Adsorption, activation, and conversion of carbon dioxide on small copper–tin nanoclusters

Abstract

Carbon dioxide (CO₂) conversion to value-added chemicals is an attractive solution to reduce globally accelerating CO₂ emissions. Among the non-precious and abundant metals tested so far, copper (Cu) is one of the best electrocatalysts to convert CO₂ into more than thirty different hydrocarbons and alcohols. However, the selectivity for desired products is often too low. We present a computational investigation of the effects of nanostructuring, doping, and support on the activity and selectivity of Cu–Sn catalysts. Density functional theory calculations were conducted to explore the possibility of using small Cu–Sn clusters, Cu_4−nSn_n (n = 0–4), isolated or supported on graphene and γ-Al₂O₃, to activate CO₂ and convert it to carbon monoxide (CO) and formic acid (HCOOH). First, a detailed analysis of the structure, stability, and electronic properties of Cu_4−nSn_n clusters and their ability to absorb and activate CO₂ was considered. Then, the kinetics of the gas phase CO₂ direct dissociation on Cu_4−nSn_n to generate CO was determined. Finally, the mechanism of electrocatalytic CO₂ reduction to CO and HCOOH on Cu_4−nSn_n, Cu_4−nSn_n/graphene and Cu_4−nSn_n/γ-Al₂O₃ was computed. The selectivity towards the competitive electrochemical hydrogen evolution reaction on these catalysts was also considered. The Cu₂Sn₂ cluster suppresses the hydrogen evolution reaction and is highly selective towards CO, if unsupported, or HCOOH if supported on graphene. This study demonstrates that the Cu₂Sn₂ cluster is a potential candidate for the electrocatalytic conversion of the CO₂ molecule. Moreover, it identifies insightful structure–property relationships in Cu-based nanocatalysts, highlighting the influence of composition and catalyst support on CO₂ activation.

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1. Introduction

The increasing carbon dioxide (CO₂) level in the environment is the main contributor to global warming.¹ The global temperature has increased by 1 °C compared to the pre-industrial era and will increase by a further 1.5 °C within the next two decades.² One of the solutions is CO₂ utilization, that is, the conversion of captured CO₂ to value-added materials and chemicals.³ CO₂ conversion is a flourishing area of research as it could use CO₂ as a virtually illimited source of carbon feedstock material. One of the most promising approaches is the electrochemical CO₂ reduction (eCO₂R) to other C₁ products such as formic acid (HCOOH) and carbon monoxide (CO), and C₂₊ substances such as ethylene, ethanol, and others.^2,3 The main challenge in eCO₂R research lies in the activation of CO₂ minimizing competitive pathways such as the hydrogen evolution reaction (HER, H⁺ + e⁻ → 1/2H₂). Thus, catalysts are indispensable for accelerating the eCO₂R process to recycle CO₂ to value-added chemicals with high faradaic efficiency (FE).

Copper (Cu) is the only metal surface that reduces CO₂ to more than thirty different hydrocarbons and oxygenates.⁴ For this reason, Cu is considered the best candidate for eCO₂R but lacks the required selectivity. For example, the FE achieved for ethylene, the simplest of the C₂ products, is no more than 40% at 200 mA cm⁻²,⁵ still too low for practical application.⁶ However, the synthesis of small nanoclusters, Cu_n (n = 3, 4, 8, 20), supported on alumina (Al₂O₃) exhibited high turnover for the CO₂ conversion to methanol.^7,8 Larger Cu nanoclusters (1.24 nm) also showed a high FE of 93.5% at −0.50 V (RHE) for CO₂ reduction to CO.⁹ A complementary approach to nano-structuring is the incorporation of a second metal¹⁰ to alter the electronic structure (ligand and strain effects)¹¹ of the host material and the atomic arrangement (geometric ensemble effects) of the active sites. Metal doping can result in the introduction of new actives sites in the bimetallic catalyst that are not present in its mono-metallic counterparts. Experimental results also showed that the activity and selectivity of eCO₂R to HCOOH and CO can be enhanced by incorporating metals such as gold (Au), indium (In), palladium (Pd), cobalt (Co), tungsten (W), and tin (Sn).¹² Au, In and Pd are expensive and/or scarce, whereas Co and W are toxic. Earth-abundant and non-toxic metals such as Sn would be the preferred option for a long-term Cu-based sustainable catalyst solution to eCO₂R. The use of Sn is also attractive because of its high selectivity toward C₁ products.¹³ Cu–Sn catalysts suppress the HER by weakening the binding strength of H and show selectivity for formate (HCOO⁻) or CO depending on the Cu/Sn ratio.¹⁴ In this regard, a techno-economic evaluation of low-temperature CO₂ electrolysis showed that achieving high yields of C₁ products such as HCOOH and CO is competitive to conventional processes compared to achieving C₂ products, whose production has substantially higher costs.¹⁵ Consequently, significant progress in the electrochemical CO₂ reduction to C₁ products could be achieved using Cu-based alloy clusters doped with earth-abundant metals such as Sn.

The use of small four-atom Cu clusters, Cu₄, as catalysts for the conversion of CO₂, to not only methanol but also other several chemicals, has been reported in several experimental and theoretical studies.^7,8,16–18 Previous computational work focused on tetrahedral non-supported bimetallic Cu–Pt,¹⁹ Cu–Pd and Cu–Ni,²⁰ and Cu–Zr.²¹ But there is still a lack of understanding regarding the effect of the inclusion of Sn atoms in small copper clusters. Moreover, in the realistic electrocatalytic reaction, a cluster is supported by oxides and other substrates. Lee et al. studied CO₂ conversion on Fe–Cu–K catalysts supported on various oxides, such as Al₂O₃, SiO₂, and TiO₂, and found that alumina enhances CO₂ chemisorption and, therefore, its activation.⁹ Another study found stable Cu nanoclusters (n = 3, 4, 8, 20 and so forth) synthesized on alumina (Al₂O₃) to be active towards CO₂ conversion to methanol.⁷ Similarly, Cu₄ deposited on Al₂O₃ exhibited a high turnover for catalytic CO₂ hydrogenation.⁸ A density functional theory (DFT) study of CO₂ adsorption on Cu–Co clusters supported on partially hydroxylated Al₂O₃ showed that CO₂ interacts not only with the catalysts but also with the support via hydrogen bond with the hydroxyl group or direct bonding of the O atoms of the alumina surface.²² This process stabilizes and activates the adsorbed CO₂ species (elongated C–O bonds and reduced O–C–O angle). Apart from metal oxides, graphene, a 2D material, has been used to disperse Cu clusters for the eCO₂R to C₁ products.²³ Anchoring Cu atoms over defective diamond graphene showed high catalytic activity for the selective conversion of acetylene to ethylene.²⁴ Nitrogen-doped (ND) graphene-supported gold clusters catalyse the CO₂ electroreduction to CO.²⁵ Experimental work showed the efficient formation of two-carbon (C₂) products on small Cu clusters embedded in mesoporous carbon spheres,²⁶ metal-organic frameworks or Cu-coordinated polymers.^27,28 Li et al. studied the catalytic activity of small copper clusters anchored over defective nanodiamond graphene for CO₂ reduction and found Cu₃/ND@graphene and Cu₄/ND@graphene to convert CO₂ to C₁ compounds.²⁹

On this basis, we present a systematic computational investigation, based on DFT calculations, of the structure, stability, and electronic properties of the Cu–Sn clusters Cu_4−nSn_n (n = 1–4) and compare their results to monometallic Cu₄ and Sn₄ systems. We considered a detailed characterization of the adsorption and activation (geometry changes, charge transfer) of CO₂ on the most stable structure of the monometallic and bimetallic Cu–Sn clusters and their efficacy towards the direct gas-phase dissociation of CO₂ to CO and O fragments. We then compare the free energy profiles for the eCO₂R to HCOOH and CO on isolated Cu_4−nSn_n (n = 1–4) clusters as well as on clusters supported on graphene (Cu_4−nSn_n/graphene) and on the partially hydroxylated (110) surface of γ-Al₂O₃ (Cu_4−nSn_n/γ-Al₂O₃). Finally, the free energies of the competitive HER on Cu_4−nSn_n, Cu_4−nSn_n/graphene and Cu_4−nSn_n/γ-Al₂O₃ were also computed, from which we could conclude on the effect of composition and support on the selectivity towards CO or HCOOH.

2. Computational details

2.1 Computational methods

All DFT calculations were conducted with the “Vienna ab initio simulation package” (VASP, version 6.3.0)³⁰ together with the Perdew–Burke–Ernzerhof (PBE) exchange correlation functional³¹ and the dispersion correction (Grimme's-D3).^32,33 A single (1 × 1 × 1) k-point and a (3 × 3 × 1) mesh were used for the nanoclusters and surface models, respectively, to sample the Brillouin zone of the simulation supercell. A plane-wave basis set within the framework of the projector augmented wave method³³ was used with a kinetic energy cutoff set to 450 eV. The transition state structures connecting adsorbed CO₂* and CO* + O* were determined using the climbing image nudged elastic band (cNEB) as implemented in VASP.^33,34 The eCO₂R to HCOOH and CO includes two concerted proton–electron transfer (CPET) steps. For example, the CPET steps leading to CO formation are (i) CO₂* + (H⁺ + e⁻) → COOH* and (ii) COOH* + (H⁺ + e⁻) → CO + H₂O.³⁵ The competitive HER to generate H₂ involves a single CPET step: H⁺ + e⁻ → 1/2 H₂. The reaction free energies of these CPET steps were calculated using the computational hydrogen electrode (CHE) approach:³⁶

ΔG = ΔE_DFT + ΔE_ZPE − TΔS + ΔG_pH + ΔG_U (1)

The terms in eqn (1) are the reaction energy, ΔE_DFT, computed at the DFT-PBE level including the effect of hydration obtained with the VASPsol implicit solvation model;^37,38 the change of zero point energy (ΔE_ZPE); the entropy change, ΔS, and the temperature, T, of the system set at 298.15 K; the free energy correction due to the difference in proton concentration, ΔG_pH = 2.303·k_B·T·pH (pH = 0 as assumed in this paper); and the free energy correction due to the difference in electrode potential, ΔG_U = −neU, where n is the number of electrons transferred, e is the electronic charge, and U is the applied potential (U = 0 as assumed in this paper). The zero-point energies and entropies were determined within the harmonic approximation by taking the vibrational frequencies of adsorbates and molecules calculated with DFT. Based on previous studies using the CHE model,³⁹ the variations in E_ZPE and S were considered to be small compared to variations in the reaction energies based on E_DFT alone and, therefore, to be constant for all systems in this study.

2.2 Computational models

The structures of Cu–Sn nanocluster models were identified using the ab initio random structure searching (AIRSS) code,⁴⁰ an unbiased approach that has been used extensively to predict the structure of a variety of materials, including clusters.^41,42 The starting point for each calculation is a set of atoms corresponding to the monometallic, (Cu₄) and (Sn₄), and bimetallic, Cu_4−nSn_n (n = 1–3), clusters placed randomly within the central volume of a large cube of edge length 20 Å to minimize the interactions between the periodic images. For each system, we generated fifty random structures. The efficiency of the searches was improved by employing constraints on the minimum interatomic separations of the initial structure: d(Cu–Cu) = 2.1 Å and d(Sn–Sn) = 2.6 Å.⁴² The energies of the atomic structures generated by AIRSS were determined with VASP. From the DFT energies of the random structure, the Boltzmann factor f_i corresponding to each ith isomer was evaluated according to the expressionwhere E_i is the energy of the ith candidate structure and E₀ is the energy of the most stable candidate structure. We selected the candidates with a Boltzmann factor f_i ≥ 0.01 and, to increase our sampling, we also considered ten to fifteen randomly selected structures such that 1 ≤ E_i − E₀ ≤ 3 eV. These structures were subject to geometry optimization to determine the most stable Cu, Sn and Cu–Sn clusters. During structural relaxation, we used a convergence criterion of 10⁻⁶ eV for the energy change and 0.01 eV Å⁻¹ for the forces. The structures of the most stable clusters were supported on graphene and on the (110) surface of γ-Al₂O₃. The coordinates of γ-Al₂O₃ (P2₁/m space group)⁴³ were used as the starting geometry to optimize the internal coordinates and cell parameters of the bulk structure of γ-Al₂O₃.⁴⁴ The values of the optimized lattice parameters of γ-Al₂O₃ were a = 5.538 Å, b = 8.347 Å, c = 8.024 Å, β = 90.6° and α = γ = 90.0°. Starting from the optimized bulk structure, we generated a 2 × 2 × 1 supercell of partially hydroxylated slabs of the γ-Al₂O₃ (110) surface containing three atomic layers. In fact, previous IR experiments showed that isolated hydroxyl (OH) groups (around 3701 cm⁻¹) at the γ-Al₂O₃ surfaces were present even after annealing at 900 °C for 30 min.⁴⁵ The lattice parameters of the unit cell were 16.05 Å and 10.02 Å and the vacuum space in the z-direction was set to 21.71 Å. All atoms were relaxed during the optimization. For graphene-supported nanoclusters, a 5 × 5 × 1 supercell was used. Calculation of the energy of adsorption of the Cu_4−nSn_n (n = 0–4) clusters on the graphene and γ-Al₂O₃ (100) surfaces shows a favorable interaction between the Cu–Sn clusters and the support (Table S2 in the ESI†). The adsorption and dissociation of CO₂ were studied using the most stable structure of every composition of the Cu_4−nSn_n (n = 1–4) clusters. To compute the free energy pathways for the electrochemical conversion of CO₂ to formic acid (CO₂ + 2H⁺ + 2e⁻ → HCOOH) and carbon monoxide (CO₂ + 2H⁺ + 2e⁻ → CO + H₂O), we started from the most stable optimized structure of adsorbed CO₂* and from it generated and optimised the structure of OCHO*, COOH* and CO* on the nanoclusters. The structures of all atomistic models generated in this study are provided in Section S2 of the ESI.†

3. Results and discussion

3.1 Structure, stability, and electronic properties of Cu–Sn clusters

The structures and relative energies of the low energy isomers of the tetrahedral Cu_4−nSn_n (n = 0–4) clusters are reported in Fig. 1. Since the thermal energy is k_BT = 0.025 eV at T = 298.15 K, where k_B is the Boltzmann constant, we expect that for each cluster more than one isomer may coexist at room temperature if their energies are within the 0.25 eV energy range. According to Fig. 1, except for Cu₃Sn, the other bimetallic Cu–Sn clusters and pure Cu₄ and Sn₄ exist as one isomer at room temperature because the energy difference between the most stable isomer and its next higher energy isomer is more than 0.25 eV. The ground state of Cu₄ is a two-dimensional structure with D_2h symmetry, in agreement with previous studies (Fig. 1a).^7,20,46 The next higher energy isomer of Cu₄ is also a 2D structure with a planar-triangular geometry and C_s symmetry, which is 0.4 eV higher in energy than the lowest energy isomer. The bimetallic Cu₃Sn cluster has three isomers with similar energy: the most stable is a distorted three-dimensional (3D) structure with C_s symmetry, followed by an isomer of tetrahedral geometry (0.06 eV), and an isomer where the planarity of the Cu₄ cluster is preserved (C_2v symmetry, 0.17 eV) (Fig. 1b). For Cu₂Sn₂, the distorted 3D (C_2v) and tetrahedral (D_2h) structures were found to be the most stable isomers but in this bimetallic cluster the energy difference between them is large (0.83 eV) (Fig. 1c). For CuSn₃, the ground-state 2D structure with the Sn atoms forms trigonal geometry with the Cu atom at the lateral position, the second most stable isomer is a 3D pyramidal structure, and the least stable one has a 2D rhombus-like geometry (Fig. 1d). Like Cu₄, the most stable isomer of the Sn₄ cluster has a two-dimensional structure with D_2h symmetry (Fig. 1e). Overall, the four atom clusters of the ground-states of monometallic Cu₄ and Sn₄ have a 2D planar rhombus-like geometry, whereas the mixed Cu–Sn clusters tend to form distorted 3D, tetrahedral, and quasi-planar geometries.

Fig. 1 The lowest energy isomers of the four-atom Cu_4−nSn_n (n = 0–4) clusters: (a) Cu₄; (b) Cu₃Sn; (c) Cu₂Sn₂; (d) CuSn₃; (e) Sn₄. The symmetry of the structures is given at the bottom of each isomer. The values within the parenthesis are the relative energies (in eV) of the cluster relative to the most stable isomer. Blue and grey balls represent Cu and Sn atoms, respectively.

To gain insights into the relative stabilities of pure and bimetallic four-atom systems, we computed the binding energy per atom (E_B), here defined as

where E(Cu_4−nSn_n) is the total energy of the most stable isomer of each Cu_4−nSn_n (n = 0–4) cluster, and E(Cu) and E(Sn) are the total energies of Cu and Sn atoms, respectively. A higher positive value of E_B indicates higher thermodynamic stability of the cluster. As the variation of binding energy does not always provide a clear picture of the relative stability of the nanoclusters, we have also employed the second-order difference in energy (Δ₂E):

Δ₂E(Cu_nSn_m) = E(Cu_n+1Sn_m−1) + E(Cu_n−1Sn_m+1) − 2(Cu_nSn_m) (4)

The Δ₂E index assesses the stability of a cluster, Cu_nSn_m, compared with its neighboring structures, Cu_n+1Sn_m−1 and Cu_n−1Sn_m+1. A higher value of Δ₂E signifies a greater stability for the cluster as compared to its nearest neighbors. Table 1 reports the values of the energy descriptors E_B and Δ₂E together with a list of electronic properties of the clusters: the energy difference between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) (HOMO–LUMO gap, Δ_H–L), the Bader charges (Q_B) of the Cu and Sn atoms, and the average interatomic distance between nearest neighbors. According to Table 1, the two isomers of Cu₄ have the lowest binding energies (1.42 eV for D_2h and 1.32 eV for C_s) out of all four-atom clusters. The value of E_B increases with the incorporation of more Sn atoms, with the maximum value (2.14 eV) computed for the monometallic Sn₄ cluster. Assessing from the binding energies, the stability of the four-atom bimetallic Cu_4−nSn_n is more than that of pure Cu₄ and it is the highest for Cu₂Sn₂ (1.92 eV). From the results of second-order difference in the energy, Δ₂E, we observe that among the three bimetallic Cu–Sn clusters considered here, the lowest energy isomer of Cu₂Sn₂ exhibits greater stability than the respective isomers of Cu₃Sn and CuSn₃. In summary, the Cu₄ cluster is thermodynamically the least stable. However, the cluster stability is strongly affected by its composition: the incorporation of Sn stabilizes the Cu–Sn system compared to the pure copper cluster.

Table 1 Binding energy per atom (E_B), second-order energy difference (Δ₂E), HOMO–LUMO gap (Δ_H–L), Bader charge (Q_B), and average distance (d) between atoms

System	Symmetry	E B (eV)	Δ2E (eV)	ΔH–L (eV)	Q B(Cu) (e)	Q B(Sn) (e)	d Cu–Cu (Å)	d Cu–Sn (Å)	d Sn–Sn (Å)
Cu4	D 2h	1.42	—	0.94	—	—	2.34	—	—
	C s	1.32	—	0.96	—	—	2.32	—	—
Cu3Sn1	C s	1.68	0.08	1.33	−0.67	2.01	2.8	2.53	—
	C 3v	1.66	—	1.71	−0.64	1.91	2.43	2.53	—
	C 2v	1.64	—	1.67	−0.61	1.84	2.33	2.54	—
Cu2Sn2	C 2v	1.92	1.01	0.97	−0.92	0.92	2.49	2.6	2.88
	D 2h	1.71	—	0.91	−1.68	1.68	3.94	2.52	3.14
Cu1Sn3	C s	1.91	−0.97	1.94	−1.81	0.60	—	2.53	2.84
	C s	1.84	—	1.29	−1.38	0.46	—	2.63	3.04
	C 2v	1.81	—	1.33	−0.69	0.23	—	2.62	2.78
Sn4	D 2h	2.14	—	1.03	—	—	—	—	2.92
	D 2d	1.93	—	1.62	—	—	—	—	3.27

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From Table 1, the stability correlates with the average interatomic distances in the cluster. The average Cu–Cu distance of the lowest energy isomer is 2.34 Å, in good agreement with other DFT plane wave studies of Cu clusters (2.36 Å).⁴⁷ This average interatomic distance increases to 2.92 Å in Sn₄ because of the different atomic radii of Cu (1.28 Å) and Sn (1.41 Å). In the bimetallic clusters, the trend is an increase of Cu–Cu and a decrease of Sn–Sn, while Cu–Sn distances lie between the Cu–Cu and Sn–Sn distances. We also conducted a Bader charge (Q_B) analysis to determine the charge distribution in the Cu–Sn clusters.^48–50 In Cu₃Sn, Cu₂Sn₂ and CuSn₃, the copper and tin atoms have negative and positive Bader charges, respectively (see Table 1). Consequently, during the reduction reaction, the electron transfer process will occur from the electron-rich Cu atoms to the C atom of CO₂, which is in its highest oxidation state. A descriptor to analyze the global reactivity is the gap energy, Δ_H–L. This property relates to the energy cost for an electron to jump from the HOMO to the LUMO orbital and, therefore, characterizes the chemical stability of the system, with a higher value of Δ_H–L corresponding to a more chemically stable (less reactive) cluster. If we compare the values of Δ_H–L of the most stable isomers of each monometallic and bimetallic cluster, Cu₄ has the lowest HOMO–LUMO gap (0.94 eV). This value increases substantially for Cu₃Sn (1.33 eV) and CuSn₃ (1.94 eV) but is similar in Cu₂Sn₂ (0.97 eV). In summary, from the energy descriptors E_B and Δ₂E and the electronic descriptor Δ_H–L, Cu₂Sn₂ is the most stable cluster and maintains the reactivity of the pure copper cluster.

3.2 The adsorption and activation of CO₂

The activation of CO₂ is a result of the electronic charge transfer from the HOMO of the cluster to the LUMO of the CO₂ molecule.⁵¹ To investigate the direction of charge migration between the clusters and CO₂, we have compared in Fig. 2 the energies of the HOMO and LUMO of the most stable isomers of Cu–Sn with those of gas-phase CO₂. For all clusters, the energy difference between the HOMO of CO₂ and the LUMO of the clusters (dark gray dashed line) is higher than the energy difference between the HOMO of the clusters and the LUMO of CO₂ (blue dashed line). For example, the energy difference between the HOMO of Cu₄ and the LUMO of CO₂ is 3.38 eV, while the energy difference between the HOMO of CO₂ and the LUMO of Cu₄ is 5.68 eV. Similarly, the energy difference between the HOMO of monometallic Sn₄ and the LUMO of CO₂ is 3.52 eV, while the energy difference between the HOMO of CO₂ and the LUMO of monometallic Sn₄ is 5.62 eV. The charge migration from clusters to the CO₂ molecule is favorable, which results in radical anion CO₂^−δ formation upon CO₂ adsorption.

Fig. 2 The HOMO (black line) and LUMO (red line) energy levels of monometallic and bimetallic tetrahedral Cu–Sn clusters compared to that of CO₂. The dark grey dashed line represents the energy difference between the HOMO of CO₂ and the LUMO of the clusters. The blue dashed line is the energy difference between the HOMO of the clusters and the LUMO of CO₂.

The first step in the catalytic conversion of CO₂ is the adsorption process, in which the molecule can physisorb or chemisorb to the catalyst. In the physisorbed state, the CO₂ molecule maintains the C–O bond length (1.18 Å) and the O–C–O bond angle (180°) of the gas phase molecule. In contrast, in the chemisorbed state, the CO₂ molecule shows elongated C–O bonds and a decreased O=C=O bond angle (linear to bent mode), which corresponds to the activation of CO₂ because of the charge transferred from the metal catalyst to the π* molecular orbitals of the CO₂ molecule.⁵²Fig. 3 displays the physisorption and chemisorption of CO₂ on monometallic and bimetallic four-atom Cu_4−nSn_n (n = 0–4) clusters. When physisorbed, CO₂ prefers “top” coordination with the O interacting with the Cu atom. When chemisorbed, CO₂ coordinates at the bridge site between the electron-rich Cu and the electron-deficient Sn (Table 1) leading to the formation of Cu–C and Sn–O bonds. From Fig. 3, we also note that on the pure tin cluster, Sn₄, we could only locate a weakly physisorbed CO₂ state. We have further characterised in Table 2 the interaction and activation of CO₂ on the Cu_4−nSn_n (n = 0–4) clusters relative to the properties of the CO₂ molecule in gas phase using the following energetic, structural, and electronic indicators: adsorption energy (ΔE_CO2); C–O elongation (d_C–O); O–C–O angle bending (θ_OCO); and charge transfer from the cluster to CO₂ (ΔQ). The adsorption energy (ΔE_CO2) was computed according to the following expression:

ΔE_CO2 = E[(Cu_4−nSn_n)⋯CO₂] − E(CO₂) − E(Cu_4−nSn_n) (5)

where the first term is the energy of the cluster–CO₂ complex, and the second and third terms are the energies of the isolated CO₂ molecule and the pristine cluster, respectively. Here, a negative value of the adsorption energy indicates a favorable interaction between CO₂ and the metal cluster.

Fig. 3 Lowest energy structures of CO₂ physisorbed and chemisorbed on monometallic and bimetallic Cu–Sn clusters. Blue, grey, brown, and red balls represent Cu, Sn, C and O atoms, respectively.

Table 2 Adsorption energy (ΔE_CO2), C–O bond length (d_C–O), and bond angle (θ_OCO) of CO₂, and charge transfer from the catalyst to CO₂ using Bader analysis (ΔQ) when physisorbed and chemisorbed on the Cu_4−nSn_n (n = 0–4) clusters. For comparison, the bond length and the bond angle of the CO₂ molecule in the gas phase are 1.18 Å and 180 degrees, respectively

System	Physisorption				Chemisorption
	ΔECO2 (eV)	d C–O (Å)	θ OCO (°)	ΔQ (e)	ΔECO2 (eV)	d C–O (Å)	θ OCO (°)	ΔQ (e)
Cu4	−0.32	1.18, 1.17	179.7	−0.01	−0.53	1.26, 1.22	140	−0.51
Cu3Sn	−0.32	1.18,1.17	179.5	−0.03	−0.21	1.23,1.24	142.6	−0.52
Cu2Sn2	−0.14	1.18, 1.17	179.3	−0.02	0.50	1.28, 1.22	132.7	−0.64
CuSn3	−0.18	1.19, 1.17	179.6	−0.01	0.40	1.24, 1.27	136.9	−0.70
Sn4	−0.16	1.18, 1.80	179.9	0.00	—	—	—	—

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The results in Table 2 confirm that when CO₂ chemisorbs to the cluster, there is a significant elongation of the C–O bond, loss of linearity of CO₂, and charge transfer, which does not occur when CO₂ is physisorbed. For the chemisorbed state, the inclusion of Sn leads to a more pronounced bending of the CO₂ molecule (140.0° for Cu, 142.6° for Cu₃Sn, 132.7° for Cu₂Sn₂, and 136.9° for Cu₃Sn) and charge transfer compared to pure Cu (−0.51e for Cu₄, −0.52 for Cu₃Sn, −0.64e for Cu₂Sn₂, and −0.70e for CuSn₃). However, there is a counterintuitive behaviour in the inverse relationship between the charge transferred to CO₂ and the adsorption energy: a higher charge transfer from the Cu–Sn cluster to CO₂ does not correspond to a favourable adsorption energy. As shown in Table 2, the interaction of CO₂ on Cu₄ and Cu₃Sn is favourable for both chemisorption and physisorption (ΔE_CO2 < 0 in Table 2). On the other hand, for the other Cu–Sn clusters, the CO₂ chemisorption (ΔE_CO2 > 0) is less stable than physisorption (ΔE_CO2 < 0). The higher activation of CO₂ in Sn-bearing clusters (charge transfer and bending) does not correspond to a favourable interaction between the CO₂ and the catalyst, as previously shown in tetrahedral Cu–Pd, Cu–Ni, and Cu–Zr clusters.^20,21 For the Cu₃Sn system, we have considered the CO₂ adsorption on the three low-energy isomers in Fig. 1(b) because their energy difference is less than 0.2 eV but, upon optimization, the C_2v and C_3v isomers of Cu₃Sn rearranged to the C_s isomer (see Fig. S5 in the ESI†). The structures of the Cu₃Sn···CO₂ complex in Fig. 2(b) and the values in Table 2 refer to the lowest energy adsorption site of CO₂. In comparison, previous DFT calculations of CO₂ adsorption on other four-atom Cu clusters doped with Pd, Ni, Pt and Zr reported values of ΔE_CO2 (up to −1 eV) corresponding to more favorable adsorption and Bader charge transfer (up to 1e) compared to the Cu–Sn clusters (Table S3 of the ESI†).^18–20 CO₂ is adsorbed weakly on the Cu–Sn clusters compared to Cu–Pd, Cu–Ni, Cu–Pt and Cu–Zr. However, the values of θ_OCO and d_CO are close to those found for Cu–Pd, Cu–Ni, Cu–Pt and Cu–Zr (Table S3, ESI†). Therefore, adsorption energy is not always a good indicator of CO₂ activation.

The adsorption energy, bond length, bond angle, and the overall charge of the CO₂ molecule physisorbed on graphene and γ-Al₂O₃ supported Cu_4−nSn_n (n = 0–4) clusters are reported in Table S4 of the ESI.† For most clusters on graphene and γ-Al₂O₃, the physisorption of CO₂ has favourable adsorption energy with a small charge transfer, between −0.01e and −0.03e, to the CO₂ molecule. However, for CuSn₃/graphene and Cu₃Sn/γ-Al₂O₃, CO₂ is in the chemisorbed state, as shown by the significant bending of CO₂ and elongation of C–O (Fig. S3 and S4 of the ESI†). The Bader charge transfer for the chemisorbed CO₂ is between −1.0 and −1.6e, much higher than that for the unsupported clusters reported in Table 2 (−0.5 to −0.7e). However, the chemisorption of CO₂ is not favorable in CuSn₃/graphene, as evidenced by the positive adsorption energy of 0.34 eV. Similarly, for Cu₃Sn/γ-Al₂O₃, the chemisorption of CO₂ seems to be favorable with a high Bader charge transferred to CO₂.

3.3 CO₂ direct dissociation over Cu_4−nSn_n clusters

The favorable chemisorption on monometallic Cu₄ compared to Cu–Sn clusters motivated us to determine if the activation of CO₂ (chemisorption) affects the kinetic barrier for breaking the C–O bond. To model the gas-phase CO₂ dissociation on the Cu_4−nSn_n (n = 0–4) clusters we used the following reaction: CO₂* → CO* + O*. To this end, we performed transition state search calculations to find the energy barrier to break one of the C–O bonds of the CO₂ molecule. The starting point was the most stable structure of adsorbed CO₂. The final product was modelled by choosing the most elongated C–O bond of the adsorbed CO₂, systematically increasing the bond distance to nearly 3.0 Å and optimizing the resulting structure.²¹ The relative energies of the reactant complexes (CO₂*), transition state (TS) and product complexes (CO* + O*) are reported in Fig. 4 together with the structures of these intermediates for Cu₄. The optimized structures of the initial state, transition state, and final state involved in CO₂ direct dissociation to CO over the other clusters are reported in Fig. S1 of the ESI.† Activation barriers of forward (E_a,f) and backward (E_a,b) reactions, the reaction energy (ΔE) measuring the energy difference between the reactant (CO₂*) and the product (CO* + O*), and the C–O bond distance of the adsorbed CO fragment in the final dissociated product (d_CO) are summarized in Table 3. The energy barrier to dissociate adsorbed CO₂ and form the CO* and O* products is between 2.2 and 3.1 eV. Specifically, the activation energy for the dissociation of CO₂ on Cu₄ is 2.3 eV, which is close to the value of 2.4 eV obtained by Mondal and co-workers using the B3LYP functional with the Stuttgart pseudopotential basis set (SDD) for Cu and the triple-zeta basis set for C and O.²¹ However, another DFT study using the PBE0 functional with the SDD basis set for all atoms gave a much lower barrier of 1.8 eV.²⁰ The double zeta basis set in these calculations could cause such discrepancy, not observed in our case because of the use in this study of delocalized plane waves to describe all atoms. As the reaction energy over Cu₄ is close to zero, the direct gas-phase CO₂ dissociation could thermodynamically occur on this cluster, but the process is kinetically limited. The results in Table 3 also suggest that except for Cu₂Sn₂, the energetics of the CO₂ direct dissociation reaction are not significantly affected by the incorporation of Sn. The activation barrier for Cu₃Sn is similar to that of monometallic Cu₄ (see Fig. 4). It is the highest for Cu₂Sn₂ (3.1 eV), and the lowest for CuSn₃ (2.18 eV). The formation of Cu–Sn bonds does not lower the activation energy compared to the monometallic clusters, leading to values for the kinetic barriers close to that found for Cu₄, suggesting a lack of synergy between copper and tin atoms to modulate activation barriers of the gas-phase CO₂ direct dissociation. In comparison, other reports of four-atom copper-based clusters showed modulation of the activation barriers (higher or lower values of ΔE and E_a,f) depending on the cluster composition. For Cu–Zr, the activation barrier was found to be between 0.13 and 0.52 eV, with Cu₃Zr having the lowest activation energy but also strong adsorption (−3.1 eV) of the final product (*CO and O*), which could lead to poisoning of the catalyst surface.²¹ The CO₂ direct dissociation on Cu–Pd clusters had an activation barrier of 1.1 to 2.8 eV, values which are generally lower than those for Cu–Sn (Table 3). The activation barriers of the direct CO₂ dissociation on the Cu–Sn clusters are also higher than those reported for four-atom Pt–Ni clusters.⁵³

Fig. 4 Reaction pathway for CO₂ dissociation into CO* and O* over Cu₄, Cu₃Sn, Cu₂Sn₂, and CuSn₃ clusters. The structures are those of the chemisorbed CO₂, transition state, and (CO* + O*) on Cu₄. Blue, brown, and red balls represent metal Cu, C and O atoms, respectively.

Table 3 Adsorption energy (E_A), forward (E_a,f) and backward (E_a,b) activation barriers, reaction energy (ΔE) of the CO₂ direct dissociation (CO₂* → CO* + O*), and C–O bond distance (d_CO) of the adsorbed CO fragment in the final dissociated product for CO₂ dissociation over Cu_4−nSn_n (n = 0–4)

System	E A (eV)	E a,f (eV)	E a,b (eV)	ΔE (eV)	d CO (Å)
Cu4	−0.53	2.30	2.30	0.00	1.16
Cu3Sn1	−0.32	2.30	2.09	0.21	1.16
Cu2Sn2	−0.14	3.14	2.32	0.82	1.15
Cu1Sn3	−0.18	2.18	2.10	0.08	1.15

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The adsorption energy of CO₂ on these clusters is significantly lower than the dissociation barrier for the cleavage of the C–O bond, suggesting that the adsorption of CO₂ will be more favorable than the direct dissociation of CO₂. The positive values of the reaction energy ΔE, which is the energy difference between the reaction and the final products, suggest that for Cu₂Sn₂ and Cu₃Sn, the dissociation of CO₂ to CO + O is endothermic. The highly positive ΔE of 0.82 for Cu₂Sn₂ compared to other clusters indicates that this would have an unstable final product. In summary, the gas-phase CO₂ direct dissociation, CO₂* → CO* + O*, is kinetically and thermodynamically unfavourable on Cu–Sn clusters and unlikely to occur.

3.4 Mechanisms of reduction reactions on Cu–Sn clusters

3.4.1 Electrochemical CO₂ reduction to carbon monoxide and formic acid. In this section, we present calculations of the mechanism of the electrochemical CO₂ reduction (eCO₂R) to CO and HCOOH catalyzed by the Cu_4−nSn_n clusters. We have also considered the effects of two supports, graphene and partially hydroxylated γ-Al₂O₃ (110) surface. The concerted proton–electron transfer (CPET) steps controlling the eCO₂R to HCOOH and CO are shown in Fig. 5. After the adsorption of CO₂* to the catalyst surface, the first CPET step leads to two possible intermediates, OCHO* or COOH*, depending on the atom coordinated to the catalyst: O or C. The second CPET will then form HCOOH or CO accordingly. We have computed the free energy of reactions (ΔG) of the elementary steps leading to the formation of HCOOH and CO. For HCOOH, these steps are the CPET to convert adsorbed CO₂* to an O-coordinated intermediate (CO₂* + H⁺ + e⁻ → OCHO*, ΔG_OCHO*) and the next CPET converts adsorbed *OCHO to gas phase formic acid (OCHO* + H⁺ + e⁻ → HCOOH, ΔG_HCOOH). For CO, the elementary steps are the CPET to convert adsorbed CO₂* to a C-coordinated intermediate (CO₂* + H⁺ + e⁻ → COOH*, ΔG_COOH*), and the next CPET converts adsorbed *COOH to adsorbed carbon monoxide (COOH* + H⁺ + e⁻ → CO* + H₂O, ΔG_CO*), which can then be released from the catalyst surface to form gas-phase CO (ΔG_CO).

Fig. 5 Reaction pathways for the two proton/two electron (2H⁺/2e⁻) CO₂ reduction reaction to carbon monoxide (CO) and formic acid (HCOOH) showing the optimised structures of the intermediates on Cu₄. Blue, brown, red and white balls represent metal Cu, C, O and H atoms, respectively.

The Gibbs free energy diagrams of the eCO₂R reaction on the Cu_4−nSn_n (n = 0–4) clusters in Fig. 6(a–d) show that the CPET steps controlling the distribution of the HCOOH and CO products are significantly affected by the cluster composition and the type of support. Graphene-supported Cu–Sn clusters give lower energetic pathways for CO and HCOOH compared to γ-Al₂O₃. The optimized structures of the intermediates involved in the eCO₂R on the isolated and supported Cu₄ clusters are shown in Fig. 7. The Gibbs free energy diagram for the eCO₂R pathways to HCOOH and CO on each Cu–Sn cluster is discussed in detail below. The optimized structures of the intermediates CO₂*, OCHO*, COOH*, and CO* adsorbed on Cu₄, Cu₄/graphene, and Cu₄/γ-Al₂O₃ are shown in Fig. 7. The intermediates over the other Cu–Sn clusters are reported in Fig. S2 and S4 of the ESI.†

Fig. 6 Gibbs free energy diagram for the eCO₂R pathways to HCOO and CO on the (a) Cu₄, (b) Cu₃Sn, (c) Cu₂Sn₂, and (d) CuSn₃ clusters. Reduction pathways on isolated Cu_4−nSn_n (n = 1–3) clusters (light purple) and clusters supported on graphene (blue) and on the partially hydroxylated (110) surface of γ-Al₂O₃ (orange).

Fig. 7 Optimized structures of the CO₂*, OCHO*, COOH*, and CO* intermediates on (a) Cu₄, (b) Cu₄/graphene, and (c) Cu₄/γ-Al₂O₃ (110). Blue, grey, brown, red and white balls represent Cu, Al, C, O and H atoms, respectively.

Cu₄. According to Fig. 6(a), the adsorption of CO₂ is favorable on Cu₄ and Cu₄/graphene but not on Cu₄/γ-Al₂O₃ (ΔG_CO2* = 0.6 eV). On Cu₄, the eCO₂R is selective towards HCOOH because of the lower free energy of the *OCHO intermediate (ΔG_OCHO* = 0.7 eV) compared to *COOH (ΔG_COOH* = 1.0 eV). On Cu₄/γ-Al₂O₃, the intermediate OCHO* binds strongly to the catalyst (ΔG_OCHO* = −1.8 eV) compared to COOH* (ΔG_COOH* = −0.9 eV). Consequently, the subsequent CPET step favors CO formation as it is easier to release CO as a product from the catalyst than HCOOH. For Cu₄/graphene, the selectivity is towards CO formation as it is easier to form *COOH (ΔG_COOH* = −0.4 eV) than *OCHO (ΔG_OCHO* = 0.5 eV). In addition, the next CPET step from *COOH to *CO is also favorable (ΔG_CO* = −0.2 eV). In summary, while the isolated Cu₄ is selective towards HCOOH, the effect of both graphene and γ-Al₂O₃ (110) is to drive the reaction towards CO formation. However, the strong binding of OCHO* on Cu₄/γ-Al₂O₃ could poison the eCO₂R on this catalyst.
Cu₃Sn. In Fig. 6(b), the CPET steps leading to the formation of *OCHO (ΔG_OCHO* = −0.9 eV) and *COOH (ΔG_COOH* = −0.9 eV) on the most stable isomer of Cu₃Sn (C_s) are both exergonic and the subsequent steps to generate HCOOH (ΔG_HCOOH = 1.4 eV) and CO (ΔG_CO* = 1.1 eV) are highly endothermic. The Gibbs free energy diagrams for the other two isomers of Cu₃Sn are given in Fig. S6 (ESI†). On Cu₃Sn/γ-Al₂O₃, the strong binding of the *OCHO intermediate makes the formation of HCOOH (ΔG_HCOOH = 1.9 eV) highly unfavourable compared to *CO (ΔG_CO* = 0.8 eV). The subsequent step to release CO is exergonic (ΔG_CO(g) = −0.5 eV). Unlike Cu₃Sn and Cu₃Sn/γ-Al₂O₃, on graphene the energetics for the reaction steps leading to HCOOH and CO are similar. The formation of *OCHO (ΔG_OCHO* = 0.2 eV) is slightly favored compared to *COOH (ΔG_COOH* = 0.3 eV). However, the next step from *OCHO to HCOOH(g) has higher positive free energy (ΔG_HCOOH(g) = 0.6 eV) relative to the formation of CO* (ΔG_CO* = 0.3 eV), whose desorption energy from the surface (ΔG_CO(g)) is −0.2 eV.
Cu₂Sn₂. For the Cu₂Sn₂ system, the *OCHO intermediate (ΔG_OCHO* = −0.2 eV) is more stable than *COOH (ΔG_COOH* = 0.3 eV), but, overall, the energy pathway of CO formation (ΔG_CO* = −0.2 eV) is lower than that of HCOOH(g) formation (ΔG_HCOOH(g) = 0.8 eV). This Cu–Sn cluster is, therefore, selective towards CO. The support effect is destabilizing both *OCHO and *COOH intermediates. On Cu₂Sn₂/graphene, the *OCHO formation (ΔG_OCHO* = 0.2 eV) is more favorable than *COOH formation (ΔG_COOH* = 0.7 eV) and the overall selectivity is towards HCOOH compared to CO as it has the lowest energetic pathway. On the Cu₂Sn₂/γ-Al₂O₃ system, the first CPET steps to form COOH* (ΔG_COOH* = 1.2 eV) and OCHO* (ΔG_OCHO* = 0.8 eV) are both highly unfavorable. The overall product selectivity is towards HCOOH relative to CO, as evidenced by the overpotential for HCOOH (0.44 eV) being less than that for CO (1.07 eV) formation.
CuSn₃. For gas-phase CuSn₃, the electrochemical CO₂-to-CO conversion is highly unfavourable because of the strong CO* adsorption (ΔG_CO* = 2.2 eV). In comparison, the highest free energy of the reaction step for the HCOOH pathway is ΔG_HCOOH(g) = 0.9 eV. The CuSn₃ cluster is, therefore, selective towards the formation of HCOOH. In the Cu₃Sn/γ-Al₂O₃ system, the *OCHO formation (ΔG_OCHO* = −0.5 eV) is more favourable than COOH* formation (ΔG_COOH* = −0.1 eV). The subsequent steps from *COOH to *CO (ΔG_CO* = 1.4 eV) and from *OCHO to HCOOH (ΔG_HCOOH = 1.2 eV) are unfavourable. The HCOOH reaction pathway is lower compared to CO in this system. For the CuSn₃/graphene system, the *OCHO intermediate (ΔG_OCHO* = −0.7 eV) is more favourable than *COOH (ΔG_COOH* = −0.3 eV). The subsequent steps for both initial intermediates have a positive Gibbs free energy (ΔG_CO* = 0.8 eV and ΔG_HCOOH(g) = 1.0 eV). Moreover, the limiting potential for the electrochemical pathways shows CO (0.83 V) to be slightly lower than HCOOH (0.95 V).

The correlation of the Bader charge of Cu of supported clusters with the overpotential for the eCO₂R reactions to HCOOH and CO is reported in Fig. 8. For both reaction pathways, the greater the negative charge on Cu, the higher the overpotential. The only exceptions are Cu₄ and Cu₃Sn on γ-Al₂O₃. Since the site for CO₂ adsorption on the clusters is always the Cu atom (see Fig. S4, ESI†), a higher charge transferred to the Cu atoms will lead to stronger binding of the intermediates. However, there is a difference in the charge distribution on the catalyst depending on the support. For the Cu–Sn clusters on γ-Al₂O₃, the Bader charge of Cu is higher compared to the case when the support is graphene. This behavior correlates with a higher overpotential for the reactions on Cu–Sn/γ-Al₂O₃ (vide infra). Overall, the cluster composition and support influence the reaction energetics and hence the final product selectivity. This shows the importance of tuning the properties of the active sites to tune the binding of the intermediates to control the selective formation of a specific product.

Fig. 8 Overpotential for CO and HCOOH formation as a function of the Bader charge, Q^b, of Cu in the Cu–Sn clusters supported on graphene (blue) and γ-Al₂O₃ (orange). The dashed black lines in both figures serve as a guide to the eye.

3.4.2 Hydrogen evolution reduction. Since eCO₂R occurs in aqueous solutions, there is always a competition between CO₂R and HER (H⁺ + e⁻ → 1/2 H₂). The faradaic efficiency of HER is usually higher than that of CO₂R due to its much lower overpotential on most metallic catalysts. It was proposed that Sn could weaken the binding strength of H suppressing the HER.¹⁴ Therefore, we have also computed the free energy of H adsorption (ΔG_H*) on the Cu and Cu–Sn clusters to determine the role of Sn incorporation in the selectivity towards the CO₂R rather than the HER. In Fig. 9(a), for the isolated Cu and Cu–Sn clusters the H adsorption is favorable on Cu₄ (ΔG_H* = −0.19 eV), Cu₃Sn (−0.45 eV), and CuSn₃ (−0.21 eV). In comparison, the value of ΔG_H* computed at the DFT-PBE level of theory for the copper (100) surface is −0.21 eV.⁵⁴ On the other hand, Cu₂Sn₂ has the highest Gibbs free energy of H adsorption (0.55 eV). The order from least to most favorable for the HER is Cu₂Sn₂ > Cu₄ > CuSn₃ > Cu₃Sn. For the clusters supported on graphene, Fig. 9(b), Cu₂Sn₂ has, again, a positive free energy (ΔG_H* = 0.50 eV), while the H adsorption is favorable on Cu₄ (ΔG_H* = − 0.60 eV), Cu₃Sn (ΔG_H* = −0.30 eV) and CuSn₃ (ΔG_H* = −0.18 eV). The order from least to most favorable for the HER is Cu₂Sn₂/graphene > CuSn₃/graphene > Cu₃Sn/graphene > Cu₄/graphene. Similarly, for the clusters supported on γ-Al₂O₃, Fig. 9(c), Cu₂Sn₂ has the highest Gibbs free energy for H adsorption (ΔG_H* = 0.66 eV). The H adsorption is favorable on Cu₄ (ΔG_H* = −0.07 eV), Cu₃Sn (ΔG_H* = −0.20 eV), and CuSn₃ (ΔG_H* = −0.72 eV). The order from least to most favorable for the HER is Cu₂Sn₂/γ-Al₂O₃ > Cu₄/γ-Al₂O₃ > Cu₃Sn/γ-Al₂O₃ > CuSn₃/γ-Al₂O₃. Overall, for both gas-phase and supported clusters, Cu₂Sn₂ is highly unfavorable towards HER.

Fig. 9 Gibbs free energy diagram for the HER pathways to HCOO and CO on the (a) Cu₄, (b) Cu₃Sn, (c) Cu₂Sn₂, and (d) CuSn₃ clusters. Reduction pathways on isolated Cu_4−nSn_n (n = 1–3) clusters (light purple) and clusters supported on graphene (blue) and on the partially hydroxylated (110) surface of γ-Al₂O₃ (orange).

3.4.3 Selectivity. The limiting potential (U_L) and the overpotential (η) are important factors for evaluating the catalytic activity of eCO₂R electrocatalysts.⁵⁵ The limiting potential is given by the formula

U_L = −ΔG_max/ne (6)

where ΔG_max is the free energy change of the rate-determining step at U = 0 V, n is the number of electrons transferred, and e is the electron charge. The overpotential (η) can then be obtained from the difference between the equilibrium potential (U_eq) and the limiting potential (U_L), which represents the minimum applied potential required to facilitate the formation of relevant intermediates:

η = U_eq − U_L (7)

The magnitudes of the overpotential of the Cu–Sn catalysts for the eCO₂R to CO, HCOOH and H₂ are summarized in Fig. 10(a–c). Poor catalytic performance towards a specific reaction is associated with a larger overpotential, and vice versa.

Fig. 10 Overpotential for the electrocatalytic formation of CO, HCOOH and H₂ on (b) isolated Cu_nSn_2-n, (b) Cu_nSn_2-n/graphene, and (c) γ-Al₂O₃.

The eCO₂R on the isolated Cu₂Sn₂ cluster displays high selectivity towards CO since it has low overpotential (high catalytic activity) for CO formation (0.14 V) and high overpotential for H₂ (0.55 V) and HCOOH (0.48 V) formation (low catalytic activity). The Cu₂Sn₂ catalyst could significantly enhance carbon monoxide selectivity because it is both less selective to formic acid and suppresses HER. CO is the key intermediate in the mechanism of eCO₂R on copper-based catalysts for the generation of C₁ and C₂₊ value-added chemicals such as methane, methanol, ethanol, ethylene, and others.³ On graphene, however, Cu₂Sn₂ has the lowest overpotential for HCOOH (0.06 V) compared to the relatively high overpotential for CO (0.61 V) and H₂ (0.50 V) formation and, consequently, Cu₂Sn₂ is selective towards formic acid. Of the γ-Al₂O₃ supported clusters, Cu₂Sn₂ has the lowest overpotential for HCOOH (0.44 V). However, it is more susceptible to the HER, as the overpotential for H₂ formation is only slightly higher (0.66 V). The other clusters show negative overpotentials for the HER and therefore are unlikely to be selective towards the eCO₂R reaction. Overall, these results show the combined effects of the composition of the cluster and its supporting material on the selectivity towards CO, HCOOH or H₂.

4. Conclusions

In this work, we conducted density functional theory calculations to characterize the properties of pristine Cu_4−nSn_n (n = 0–4) clusters and their ability to absorb, activate, and convert CO₂ to carbon monoxide (CO) and formic acid (HCOOH). We have divided the discussion of the calculations into four sections: (a) structure, stability, and electronic properties of Cu_4−nSn_n clusters; (b) CO₂ binding and activation over the most stable systems; (c) kinetics of the gas phase CO₂ direct dissociation on Cu_4−nSn_n clusters to generate CO; and (d) liquid-phase electrochemical CO₂ reduction reactions to CO and HCOOH on Cu_4−nSn_n clusters and graphene and γ-Al₂O₃ supported Cu–Sn clusters and their competition with the hydrogen evolution reaction. The energy descriptors, binding energy per atom and the second-order difference in energy, identify Cu₂Sn₂ as the most stable cluster. The electronic energy descriptor HOMO–LUMO gap also suggests that the reactivity of the pure copper cluster, Cu₄, is maintained for Cu₂Sn₂ compared to the other Cu–Sn systems. Moreover, Cu₂Sn₂ activates CO₂ as shown by pronounced bending of the linear O=C=O molecule. Calculations of the free energies for CO₂ reduction pathways to HCOOH and CO show that the isolated Cu₂Sn₂ system has the highest potential as a stable and selective catalyst for the electrochemical CO₂ conversion to CO. But when supported on γ-Al₂O₃ and graphene, the electrochemical CO₂ reduction on Cu₂Sn₂ favors HCOOH formation, particularly over the Cu₂Sn₂/graphene catalyst. In addition, Cu₂Sn₂ suppresses hydrogen evolution reaction. We can conclude that the isolated Cu₂Sn₂ system has the highest potential as a stable and selective catalyst for the electrochemical CO₂ conversion to CO. When supported on graphene and γ-Al₂O₃ (110) surfaces, Cu₂Sn₂ can selectively generate HCOOH.

Author contributions

Conceptualisation of work: AM, AGN and DDT; conduction of computations: AM and QZ; data analyses: AM and AGN, AR; data dissemination & graphics: AM and DDT; writing of the manuscript: AM, AGN and DDT; project support: AR, DDT.

Data availability

Raw data were generated at Queen Mary's Apocrita HPC facility. Derived data supporting the findings of this study are available from the corresponding author (DDT) on request.

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements

A.G.N acknowledges the Pakistan HEC-QMUL PhD Scholarships for funding. Q.Z. thanks the China Scholarship Council for financial support. D.D.T acknowledges the ACT programme (Accelerating CCS Technologies, Horizon2020 Project no. 294766), which funded the FUNMIN project. Financial contributions from the Department for Business, Energy & Industrial Strategy (BEIS) together with extra funding from NERC and EPSRC research councils, United Kingdom, ADEME (FR), and MINECO-AEI (ES) are acknowledged. We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/T022213/1, EP/W032260/1 and EP/P020194/1). Via our membership of the UK's HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202), this work used the ARCHER UK National Supercomputing Service (https://www.archer.ac.uk). This research utilized Queen Mary's Apocrita HPC facility, supported by QMUL Research-IT, https://doi.org/10.5281/zenodo.438045.

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Footnote

† Electronic supplementary information (ESI) available: (i) Energies, zero-point energies, and entropies of H₂(g), CO₂(g), and CO(g). (ii) Configurations of the initial state, transition state, and final state involved in CO₂ direct dissociation to CO (CO₂* → CO* + O*) over Cu₄, Cu₃Sn, Cu₂Sn₂, and CuSn₃. (iii) Configurations of the intermediates OCHO*, COOH*, and *CO involved in the CO₂ reduction reactions over Cu₄, Cu₃Sn, Cu₂Sn₂, and CuSn₃. (iv) CO₂ adsorption, activation and reduction reaction on the isomers of Cu₃Sn. See DOI: https://doi.org/10.1039/d3cp00477e

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