Role of composition and texture on bifunctional catalytic performance of extruded Au–Cu alloys

Abstract

The catalytic activity of Au₂Cu and AuCu samples for the electro-oxidation of CH₃OH and HCOOH, together with their structure and micro-hardness were investigated using various techniques. The addition of Cu can improve the micro-hardness of samples, which is ascribed to the solid solution strengthening effect. The Schmid factor and low angle grain boundary fraction confirm the difference of plastic deformation ability for samples, being consistent with hardness results. The Au–Cu samples exhibit good bifunctional catalytic performance due to the synergistic effect between Au and Cu. In addition, the Au₂Cu sample exhibits a higher catalytic activity than the AuCu sample, suggesting that appropriate preferred orientation plays a key role in the improvement of catalytic activities of Au based catalysts.

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1. Introduction

Au is a noble metal which once was thought to be inactive due to its full 5d band structure and high ionization potential.^1,2 However, since the beginning of the 20th century, this view has gradually been changed. As early as 1906, Bone and Wheeler found that the reaction of hydrogen and oxygen can be catalyzed by Au under certain conditions.³ In 1973, Bond et al. reported the catalytic activity of Au for ethylene hydrogenation.⁴ In 1985, Hutchings reported that Au also exhibited good catalytic activity for the hydrochlorination of acetylene.⁵ In 1987, Haruta et al. first found that Au exhibited high catalytic activity for the oxidation of CO at low temperature.^6,7 Based on these studies, the catalytic activity of Au has been confirmed continuously.

With the consumption of fossil fuels, the demand for clean energy increases. Due to significant potential applications for electric vehicles, distributed home power generators, and power sources for small and portable electronics, fuel cells can contribute to solving the global energy crisis.⁸ It is necessary to decrease the cost and improve the oxidation kinetics of Au based catalysts for the development of fuel cells. However, active site density and intrinsic activity define the efficiency of catalyst, and active sites play important role in understanding of catalytic mechanism and driving the development of catalyst.^9,10 Alloying noble metals with non-noble metals is a promising method to obtain catalysts with reduced cost and more active sites, which is helpful for the improvement of catalytic activity.¹¹ In fact, bimetallic catalysts usually exhibit superior performance than monometallic catalysts, which is ascribed to the synergistic effect between Au and alloying element.^12–14 The electro-oxidation reactions of methanol (CH₃OH) and formic acid (HCOOH) play the important roles in the development of direct methanol fuel cells (DMFCs) and direct formic acid fuel cells (DFAFCs).^15,16 Recently, there are some bimetallic catalysts used for DMFCs and DFAFCs. For example, Kim et al. prepared carbon-supported Pt–Ru catalyst with excellent catalytic activity for oxidation of CH₃OH and improved the fuel cell performance;¹⁷ Lu et al. prepared Pd–Cu nanocrystals which exhibited high catalytic activity for the oxidation of HCOOH.¹⁸

It is considered that Au–Cu alloy catalyst may also potentially act as promising alternative for the electro-oxidation reactions of CH₃OH and HCOOH. In recent years, Au–Cu alloys have attracted many attentions due to the improved catalytic activity for some reactions and the low cost of Cu.^19–21 For example, Bauer et al. reported that silica supported AuCu alloy catalyst showed a good catalytic activity for CO oxidation;¹ Zhang et al. reported that AuCu₃/C intermetallic nanoparticles exhibited a better catalytic activity for the oxygen reduction reactions in alkaline solution;²² Schünemann et al. reported that due to the synergistic effect between Au and Cu, the mesoporous silica supported AuCu nanoparticles exhibited a better catalytic activity for the glycerol oxidation;²³ Birhanu et al. reported that Au–Cu bimetallic nanoparticles exhibited a good catalytic activity for the electrochemical reduction reaction of CO₂.²⁴ These Au–Cu catalysts are all in nanoscale and their catalytic activity are expected to be improved based on the adjustment of size and supporter. However, the study on catalytic activity of Au–Cu alloy catalysts for the electro-oxidation reactions of CH₃OH and HCOOH is few. Hence, the bifunctional catalytic performance of Au–Cu alloys is valuable to be investigated.

For a long time, it has been found that texture (preferred orientation) can affect the performance of Au catalysts.^25–27 Therefore, the catalytic activity of Au–Cu alloy catalyst may be improved based on the regulation of composition and texture. This work investigated the extruded Au–Cu alloy plates which may exhibit a certain texture, in order to find out the effect of composition and texture on the structure, micro-hardness and catalytic activity. It is expected that the valuable suggestions can be put forward for improving the catalytic activity of Au based alloy catalysts.

2. Experimental methods

The Au₂Cu and AuCu alloys employed in this study were prepared using a mini electric arc furnace (MAM-1, Edmund Bühler GmbH) under an Ar atmosphere. The compositions of samples are shown in Table 1. In order to ensure that the ingots can have a uniform structure, the pure Au and Cu were smelted in water-cooled copper mould of the arc furnace for 3 times (smelting temperature: 2500 °C, smelting time: 15 s, cooling time: 5 min). Then the original spherical ingots were extruded into thin plates with a thickness of 0.3 mm using a tablet press, therefore, the tested samples are in extruded state.

Table 1 Composition of Au₂Cu and AuCu samples

Samples	Au (wt%)	Cu (wt%)
Au2Cu	87.50	12.50
AuCu	75.00	25.00

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The phase compositions of samples were measured using X-ray diffractometer (XRD, Rigaku D/MAX-2500/PC) with a Cu target (Cu Kα, λ = 0.154056 nm). Based on the XRD data, the lattice constant a₀ is calculated according to extended Bragg equation.²⁸

where λ is the radiation wavelength, θ is the diffraction angle, and (h, k, l) is the crystal plane index.

The surface morphologies of samples and EDS mapping were observed using scanning electron microscopy (SEM, SU-70 and JSM-7610F). In order to investigate the element valence states and electronic structure of sample surfaces, the X-ray photoelectron spectroscopy (XPS, Escalab 250Xi) with Al-α radiation was used. The binding energies of tested elements for XPS test were calibrated by carbon contamination with C 1s peak (284.8 eV).

The texture changes of Au₂Cu and AuCu samples were explored using electron backscatter diffraction (EBSD, equipped with the Hitachi S-3400N SEM). Before the EBSD test, the samples were ion etched (voltage: 6 kV, incident angle: 8°, time: 45 min). The Schmid factor (SF), grain diameter, misorientation angle distribution, orientation maps and pole figures can be obtained using the software of HKL Channel 5.

The micro-hardness of samples was tested using HVT-1000A digital display micro-hardness tester. During the test, the applied load is 0.2 kg and the holding time is 15 s. Each sample was tested for 10 times and the average value is taken.

The cyclic voltammogram (CV) curves in 0.5 M KOH + 0.5 M CH₃OH solution (reference electrode in alkaline electrolyte: Hg|HgO|OH⁻, MOE) and 0.5 M H₂SO₄ + 0.5 M HCOOH solution (reference electrode in acid electrolyte: Hg|Hg₂SO₄|SO₄²⁻, MSE) were measured using advanced electrochemical workstation (CHI660E, Shanghai Chenhua Instrument Co. Ltd.). The CV curves were scanned initially from negative to positive potential during the test process.

3. Result and discussion

3.1 Structure and micro-hardness of samples

It is known that Au and Cu can form solid solution, and order–disorder transformation can occur with the change of temperature.^29,30 Fig. 1 shows the XRD patterns of Au₂Cu and AuCu samples, together with their appearances. There are five peaks on the XRD patterns which are indexed to the (111), (200), (220), (311) and (222) crystal planes of Au. The surface morphologies of two samples and the corresponding EDS mapping results are shown in Fig. S1.† It can be found that original sample surfaces are relatively smooth, and Au and Cu elements are uniformly distributed on the sample surfaces (Fig. S1(a)–(c) and (e)–(g)†). In addition, the compositions of two samples are basically consistent with the designed compositions (Fig. S1(d), (h)† and Table 1). Based on the XRD and EDS mapping results, it is believed that both samples are composed of Au–Cu solid solutions.

Fig. 1 XRD patterns of Au₂Cu and AuCu samples as well as the standard XRD pattern of pure Au. The inserts are the corresponding appearances of tested samples.

As shown in Table 2, compared to the diffraction peak position (2θ) of standard Au, the 2θ of Au–Cu samples shifts toward the higher value with the increase of Cu content. According to the extended Bragg equation (eqn (1)), a higher 2θ means a lower a₀. Since the atomic radius of Au and Cu respectively are 1.442 Å and 1.278 Å, the increase of Cu content can lead to a larger contraction of lattice structure of solid solution.²²

Table 2 Diffraction peak positions (2θ) of XRD patterns

Samples	(111)	(200)	(220)	(311)	(222)
Pure Au	38.184°	44.392°	64.576°	77.547°	81.721°
Au2Cu	39.042°	45.281°	66.261°	79.662°	84.017°
AuCu	40.162°	46.540°	68.342°	82.280°	86.980°

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Since the as-cast samples were extruded into plates in order to obtain a certain texture, the mechanical property which can affect the extrusion process is necessary to be investigated. It is believed that the mechanical property results can provide some clues for improving catalytic activities of samples based on texture regulation in future. Fig. 2 shows the micro-hardness of samples. It can be found that the micro-hardness of Au–Cu samples (Au₂Cu: 204.2 HV_0.2, AuCu: 275.4 HV_0.2) is higher than that of pure Au (33.8 HV_0.2) apparently, and the increase of Cu content is benefit for the enhancement of micro-hardness of Au–Cu samples. In addition, the Schmid factor (SF) can be assessed using EBSD analysis based on Schmid law. Schmid law states that a slip system can be activated if the resolved shear stress (τ) on the slip plane and in the slip direction reaches a certain critical value. The τ in the slip direction can be expressed as the following equation.^31,32

Here, F and A respectively are the loading force and the section area of sample (i.e., is stress), λ is the angle between stress axis and the slip direction, and φ is the angle between stress axis and the normal direction of the slip plane. The SF is defined as cos λ·cos φ. Hence, a higher SF means a higher plastic deformation tendency. As shown in Fig. 3, the SF of AuCu sample is lower than that of Au₂Cu sample, which is consistent with the micro-hardness results.

Fig. 2 Micro-hardness of Au₂Cu and AuCu samples. Red line segment represents the error bar.

Fig. 3 Schmid factors (SF) of Au₂Cu and AuCu samples.

As mentioned above, there is the lattice distortion in the Au–Cu solid solution, which can lead to the solid solution strengthening effect. As shown in Fig. 4, although the average grain diameter for AuCu sample (∼13.37 μm) is slightly higher than that for Au₂Cu sample (∼12.35 μm), the AuCu sample with a larger lattice distortion exhibits a higher micro-hardness value.

Fig. 4 Grain diameter distribution of Au₂Cu and AuCu samples.

3.2 Electro-oxidation reaction of CH₃OH on Au–Cu samples

Fig. 5 shows the CV curves of Au–Cu samples in 0.5 M KOH + 0.5 M CH₃OH solution (scan rate v: 50 mV s⁻¹). As the potential sweeps positively, there are the peaks P₁ (∼−0.01 V vs. MOE), P₂ (∼0.36 V vs. MOE), P₃ (∼0.57 V vs. MOE) and P₄ (∼0.99 V vs. MOE). The peak P₁ is corresponding to the partial charge-transfer of chemisorbed OH⁻ anions, pre-oxidation of Au surface and formation of pre-oxidation precursor.^26,33

Au + OH⁻ − λe⁻ → Au–OH_ads^(1−λ)− (3)

Here ads indicates the chemisorbed species on Au surface and λ varies from 0 to 1. The chemisorbed species can play a promoting role in the electro-oxidation of CH₃OH in alkaline solution.³³ The peak P₂ can be assigned to the formation of Cu(I) species.^34,35 As shown in Fig. S2,† the peaks P₃ and P₄ also appear on the CV curves for pure Au. The current density (j) of peak P₃ in 0.5 M KOH + 0.5 M CH₃OH solution is apparently higher than that in 0.5 M KOH solution, indicating that the peak P₃ can be ascribed to the oxidation of Au and CH₃OH. In addition, it is thought that the peak P₃ for Au–Cu samples should be also related to the formation of Cu(II) species.^34,35 In this region, CH₃OH is oxidized to HCOO⁻ with a reaction of 4 electron transfer.^33,36

Au–OH_ads^(1−λ)− + OH⁻ − (2 − λ)e⁻ → AuO + H₂O (4)

CH₃OH + 5OH⁻ − 4e⁻ → HCOO⁻ + 4H₂O (5)

Fig. 5 Cyclic voltammograms (CVs) of (a) Au₂Cu and (b) AuCu samples in 0.5 M KOH + 0.5 M CH₃OH solution. The dash lines are the CV curves in 0.5 M KOH solution. Scan rate v: 50 mV s⁻¹.

The peak P₄ for Au–Cu samples is apparently higher than that for pure Au. It is thought that the peak P₄ can be ascribed to the oxidation reaction of Cu(II) to Cu(III), which is accompanied by the oxidation of CH₃OH on Au oxide and Cu(III) species surfaces. In fact, Cu(III) species is a strong oxidant which can play an important role in the oxidation of CH₃OH, and after the reaction the Cu(III) will be reduced to Cu(II).^34,35 During this reaction process, the CH₃OH is oxidized to CO₃²⁻ with a reaction of 6 electron transfer.^26,33

CH₃OH + 8OH⁻ − 6e⁻ → CO₃²⁻ + 6H₂O (6)

As the potential sweeps negatively, there is the peak P₅ (∼0.10 V vs. MOE) ascribed to the reduction reaction of Au oxides and Cu(II) species, while the peak P₆ (∼−0.09 V vs. MOE) can be assigned to the reduction of Cu(I) to Cu.

However, it can be found that the j of peaks P₃ and P₄ for Au–Cu samples are obviously higher than that for pure Au (Fig. 5 and S2†), indicating that the addition of Cu is benefit for enhancing the catalytic activity of Au based catalysts for the electro-oxidation of CH₃OH. The improvement of catalytic activity is related to the synergistic effect between Au and Cu.^12,23

The SEM images of Au–Cu sample surfaces after CV tests in 0.5 M KOH + 0.5 M CH₃OH solution are shown Fig. 6. The surfaces of Au–Cu samples after test are much rougher compared with those before test (Fig. S1† and 6). In addition, the surface of Au₂Cu sample is rougher than that of AuCu sample, indicating that the redox reactions for Au₂Cu sample are more intense than those for AuCu sample. However, the surface of pure Au sample is relatively smooth after CV test (Fig. S3†), also meaning the higher activity for Au–Cu samples. The EDS mapping results of Au–Cu samples after CV test are shown in Fig. S4.† It is clear that compared with the Cu contents of two sample surfaces before CV test, the Cu contents after CV test decrease, indicating that partial Cu was released from the surface into the solution during the reaction process (Fig. S1 and S4†).

Fig. 6 SEM images of (a) Au₂Cu and (b) AuCu samples after CV tests in 0.5 M KOH + 0.5 M CH₃OH solution.

According to ref. 37 and 38, the relationship between j and v can be expressed as following.

log j = b log v + a (7)

Here, a and b are the adjustable constants. The b value corresponded to the slope of fitted line can indicate the adsorption-controlled process (b = 1.0) and diffusion-controlled (b = 0.5) process, respectively.^39–41 Fig. 7 shows the CV curves of Au–Cu samples with different v in 0.5 M KOH + 0.5 M CH₃OH solution. The j increases with increasing the v, because the concentration gradient becomes much steeper in diffusion layer (Fig. 7(a) and (b)).⁴² According to the log j–log v plots, the b values of j_P3 are 0.30 and 0.46 for Au₂Cu and AuCu respectively (Fig. 7(c)). Hence, it is believed that the oxidation reaction corresponded to peak P₃ is mainly controlled by diffusion. However, there is small deviation from the theoretical value of 0.5, which is related with the kinetic limitation in reaction.^39,40 The b values corresponded to j_P5 are 0.86 and 1.29 for Au₂Cu and AuCu respectively (Fig. 7(e)), indicating that the reduction reactions corresponded to peak P₅ mainly are adsorption-controlled processes. With increasing the v, the peak-to-peak potential separation (ΔE_P = E_P3 − E_P5) increases (Fig. 7(d) and (f)), which is due to the presence of kinetic limitation.^40,43

Fig. 7 Cyclic voltammograms (CVs) of (a) Au₂Cu and (b) AuCu samples in 0.5 M KOH + 0.5 M CH₃OH solution with different scan rates (scan rate v: 5, 10, 20, 50, 80, 100, 120, 150 and 180 mV s⁻¹). The dependency of the log of current densities log j and the potentials E of (c and d) peak P₃ and (e and f) peak P₅ of CV curves in (a) and (b) on log v are also shown.

3.3 Electro-oxidation reaction of HCOOH on Au–Cu samples

It is known that the electro-oxidation reaction of HCOOH can proceed via direct pathway and indirect pathway.^44,45

HCOOH → CO₂ + 2H⁺ + 2e⁻ (8)

HCOOH → CO_ads + H₂O → CO₂ + 2H⁺ + 2e⁻ (9)

The direct pathway is dehydrogenation reaction during which CO₂ is directly formed (eqn (8)). The indirect pathway is dehydration reaction, during this reaction process CO_ads can be formed and then it is oxidized to CO₂ at a higher potential (eqn (9)).

Fig. 8 shows the CV curves of Au–Cu samples in 0.5 M H₂SO₄ + 0.5 M HCOOH solution. There are peaks P_i, P_ii and P_iii on the CV curves, while there is no peak P_i for pure Au (Fig. S5†). Hence, the peak P_i should be related with Cu. In addition, the j of peaks P_i and P_ii on CV curve in solution contained HCOOH is apparently higher than those only in H₂SO₄ solution. Hence, the peak P_i can be ascribed to the oxidation reactions of Cu and HCOOH, and the peak P_ii can be ascribed to the oxidation reactions of Au and HCOOH. Furthermore, the j of peak P_ii for Au–Cu samples is higher than that for pure Au, indicating that the addition of Cu also can improve the catalytic activity of Au based catalyst for electro-oxidation of HCOOH (Fig. 8 and S5†). Here, it is thought that the electro-oxidation of HCOOH proceeds via direct pathway (eqn (8)). In the negative scans of CV curves, there is a peak P_iii ascribed to the reduction reaction of Au and Cu oxides.

Fig. 8 Cyclic voltammograms (CVs) of (a) Au₂Cu and (b) AuCu samples in 0.5 M H₂SO₄ + 0.5 M HCOOH solution. The dash lines are the CV curves in 0.5 M H₂SO₄ solution. Scan rate v: 50 mV s⁻¹.

Since the peak P_i is related to Cu, therefore increasing the Cu content, the j of peak P_i increases. Consequently, there are more Cu oxides formed on the surface of AuCu sample than those of Au₂Cu sample. Because the formed Cu oxides can isolate the active sites from catalyzing the electro-oxidation reaction of HCOOH, the j of peak P_ii for AuCu sample is lower than that for Au₂Cu sample (Fig. 8).

The surface morphologies of Au–Cu samples after CV tests in 0.5 M H₂SO₄ + 0.5 M HCOOH solution are shown in Fig. 9. The surfaces of Au–Cu samples after test are much rougher than those before test (Fig. S1† and 9). It can also be seen that the surface of pure Au after CV test is relatively smooth, while the surfaces of Au–Cu samples are much rougher (Fig. 9 and S6†), also indicating a higher catalytic activity of Au–Cu samples.

Fig. 9 SEM images of (a) Au₂Cu and (b) AuCu samples after CV tests in 0.5 M H₂SO₄ + 0.5 M HCOOH solution.

Fig. 10 shows the CV curves of Au–Cu samples with different v in 0.5 M H₂SO₄ + 0.5 M HCOOH solution. It is similar to the case in CH₃OH solution, the j increases with increasing the v (Fig. 10(a) and (b)), which is due to the steepening of concentration gradient in diffusion layer.⁴² According to the log j–log v plots, the b values for peak P_ii of Au₂Cu and AuCu samples are 0.31 and 0.40 respectively, while the b values for peak P_iii of two samples respectively are 0.94 and 1.06 (Fig. 10(c) and (e)). Hence, it is considered that the oxidation reactions corresponded to peak P_ii are mainly controlled by diffusion together with the presence of kinetic limitation, and the reduction reactions corresponded to peak P_iii are mainly controlled by adsorption. With increasing the v, the oxidation peak potentials (E_Pii) shift to the positive direction (Fig. 10(d)), while the potentials of reduction peaks (E_Piii) shift to the negative direction (Fig. 10(f)), resulting in an increase of peak-to-peak potential separation (ΔE_P = E_Pii − E_Piii). This increase also indicates the presence of kinetic limitation,⁴³ which is similar to the phenomenon in CH₃OH solution (Fig. 7(d) and (f)).

Fig. 10 Cyclic voltammograms (CVs) of (a) Au₂Cu and (b) AuCu samples in 0.5 M H₂SO₄ + 0.5 M HCOOH solution with different scan rates (scan rate v: 5, 10, 20, 50, 80, 100, 120, 150 and 180 mV s⁻¹). The dependency of the log of current densities log j and the potentials E of (c and d) peak P_ii and (e and f) peak P_iii of CV curves in (a) and (b) on log v are also shown.

3.4 Effect of alloying and texture on catalytic activity of Au–Cu samples

As mentioned above, the alloying of Au with Cu is benefit for the improvement of catalytic activity of Au based catalysts for both electro-oxidation reactions of CH₃OH and HCOOH, and the Au₂Cu sample exhibits a better catalytic activity than AuCu sample (Fig. 7 and 10).

In fact, the alloying can tune the electronic structure of Au based catalysts, which plays a key role in catalytic activity.^46,47 As shown in Fig. 11 and S7,† the XPS spectra of Au₂Cu and Au sample surfaces before and after the appearance of peak P₃ on CV curves tested in 0.5 M KOH + 0.5 M CH₃OH solution were obtained. It can be found that the binding energies (BE) of Au 4f and Cu 2p tend to shift towards for the higher values after the peak P₃. According to the BE, there is zero valent Au (Au⁰) on the Au₂Cu and Au sample surfaces before the peak P₃, while the electronic structure of Au 4f after the peak P₃ can be considered as coexistence of Au⁰ and Au⁺ (Fig. 11(a) and S7†).⁴⁸ However, the BE of Au 4f_7/2 and Au 4f_5/2 for Au⁰ on Au₂Cu sample surface (before the peak P₃: 84.32 eV and 87.99 eV; after the peak P₃: 84.25 eV and 87.90 eV) were higher than those on Au sample surface (before the peak P₃: 84.16 eV and 87.83 eV; after the peak P₃: 84.14 eV and 87.82 eV), indicating that Au tends to lose valence electrons to Cu and this perturbation can affect the surface energy of Au to achieve a better electrochemical performance.^12,24 In addition, the BE of Cu 2p before the peak P₃ are ascribed to Cu⁺, while those after the peak P₃ are ascribed to Cu⁺ and Cu²⁺ (Fig. 11(b)).^49–51 In fact, compared with pure Au, the alloying with Cu can lead to the upper shift of d-band center, which can enhance the combine ability with O for alloys.^46,52,53 Moreover, Cu can form Cu–O species which can provide reactive O during the oxidation process.^14,49 Hence, the Au–Cu samples exhibit a better catalytic activity than monometallic Au.

Fig. 11 XPS spectra for (a) Au 4f and (b) Cu 2p of sample surfaces with Au₂Cu before and after the anodic peak P₃ of CV curves tested in 0.5 M KOH + 0.5 M CH₃OH solution.

According to ref. 27, 54 and 55, preferred orientation plays a key role in catalytic activity of Au based catalysts. Since the Au–Cu samples are in extruded state, the difference of catalytic activity between Au₂Cu and AuCu samples may be related with their texture. The degree of crystalline orientation can be determined using texture coefficient (TC).^56,57

where I_(hkl)i and I_0(hkl)i are the integral intensities of (hkl)_i diffraction peaks for the experimental and completely random samples, respectively; n is the number of diffraction peaks considered in the analysis. It can be seen from Fig. 12 that the TC values of (111) crystal planes for both samples are nearly equal, while the TC value of (100) crystal plane for Au₂Cu sample is much higher and its TC value of (110) crystal plane is much lower.

Fig. 12 Texture coefficients (TC) of (111), (100) and (110) crystal planes for Au₂Cu and AuCu samples.

In order to further verify the texture variation, the misorientation angle distributions and pole figures for samples were obtained using EBSD technique. In Fig. 13, the “Correlated” misorientation angles are calculated from neighboring points, the “Uncorrelated” misorientation angles are calculated from random points, and the “Random” misorientation distribution is deduced from a completely random texture. It can be found that there are slight deviations between the uncorrelated misorientation and random misorientation for both Au–Cu samples, indicating the presence of weak texture. According to the correlated misorientation, the fractions of low angle grain boundary (LAGB, <5°), medium angle grain boundary (MAGB, 5°–15°) and high angle grain boundary (HAGB, >15°) are listed in Table 3. It is known that the LAGB is mainly composed of a series of dislocations.^58–60 The fraction of LAGB in AuCu sample is about 61%, while that in Au₂Cu sample is about 49%. That is to say that there are more dislocations in AuCu samples, also indicating that the addition of Cu can affect the lattice structure of Au based catalysts and improve their plastic deformation ability.

Fig. 13 Histograms of misorientation angle distributions of (a) Au₂Cu and (b) AuCu samples.

Table 3 Fractions of low angle grain boundary (LAGB), medium angle grain boundary (MAGB) and high angle grain boundary (HAGB) for Au₂Cu and AuCu samples

Samples	LAGB	MAGB	HAGB
Au2Cu	0.49	0.36	0.15
AuCu	0.61	0.29	0.10

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The orientation maps and pole figures of Au–Cu samples are shown in Fig. 14. For the extruded samples, there are rolling direction (RD), transverse direction (TD) and normal direction (ND). The CV test surface is perpendicular to the ND of sample. It can be seen from the orientation maps that the grain size of AuCu sample is slightly larger than that of Au₂Cu sample, which is consistent with the grain diameter results (Fig. 4 and 14(a), (b)). According to pole figures, Au₂Cu sample exhibits a 〈100〉 preferred orientation (the normal direction of (100) crystal plane) which deviates about 30° from ND, while AuCu sample also exhibits a 〈100〉 preferred orientation which deviates about 50° from ND. Hence, compared with AuCu sample, more (100) crystal planes can be exposed on the CV test surface of Au₂Cu sample. It also can be found that AuCu sample exhibits an obvious 〈110〉 preferred orientation which is nearly parallel to the ND. In addition, both Au–Cu samples exhibit the 〈111〉 preferred orientations which deviate about 30° from RD. However, the pole figure results are consistent with the TC variation (Fig. 12 and 14(c), (d)). Based on the ref. 61, the interactions of Au(100) crystal plane and adsorbed O can lead to the drastic surface reconstruction, and the reconstructed Au(100) crystal plane exhibits a better catalytic activity. In fact, compared with Au(110) crystal plane, Au(100) crystal plane is considered to be more active for the catalytic reactions.^26,62,63 Hence, the Au₂Cu sample has a better catalytic activity than AuCu sample, which is ascribed to the different preferred orientations of their CV test surfaces.

Fig. 14 Orientation maps of (a) Au₂Cu and (b) AuCu samples and pole figures of (c) Au₂Cu and (d) AuCu samples.

4. Conclusions

Au₂Cu and AuCu samples were prepared using vacuum arc melting. Both samples are composed of Au–Cu solid solutions. A higher Cu content means a better solid solution strengthening effect. AuCu sample has a higher micro-hardness value than Au₂Cu sample, which is consistent with their plastic deformation ability confirmed by the SF values and fraction of LAGB.

The addition of Cu can improve the catalytic activity of Au based catalysts, which is related to the electron transfer between two elements and a better ability to combine with oxygen for Cu. There are more Au(100) crystal planes on the CV test surface of Au₂Cu sample. Since Au(100) crystal plane is more active for the catalytic reactions of Au based catalysts, hence Au₂Cu sample exhibits a better catalytic activity than AuCu sample. This work can provide a clue to tune the catalytic activity of Au based catalysts by alloying with Cu and obtaining appropriate preferred orientation.

Author contributions

Kechang Shen: methodology, investigation, writing – original draft, writing – review & editing. Qingtao Gong: funding acquisition. Hao Zhang: visualization. Kangqiang Li: visualization. Zhongyu Sun: supervision. Guihua Li: data curation, validation. Xin Hu: project administration. Lu Liu: formal analysis. Weimin Wang: funding acquisition, conceptualization, resources, writing – review & editing.

Conflicts of interest

The authors declare no competing interests.

Acknowledgements

This work was financially supported by National Natural Science Foundation of China (No. 51771103, 52101196 and 52171158), Key Research and Development Program of Shandong Province (No. 2021ZLGX01 and 2020ZLYS11), Major Innovation Projects in Shandong Province (No. 2020CXGC010701 and 2020CXGC010702), Yantai Science and Technology Planning Project (No. 2021ZDCX001), Open Project Program of Shandong Marine Aerospace Equipment Technological Innovation Center (Ludong University) (No. MAETIC2021-11), and Outstanding Youth Innovation Team Project of Shandong Higher Education Institution (No. 2021KJ042).

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Footnote

† Electronic supplementary information (ESI) available. See https://doi.org/10.1039/d2ra03438g

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