 ACS Paragon Plus Environment

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between adjacent lattice points in real space.26 The shift vectors were automatically chosen to minimize the number of irreducible k-points, and the grids were automatically optimized for slab calculations. Van der Waals interactions were accounted for by using the DFT-D3 method27 (referred to as RPBE-D3), which has been shown to accurately calculate properties for water and small organic molecules on metal surfaces.28–31 The calculations without inclusion of van der Waals interactions are referred as RPBE calculations. To treat the effects of the aqueous solvent on adsorption energies, we have used the VASPsol implicit solvation method.32,33 A total of 13 different Cu facets were considered for the CO2 reduction pathway: (310), (210), (332), (322), (311)-rec, (211), (221), (331), (311), (110), (110)-rec, (100), and (111). Hereby (311)-rec and (110)-rec denote (311) and (110) surfaces with a missing-row reconstruction, respectively. Additional details of the DFT calculations are provided in the Supporting Information.

RESULTS AND DISCUSSION Catalytic materials. Highly dense Cu nanowires were synthesized by reduction of pre-grown CuO nanowires (Figure 1 a-d). The electrochemically reduced ( Figure 1e) and hydrogen-reduced (Figure 1 f-g) nanowires prepared at relatively low temperatures (≤200 oC) possess similar dimensions to the CuO nanowires, namely ~100 nm in diameter and 20-50 μm in length. Slightly thicker and shorter nanowires were obtained by hydrogen reduction at higher temperatures, with the extent of deformation depending on the time of annealing. For the HR-300 nanowires, long (>20 μm) nanowires were still observed after 1 h of annealing (Figure 1h), whereas the majority was found to be shorter than 20 μm after annealing for 15 h (Figure 1i). These nanowires obtained at 300 oC exhibit granular structures with diameters typically in the range of 200-400 nm. X-ray diffraction (XRD) patterns collected for the nanowires derived from the different reduction conditions confirmed complete conversion to metallic Cu.11,18


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Figure 1. Synthesis of Cu nanowires. (a) Cu mesh. (b-d) CuO nanowires grown on the Cu mesh via thermal treatment in air. (e-i) The different types of Cu nanowires produced by reduction of the CuO nanowires (organized in columns; see the Methods for preparation conditions and the notations): (e) ECR, (f) HR-150, (g) HR-200, (h) HR-300 (1h) and (i) HR-300 (15h).


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In line with the dimensions measured in the SEM images, the surface roughness estimated by the electrochemical capacitance measurements scales inversely to the reduction temperature used to produce the Cu nanowires (Figure 2 a-c). This was done by recording cyclic voltammograms (CVs) in an Ar purged electrolyte (0.1 M KClO4, Sigma-Aldrich 99.99% trace metals basis) at various scanning rates. In a potential window (e.g., from 0 to 0.4 V vs. Ag/AgCl, Figure 2a) where redox peaks are absent, the double-layer width of the CV is proportional to the scanning rate, with the slope giving rise to the value of capacity (Figure 2b). The surface roughness factor is thus derived by comparing the measured capacitance to the value for a polycrystalline Cu electrode (Table 1).11,34 It was found that the ECR catalyst, which possess the longest length and smallest diameter nanowires in the studied series, have a surface roughness factor of 356; the roughness factor decreases from 145 for the HR-150 nanowires to 23 for the HR-300 (15h) nanowires (Figure 2c). Crystalline structures of the Cu nanowires were characterized by TEM-based automated crystal orientation mapping (TEM-ACOM). This was done by scanning a focused electron beam across the nanowires and recording electron diffraction patterns. The recorded diffraction patterns were then indexed by searching in pre-calculated databases to determine the orientation of each crystal domain (Figure 2d).35 No strong texture is observed from the inverse pole figures (Figure S2). The Cu nanowires prepared at higher temperatures generally have larger grain sizes and lower densities of grain boundaries. The grain boundary density is estimated to be ~0.12 nm-1 for the ECR nanowires, ~0.05 nm-1 for the HR-150 and HR-200 nanowires, and ~0.03-0.04 nm-1 for the HR-300 nanowires (Figure 2e). Fractions of the different types of boundaries were also found to vary among the nanowires, with 3 (coherent, such as twin) boundaries increasing from ~8% for the ECR to ~66% for the HR-300 (15h) nanowires and correspondingly, random, high-angle grain boundaries (≥15°) decreasing from ~81% to ~32% (Figure 2e and Table S1). Such temperature-dependent crystalline restructuring is in line with previous observations on polycrystalline Cu thin films.36–38 8 ACS Paragon Plus Environment

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Figure 2. Characterization of the Cu nanowires. (a-c) Surface roughness analysis using electrochemical capacitance measurements: (a) CVs recorded for the ECR nanowires between 0 and 0.4 V (vs. RHE) at various scan rates in Ar-saturated 0.1 M KClO4; (b) Plots of the doublelayer width against the scan rate; (c) Estimated surface roughness factors for the five types of Cu nanowires. (d, e) Nanocrystalline structures of the Cu nanowires depicted by TEM-ACOM analysis: (d) Crystal orientation maps constructed for the various types of Cu nanowires; (e) Distribution of the different types of grain boundaries. For each type of nanowires shown in (d) (from left to right): TEM image, crystal boundaries marked in red and crystal orientation maps along three azimuthal directions (the wire axis, out of the plane, and in-plane perpendicular to the wire axis) indexed according to the diffraction patterns (scale bar = 200 nm).


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Table 1. Summary of the roughness factors of the Cu catalysts studied. Sample

Capacitance (mF/cm2)

Surface Roughness Factors

Untreated Cu Mesh

0.081

2.8

HR-300 (15h)

0.676

23

HR-300 (1h)

0.748

26

HR-200

4.60

158

HR-150

4.22

145

ECR

10.47

356

CO2 Reduction Electrocatalysis. Among the five types of Cu nanowires, the ECR nanowires give the highest geometric current densities (jtot, per electrode area) for CO2 reduction throughout the low-overpotential region, increasing from 0.09 mA/cm2 at 0.2 V to 2.22 mA/cm2 at 0.5 V (Figure 3a). The HR nanowires produce smaller currents than the ECR nanowires, with the value of jtot roughly decreasing as the preparation temperature increases. At 0.5 V, the least active HR-300 (15h) nanowires have a geometric current density of 0.45 mA/cm2, which is only ~20% of that for the ECR nanowires. The relatively small current densities (on the order of 1 mA/cm2) in the low-overpotential region suggest that the local pH on the nanowire surface may not differ substantially from that in the bulk electrolyte.39 The Cu nanowires prepared at lower temperatures are not only more active, but also more selective for the electroreduction of CO2 (Figure 3 c-d). In the low-overpotential region, CO and formate were found to be the two major products, with the Faradaic efficiencies (FEs) generally increasing as the potential becomes more negative. The ECR nanowires have a FE toward CO (FECO) of 29% at 0.2 V, compared to 17%, 5% and 2% for the HR-150, HR-200 and HR-300 (1h) nanowires, respectively (Figure 3c). No CO2 reduction products were detected in significant amounts for the HR-300 (15h) nanowires at this potential. The ECR nanowires achieves the 10 ACS Paragon Plus Environment

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highest FECO of ~62% at 0.4 V. In comparison, the highest FECO is found to be 22%, 13%, 6% and 3% for the HR-150, HR-200, HR-300 (1h) and HR-300 (15h) nanowires, respectively, which are all achieved at 0.5 V. The selectivity toward formate follows a similar trend as that for CO, with the ECR nanowires also being the most selective. At 0.5 V, the ECR nanowires reaches the highest FE toward formate (FEHCOOH) at 25%, whereas the HR nanowires exhibit decreasing FEHCOOH (3-16%) as the preparation temperature increased). Noticeably, small amounts of C2 products (ethanol, ethylene and ethanol) were detected on the ECR nanowires at 0.5 V with a total FE of 2.8% (Figure S5). The distinct catalytic selectivities of the different types of Cu nanowires are better visualized by the comparison of the total (JCO2) and partial (JCO) current densities for CO2 reduction. At 0.5 V, the ECR nanowires deliver a JCO2 of 1.81 mA/cm2, whereas the HR-300 nanowires give merely ~2% of that value (Figure 3b). At this potential, JCO reaches 1.18 mA/cm2 on the ECR nanowires, as opposed to 0.25 mA/cm2 by the HR-150 nanowires and even smaller values by the other HR nanowires (Figure S6).


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Figure 3. Performance of the Cu nanowire catalysts for CO2 reduction in CO2 saturated 0.1 M KHCO3. (a, b) Plots of (a) total and (b) CO2 reduction current densities per geometric area of the electrode. (c, d) Faradaic efficiencies for (c) CO and (d) formate at each electrode potential studied.

Surface structure analysis. To understand the distinct catalytic performances of the different types of Cu nanowires, we used electrosorption of oxygen (e.g., OHad), in Ar saturated 1 M KOH, to probe the surface structures of these nanomaterials. It has previously been reported that the anodic oxidation of a Cu surface is preceded by the electrosorption of oxygen, with different Cu facets exhibiting distinctive, reversible adsorption/desorption peaks in the voltammograms.40 This has allowed us to follow the surface structure of the Cu nanowires by comparing their oxygen electrosorption patterns to those previously reported for Cu single crystals. Figure 4a summarizes 12 ACS Paragon Plus Environment

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the cyclic voltammograms (CVs) recorded for the five types of Cu nanowires. A series of peaks appear in the anodic scans prior to the onset of irreversible oxidation at 0.5 V. The peaks at ca. 0.36, 0.4-0.42 and 0.44-0.46 V can be assigned to the OHad features associated with the (100), (110) and (111) facet of face-centered cubic (fcc) Cu, respectively.18, 40 The slight shifts in peak positions among the different types of Cu nanowires is likely a result of the varying surface roughness factors and/or the capacitance effect.41,42 In addition to these three peaks, another two peaks were observed at ~0.1-0.15 and ~0.32-0.34 V, which were not reported in the previous studies of extended surfaces.40 In particular, the peak ~0.32-0.34 V (denoted as * in Figure 4a and the following discussion) was found to become more pronounced for the Cu nanowires prepared at higher temperatures. To investigate potential assignments for these two peaks, we have performed DFT calculations for the binding energies of OH on the various Cu facets (see Table S11 and the corresponding discussion in the Supporting Information). The calculations show that (110) and (100) bind to OH more strongly than (111) by 14.3 and 16.3 kJ/mol, respectively, which is consistent with the above observation of OHad peaks at lower potentials (Figure 4b). Facets of higher indexes typically bind to OH even more strongly than these low-index facets; e.g., (211) has a binding energy of ~38 kJ/mol higher than (111). The low-coordination step sites on these facets are likely to be more oxophilic than the terrace sites and give rise to the low-potential peak at ~0.1-0.15 V.43 We had previously assigned the (211) or the (110)-rec facets to the * peak at ~0.32-0.34 V (Figure 4a).18 However this peak appears to be correlated to a CO-TPD peak at about 250 K, suggesting that the surface sites responsible for this feature bind OH slightly more strongly than (100) and CO more strongly than (110). From the thirteen facets we evaluated with DFT (Tables S11 and S12), (210) and (310) could meet these criteria. However, such facets possess higher surface energies than the low-index facets (Table S6) and it remains unclear why they would become more prevalent on the Cu nanowires prepared at higher temperatures (Figure 4c). Alternatively, this feature is possibly associated with the coherent boundary sites as also exhibited in the TEM-ACOM analysis (Figure 2e), which has not 13 ACS Paragon Plus Environment

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been taken into account in the present calculations. Using the relative peak areas (where the values for the HR-150 nanowires are set as the units) derived from deconvolution of the oxygen electrosorption patterns (Figure 4c), we were able to establish the correlations between the surface structures and the electrocatalytic activities (Figure 5). Hereby the electrocatalytic activity is expressed as the partial current density toward CO at a given potential, jCO, as normalized by the active surface areas (estimated from the surface roughness factors, see Figure S6). It is found that the catalytic activity for reducing CO2 to CO on the Cu nanowires correlates well to the relative peak area of Cu(110) and Cu(100), suggesting that these open facets account for the high activity and high selectivity observed on the ECR nanowires (Figure 5 a and b). In contrast, (111), as well as the surface sites associated with the OHad peak at ~0.32-0.34 V, are unlikely to be responsible for the CO2 reduction activity on the Cu nanowires, as indicated by the poor or negative correlations between jCO and the relative peak areas (Figure 5 c and d). Similar plots relating to the grain boundary densities show that the catalysts enriched with random, high-angle grain boundaries are more active for the electroreduction of CO2 to CO, whereas those with more coherent (Σ3) boundaries are inactive (Figure 5 e and f).


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Figure 4. Surface structure analysis for the Cu nanowires. (a) CVs recorded in Ar purged 1 M KOH capturing the surface-specific adsorption of oxygen (OHad). (b) Calculated OH binding energies for the Cu facets referred to Cu(111). (c) Relative peak areas (versus the value for the HR-150 nanowires) derived from the deconvolution for the OHad peaks shown in Figure S8.


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Figure 5. Correlations between the electrocatalytic activity (jCO) and the relative peak area derived from the oxygen electrosorption patterns of (a) (100), (b) (110), (c) (111) and (d) the surface sites associated with the OHad peak at 0.32-0.34 V. Also shown are the correlations between jCO and the relative densities of (e) high-angle grain and (f) coherent (Σ3) boundaries (also versus the HR150 nanowires).

Reaction pathway and catalytic mechanisms. We have performed further DFT calculations to interpret the structure-property relationships established above. The reaction pathway 16 ACS Paragon Plus Environment

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CO 2 (g)  *COOH  *CO  CO(g) is considered according to the previously reported computational studies16,17,44 (Figure 6a, see more details in the Supporting Information). We assume the transfer of proton and electron are practically simultaneous16,17 at the low overpotentials performed in this study considering that the standard reduction potential for

CO 2 (g)  *CO 2 is -1.25 V.16,17 Additionally, if formation of the CO2- radical was favored over *COOH, CO2- would still likely go via *COOH to produce CO(g), in which case the formation of *COOH would still be the potential-limiting step.45 Under the assumption that the rate-limiting step is CO 2 (g)  *COOH , our calculations show that the onset potential (at which the aforementioned step becomes exergonic) varies from −0.20 V to −0.75 V for the different types of Cu surface, with (210), (310), and (332) found to possess the lowest values (Table S12). Our results are in close agreement with the previously reported values for this reaction on the (211), (100) and (111) facets.16,17 However this approach is limited in that it does not directly consider the adsorption energy of *CO on the Cu surface, which can be the determining factor in the rate at which CO(g) is produced. To better understand the structure effects on the CO2 reduction electrocatalysis, we extended beyond the above approach and constructed a simple two-step kinetic model of CO2 reduction to CO(g). In the first step, CO2(g) is reduced to *CO, going through an intermediate state of *COOH. We have estimated the activation free energy for this step using a BrønstedEvans-Polyani46 relationship in which the activation free energy is the change in free energy from CO2(g) to *COOH plus 0.18 eV. The difference of 0.18 eV was determined based on the calculated reaction energy and activation energy for the hydrogenation of *CO2 on Cu(211).44 The second step in this model is the desorption of *CO from the surface, for which the activation free energy is simply the desorption energy of *CO in solution. Unlike the first step, this second step does not directly depend on the applied potential. There is some indirect dependence on the potential because the CO coverage, and hence free energy of *CO, depends on the applied potential. Here 17 ACS Paragon Plus Environment

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we assume dilute coverage because for each facet the first step will be rate-limiting at the onset potential for that facet. More details of this model are provided in the Supporting Information. We can further simplify the kinetic model by using the fact that there is a nearly linear relationship between *CO and *COOH on the 13 different facets studied (Figure S13). Using this relationship, the activity of CO2 reduction to CO(g) can be expressed as a function of the applied potential U and the free energy of adsorption of CO, ΔG(*CO) , following

ACO  U  =  max  2.15  ΔG(*CO)  + 0.926 + eU, 0.362  ΔG(*CO)  ,

(1)

where we have used the negative of the free energy of activation (in eV) as the measure of activity. This results in a potential-dependent volcano-type relationship between ΔG(*CO) and the catalytic activity for CO2 reduction (Figure 6b). At less negative potentials, the facets that bind to CO most strongly are the most active ones because they also tend to have relatively low values for ΔG(*COOH) . However, the free energy of CO desorption is also large for these surfaces, limiting their activity. As the potential is decreased, surfaces that bind to CO less strongly start to become more active as the barrier for the first step decreases. The facet that binds to CO the most weakly eventually becomes the most active, because the free energy of CO desorption is the smallest on this type of surface. Although the exact potentials at which different facets become active will depend on the exact values of the free energies of the transition states, the same trend can be expected to hold in general. The (110) facet is notable in that it shows a significant deviation from the linear relationship between G(*CO) and G(*COOH), suggesting even higher activity than the simple volcano plot described by equation (1) would imply (Figure S13). Plots of activity as a function of G(*CO) and G(*COOH) at potentials of 0 V, −0.2 V and −0.4 V are shown in Figure 7. These plots indicate that the (110) (including possibly (110)-rec as well) and (100) surfaces can be expected to be among the most active facets at low overpotentials. The feature that binds to CO strongly on the HR nanowires prepared at high temperatures,18 denoted by a red dashed line in Figure 7, is unlikely to be very active due to the large CO desorption energy, which 18 ACS Paragon Plus Environment

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is consistent with the experimental observation that the HR-300 nanowires are inactive for the reduction of CO2 to CO.

Figure 6. (a) DFT calculated free energy diagrams for CO2 reduction to CO(g) at an applied potential of 0 V. The green and shadowed rectangle area in (a) shows the low and high limits of chemical potentials of experimentally-observed CO(g) (Table S13). (b) Volcano plot of the activity determined by kinetic energy barrier along the pathway, as a function of G(*CO) and applied potential. The volcano plot is based on the fitted linear relationship between G(*COOH) and G(*CO) (Figure S12). The DFT(Uapplied=x) in (b), where x is the applied potential, means that the activity is based on DFT calculations rather than the linear approximation.


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Figure 7. Trends in CO2 reduction activity at an applied potential of (a) 0 V, (b) −0.2 V, and (c) −0.4 V, plotted as a function of both *CO and *COOH binding energies. The points A, B, C, D, E, F, G, and H represent (310), (210), (332), (322), (311)-rec, (221), and (331), and (311) facets respectively. The (110), (110)-rec, and (100) facets have the highest activities at an applied potential of 0 V, −0.2 V, and −0.4 V, respectively. The red dashed vertical line indicates the *CO binding energy corresponding to the right-most peak on the CO-TPD of HR-150 and HR-300 (15h) nanowires.18

We note that the activity of the different facets will depend on the steady-state pressure of CO(g) (Table S4), as the chemical potential of gas-phase CO is a function of the partial pressure of CO(g). If the chemical potential of *CO on a facet is below that of CO(g), the facet can be expected to become saturated with *CO and catalytically inactive for CO(g) production (i.e. the thermodynamic driving force will be for a net flux of CO molecules adsorbing on the facet, not desorbing). For example, at a CO(g) partial pressure of 1 atm, we predict all facets studied to be catalytically inactive for CO production at an applied potential of 0 V (Figure 6a). However, at lower partial pressures, such as the steady-state pressure observed for HR-300 (15h) at U=−0.295 V, which is 0.22 Pa (Table S4), we predict that all facets can be active for CO(g) 20 ACS Paragon Plus Environment

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production (Figure 6a). For a single crystal surface that is producing CO(g) in a closed cell, the partial pressure of CO(g) could increase until it is nearly at the equilibrium pressure, at which point CO2(g) reduction to CO(g) will slow and other reduction products will appear (provided the applied potential is sufficiently low). This explains the experimental observation that CO2 is only reduced to C2H4 on single crystals after a significant amount of CO(g) has been produced.16 A full comparison of the calculated free energies for *CO and the corresponding experimental chemical potentials of CO(g) is given in Table S14. For multifaceted materials such as the nanowires studied here, it is possible for the steadystate partial pressure to reach a level at which some surfaces can be catalytically active for CO production and others cannot. Our calculations (Table S13 and Table S14) suggest that for the ECR wires, the steady-state pressure at potentials of −0.295 V and below is sufficiently high to make the bare (211) surface, which binds CO relatively strongly, catalytically inactive for CO2 reduction to CO, although CO2 could be reduced to other products on this surface at more negative potentials. At low overpotentials, the coverage of *CO on the (211) surface can be expected to increase until the desorption of *CO to form CO(g) is exergonic, which we predict to occur at about ½ monolayer coverage. However, at this coverage the free energy of *COOH also increases (Figure 6a), increasing the activation barrier for CO2 reduction on the (211) surface by about 80 meV. Taken together with the experimental results (Figure 5 a-d), our theoretical calculations (Figures 6 and 7) suggest that among the surfaces studied, the (110) (including (110)-rec) and (100) surfaces are most likely to be responsible for the high catalytic activity and selectivity on the ECR wires at low overpotentials. In their early studies on extended single-crystal surfaces at high overpotentials (more negative than -1.0 V), Hori et al. also observed that Cu(110) had the highest FEs toward CO and HCOOH in CO2 reduction.8,9 More recently, Hahn et al. investigated epitaxially grown Cu films with (111), (100) and (751) surfaces for CO2 reduction and showed that Cu(100) and Cu(751) (with (110) terrace) had higher activities than Cu(111) for CO2 reduction, 21 ACS Paragon Plus Environment

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albeit also performed at relatively high overpotentials (from -0.9 to -1.10 V).47 It should be pointed out that other active sites may also be present on the Cu nanowires in addition to these open facets. In particular, our experimental results indicate that there is a correlation between the surface density of high-angle grain boundaries and catalytic activity at low overpotentials (Figure 5 e and f). The atomic scale surface structure of these high-angle grain boundaries is not well characterized, making it difficult to construct a computational free energy diagram for it as we have for the crystalline facets. It is possible that there exist sites on these surfaces with similarly optimal CO and COOH binding energies, making them catalytically active as well. We note that the relatively low *COOH free energy on the (110) and (211) surfaces appears to be due to the availability of sites with relatively low coordination, and similarly low-coordinated sites may exist at high-angle grain boundaries. Another possibility is that on surfaces on which the adsorption energy of OH is relatively high, such as the (211) facet (Table S11), adsorbed OH may limit CO2 reduction by blocking surface sites at low overpotentials.16,18,48 The fact that the ECR nanowires are particularly active for CO2 reduction suggests that the active sites are metastable. The low-temperature synthesis of the ECR wires allows these metastable sites to persist for an extended period of time. This suggests that the Cu(100) surface might not be responsible for the enhanced activity, as it is a relatively stable, low-energy surface that shows up prominently on a calculated Wulff construction of a Cu particle (Figure S11).18,49 On the other hand, Cu(110) is likely metastable, and it may be created by the removal of oxygen from the corresponding, relatively low-energy, CuO(011) surface.18 Similarly high-angle grain boundaries are likely to be metastable. These results suggest that the Cu(110) surface, surfaces of high-angle grain boundaries, or some closely related metastable surface feature are responsible for the high activity and selectivity of the ECR nanowires. The room-temperature synthesis of these features through electrochemical reduction provides a promising path for the synthesis of similar desirable metastable features on the surfaces of other catalytically active materials. 22 ACS Paragon Plus Environment

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CONCLUSIONS The low-overpotential electroreduction of CO2 to CO has been systematically investigated by applying highly dense Cu nanowires as the catalysts. By performing comparative studies using Cu nanowires of distinct nanocrystalline and surface structures, we drew structure-property relationships for these high-surface-area electrocatalysts and identified open facets such as (110) (including reconstructed (110)) to likely be the active sites for CO2 reduction at low overpotentials (more positive than −0.5 V). The established structure-property correlations were further understood and supported by performing DFT calculations of the reaction pathway of CO2 reduction to CO on various facets of Cu. Furthermore, a two-step kinetic model was developed to elucidate the potential-dependent shift of the volcano behaviour in catalytic activity as a function of the CO binding energy. Our findings highlight the importance of balancing the CO2 (and CO) hydrogenation barrier and CO desorption energy in the design of selective electrocatalysts for CO2 reduction.

ASSOCIATED CONTENT AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]; [email protected] Author Contributions †D.R. and L.C. contributed equally. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes The authors declare no competing financial interests.


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Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Experimental details of the Cu nanowire synthesis, material characterization and electrochemical characterization are given. Theoretical details of DFT calculations are also given. Acknowledgement This work was supported by the National Science Foundation (CHE-1437396). C. Wang and D. Raciti also acknowledge the support by the Catalyst Award from Johns Hopkins University. T. Mueller and L. Cao acknowledge computational resources provided by XSEDE through award DMR-140068 and by the Maryland Advanced Research Computing Center (MARCC). Atomicscale structural images were generated using VESTA.50 Participation of P. Rottmann and K. Hemker was supported by DOE Basic Energy Sciences (DE-FG02-07ER46437). We also thank Fenglin Yuan for his help on the Python codes. This study made use of the Johns Hopkins University Department of Chemistry Core Facilities with Dr. Joel A. Tang’s assistance. The authors thank Mr. Robert Clapper from Shimadzu for the help on the GC-MS analysis.


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