Microkinetic studies for mechanism interpretation in electrocatalytic CO and CO₂ reduction: current and perspective

Abstract

Microkinetic analysis can establish the relationship between the atomic-level reaction mechanism and macroscopic observables, such as reaction rates, product selectivity, Tafel slope, reaction order, isotopic effect, and apparent activation energy, at given operating conditions. This relationship is essential in the rational design of electrocatalysts and reactor configurations. In recent years, microkinetic analysis, particularly Tafel and reaction order analysis, has seen significant advancements in its application for interpreting reaction mechanisms in electrocatalytic CO and CO₂ reduction. This review summarizes the progress in understanding the complex kinetic processes through theoretical microkinetic simulation modeling and experimental measurements. However, the reaction mechanisms derived from microkinetic analysis are disputed, complicating efforts to design electrocatalysts. This review analyzes the discrepancies in the literature and elucidates deeper insights into experimental discrepancies. The importance of local reaction environments in the intrinsic kinetic behavior of electrocatalysts is highlighted. The report also discusses the challenges and limitations of microkinetic analysis. Finally, the review suggests some perspectives on future investigations. Overall, this review is expected to provide new insights, critical interpretation, and guidance for the future development of microkinetic measurements and analysis.

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Broader context

Electrochemical CO₂ reduction powered by renewable energy offers a sustainable and cost-effective approach to convert harmful CO₂ emissions into valuable chemicals and fuels, reducing the dependency on fossil fuels and contributing to the development of a circular carbon economy. Microkinetic analysis plays a crucial role in the design and optimization of catalytic systems, which allows for the determination of the reaction pathways and the identification of the rate-determining step involved in CO₂ electroreduction. Nevertheless, the reaction mechanisms derived from microkinetic analysis remain debatable, impeding the establishment of design guidelines for effective catalysts and the optimization of reaction conditions. This review attempts to summarize the recent advancements in the understanding of complex kinetic processes through theoretical microkinetic simulation modeling and experimental measurements. The discrepancies in the literature are analyzed, highlighting the significance of local reaction environments in the intrinsic kinetic behavior of electrocatalysts. Furthermore, it discusses the challenges and limitations of microkinetic analysis in electrochemical CO₂ reduction. Finally, operando spectroscopies as complementary tools are proposed, and strengthening the connection between experiments and computation is emphasized to increase its accuracy and reliability in reaction mechanism interpretation.

1. Introduction

The electrocatalytic reduction of CO and CO₂ (CO₍₂₎) may offer an alternative route to produce carbonaceous fuels and chemicals in decarbonized societies, especially with the decrease in renewable electricity prices.^1–3 Therefore, huge research efforts have been dedicated to this direction in the last few decades, demonstrating the potential of producing CO, formate, or hydrocarbons via electrocatalytic CO₍₂₎ reduction.^4–7 However, despite the added motivation of the gas-diffusion electrode (GDE), these processes still suffer from low efficiency and require the development of active and selective electrocatalysts for any large-scale implementation.^8–11 Unfortunately, interpreting the atomic-level reaction mechanisms remains controversial due to the involvement of multiple electron and proton transfers, hampering the establishment of robust design principles for electrocatalysts.

Microkinetic modeling is a powerful tool for addressing rate-determining steps (RDS) and detailed reaction pathways, both of which are explanatory and predictive in rational catalyst design.^12–18 For example, microkinetic analysis using density functional theory (DFT) calculations provides a deeper understanding of why Au and Ag are at the top of the experimentally obtained volcano trend for CO evolution during electrocatalytic CO₂ reduction, revealing the moderate binding affinity of the adsorbed carboxylate (*COOH) on Au and Ag.^19–22 Additionally, it distinguishes Cu from other elements as the only metal to go beyond C1 products efficiently in electrocatalytic CO₂ reduction.^4,23 Furthermore, Sargent and his colleagues developed a machine-learning-accelerated, high-throughput DFT framework to identify Cu–Al alloys as promising electrocatalysts for C₂H₄ production.²⁴ Despite the wide application of DFT-based microkinetic modeling, using it as a quantitative predictive tool is challenging.²⁵ Therefore, enhancing the connections between computation and experimental observables is essential to increase the effectiveness of microkinetic modeling.

Microkinetic analysis from experiments is essential for creating precise DFT-based microkinetic models for the active site and its environment. Tafel analysis is one of the most common experimental techniques used to understand the RDS in electrocatalytic CO₂ reduction.^26,27 In the classical analysis, the experimentally observed Tafel slope is compared with the theoretically derived slope to identify the corresponding RDS. However, this method has several assumptions, including steady-state reactions and no mass transportation limitations under reaction conditions, the constant assumptions (0 or 1) of intermediate surface coverage, and an electron transfer coefficient of 0.5.^28–31 To point out the RDS, additional methods are necessary, as different proposed RDS can yield the same identified Tafel slope.^26,27,29 For instance, one electron transfer and proton-coupled electron transfer (PCET) over Ag and Au both yield a Tafel slope of approximately 120 mV dec⁻¹.^26,31,32 Thus, the isotopic effect (hydrogen/deuterium) or operando spectroscopies are necessary to provide deeper insights into the RDS. Moreover, the type of proton donor and CO₂ source must be identified since protons can come from various sources including H₂O, free proton (H⁺), and H-containing species (e.g., HCO₃⁻); CO₂ can be supplied by gas-phase CO₂ or bicarbonate.^20,31,33 The apparent reaction orders with respect to pH and reactants (e.g., partial pressure of CO₍₂₎ (P_CO(2)) and bicarbonate concentration (c_HCO3⁻)) can distinguish between them.³⁴ Temperature dependence can also provide a fingerprint of the reaction pathways. Overall, rigorous microkinetic analysis using Tafel slopes, kinetic isotope effects (KIE), apparent reaction orders, and temperature sensitivity can deconvolve the complexity of interface reactions occurring on the electrode and elucidate the reaction mechanisms, which are highly desired in rational catalyst design.

Up to this point, several issues have arisen in interpreting experimental kinetic data, related to measuring the intrinsic kinetic behavior of catalysts.^26,33,35,36 For example, local environments and experimental designs can significantly affect the experimental results, which can, in turn, mislead the mechanistic elucidation.^37–42 Moreover, some assumptions have limitations. The theoretically derived slopes, for instance, generally assume an extreme coverage of the adsorbed species (θ = 0 or 1).^43,44 However, several studies have claimed to observe some key intermediates via joint spectroscopic/DFT techniques,^45–47 including the negatively charged CO dimer on Cu during CO reduction,⁴⁸ adsorbed CO and CH_x on Cu-based catalysts,^49–51 and proposed COOH intermediate over polycrystalline Ag (pc-Ag)⁵² during CO₂ reduction. Additionally, the charge transfer coefficient is always assumed to be 0 or 0.5 for elementary steps, although, in practice, it is a function of both overpotentials and reorganization energy.^29,30 Therefore, revisiting microkinetic analysis and understanding its limitations during CO₍₂₎ reduction are of crucial in elucidating atomic-level reaction mechanisms.

This review mainly focuses on the development, challenges, and limitations of microkinetic analysis during electrocatalytic CO₍₂₎ reduction. Firstly, it reviews the DFT-based microkinetic modeling of various products, such as CO, formic acid, CH₄, and C₂₊. It addresses the theoretically derived Tafel slopes and reaction orders under the assumption of different RDSs. Secondly, it summarizes and discusses the experimentally observed parameters (e.g., Tafel slopes, reaction orders, and kinetic isotopic effects) and corresponding reaction mechanisms over various electrocatalysts in electrocatalytic CO₍₂₎ reduction. Special attention is given to the discussions of the reported discrepancies in the literature. Moreover, the existing challenges and limitations in the microkinetic analysis are interpreted from different aspects, such as intrinsic kinetic measurements, dynamically restructured catalyst, the complexity of electrolyte effects, intermediate surface coverage assumption, and electron transfer coefficient. Finally, this review provides an outlook on the future development of kinetic analysis in rational catalyst design. It implies the importance of operando spectroscopies and computational methods as complementary tools in microkinetic modeling. This review provides deeper insights into microkinetic analysis in electrocatalytic CO₍₂₎ reduction and emphasizes the importance of these complementary tools for future research.

2. DFT-based microkinetic simulation of electrocatalytic CO₍₂₎ reduction

The pioneering work by Hori et al. examined a variety of metal electrodes for the electrocatalytic CO₂ reduction reaction, having stimulated the development of heterogeneous catalysts in this field.^53–57 Among them, copper is a unique catalyst sitting at the top of the “volcano” plot for electrocatalytic CO₂ reduction, in which both CO- and formate-paths prevail, yielding a variety of CO₂-reduction products.⁴ In contrast, there have been several elements reported to achieve exclusive production of single products (e.g., CO, HCOOH). More specifically, p-block electrodes, such as indium or tin, selectively produce formate while keeping the concurrent hydrogen evolution reaction (HER) silent.^58–62 In addition, although the metals in groups 8–10 of the periodic table favor the HER over the CO₂ reduction routes, gold and silver metal electrocatalysts direct the reaction toward the CO route selectively, achieving a faradaic efficiency toward CO (FE_CO) greater than 90%.^32,33,63,64 Interestingly, DFT calculations could interpret the activity-selectivity map by quantifying the free energies of reaction intermediates, e.g., *COOH, *CO, and *OCOH species, in agreement with the experimentally obtained results. These findings validate the reliability of DFT-based microkinetic modeling in electrocatalytic CO₂ reduction, where the active site and its electrochemical environment have been taken into account due to advancements in the computation.^37,65–72 The widespread success has made computational methods a routine practice as quantitative predictive tools.

This section reviews the recent development of DFT-based microkinetic simulation of electrocatalytic CO₍₂₎ reduction, mainly focusing on key intermediates (descriptors) and proposed reaction pathways. Moreover, the theoretically calculated kinetic parameters (e.g., Tafel slope, reaction order, and KIE) are also summarized by assuming different RDSs in proposed reaction pathways during electrocatalytic CO₍₂₎ reduction.

2.1 Key intermediates (descriptors) and reaction pathways for C₁ formation

Although the reaction pathways of CO and formic acid/formate formation (HCOOH/HCOO⁻) over different catalysts remains debated, the key intermediates (descriptors) have been well-defined by recent calculation studies. Chan and co-workers depicted a unified mechanistic picture of electrocatalytic CO₂ reduction toward CO on transition metals (TMs), molecular complexes (e.g., phthalocyanine (Pc)), and metal–nitrogen-doped carbon catalysts (MNCs) by using the Newns–Andersen model, where the effects of the adsorbate–field interactions for all the intermediates were taken into account.⁷³ The general kinetic activity of CO evolution was determined by two activity descriptors, namely the free energy of adsorption (ΔG) of CO₂ and COOH, as shown in Fig. 1a and b. Fig. 1a exhibited the unified activity volcano for the CO evolution on both TM and MNC catalysts, and the maximum theoretical activity appeared when both ΔG_CO2 and ΔG_COOH were close to 0 eV. Moreover, the RDS could be identified at any given potential using computed *CO₂, *COOH, and *CO free energies. More specifically, CO₂ adsorption was the RDS when the points appeared above the parity line (e.g., TM), while *COOH formation was the RDS if the points were below the line (e.g., NiN_x and FeN_x). They found that on MNCs, electron transfer to CO₂ was facile. Moreover, *CO desorption could be the RDS with very negative adsorption energies (Fig. 1b), where the surface was poisoned by *CO (e.g., Pd and Pt). Feaster et al. used a DFT-based microkinetic model to describe the experimentally observed trend of formate production over TMs, showing why Sn was the best-known metal electrocatalyst (Fig. 1c). The *OCHO binding energy was singled out as the most important descriptor.⁷⁴ The potential-dependent selective regions for formate, CO, and H₂ were plotted by combining descriptor (ΔG_CO, ΔG_OH) maps, as shown in Fig. 1d. The dashed green line represented the thermodynamic boundary condition defined by ΔG_COOH = ΔG_HCOO.⁷⁵

Fig. 1 (a) The trends in the binding of key intermediates (ΔG_CO2 and ΔG_COOH) and corresponding rate map at −0.8 V versus standard hydrogen electrode (SHE) and pH 2 for CO evolution over TMs and MNCs. (b) CO coverage map (θ_CO) in monolayers (ML) with the same points showing which surfaces are poisoned by CO. Reproduced with permission.⁷³ Copyright 2021, nature publishing group. (c) The trends in binding *OCHO for formic acid production over TMs. Reproduced with permission.⁷⁴ Copyright 2017. (d) The potential-dependent selective regions for formate, CO, and H₂ were plotted by combining descriptor (ΔG_CO, ΔG_OH) maps. Reproduced with permission.⁷⁵ Copyright 2020, from Elsevier.

Additionally, the nature of the electrogenerated reactive nucleophilic species were also identified as important descriptor to determine the selectivity between CO and HCOOH/HCOO⁻ during CO₂ reduction.^76–78 For metal tetraphenylporphyrin (MTPP),⁷⁶ the electrogenerated metal-hydride or phlorin-hydride (M: In, Sn, Rh, Cd, Ga, and Pd) could attack the carbon of the CO₂ to form C–H bond, which resulted in the formation of HCOOH/HCOO⁻.⁷⁹ On the contrary, the reduced metal-centers (e.g., Fe, Co, Ni) triggered the formation of the metal–carbon bond with CO₂, which then gave rise to the formation of CO.⁷⁶ Similarly, it also has been reported that the thermodynamically generated surface adsorbed hydrogen on Pd could significantly decrease the overpotential of formate production in aqueous bicarbonate solutions.⁷⁷ And the product distributions on PC–Cu and Au were shifted from CO to formate at low overpotentials after chemical modification with a poly(4-vinylpyridine) (P4VP) layer, which was suggested to promote a surface hydride mechanism for formate formation on both electrodes.⁸⁰

2.2 Key intermediates (descriptors) during reaction pathways for CH₄ and C₂₊ formation

CO is demonstrated as a key intermediate on the pathway to CH₄ and C₂₊ compounds during electrocatalytic CO₂ reduction.^57,81 Only copper-based electrocatalysts have been reported to convert CO₂ into CH₄ or C₂₊ products at moderate rates under ambient pressure and temperature, which was rationalized that Cu-based electrodes possess the balanced chemisorption energy of CO and proton (Fig. 2a).⁴ Accordingly, this review only focuses on the microkinetic models for CO electroreduction towards CH₄ and C₂₊ on Cu based electrodes.

Fig. 2 (a) Metal classification for CO₂ electroreduction. Reproduced with permission.²³ Copyright 2017, Wiley. (b) Rate map at −0.5 V versus reversible hydrogen electrode (RHE) for CH₄ formation over Cu. Reproduced with permission.⁸² Copyright 2017, Nature. (c) Schematic diagram of reaction steps for C₂₊ over Cu. Reproduced with permission.⁸⁴ Copyright 2021, from RSC Publishing.

Nørskov and co-workers developed a first-order absorbate interaction model for CH₄ formation (Fig. 2b), where the proton-electron transfer from *CO to *H-CO was considered as RDS for CH₄ formation and the stepped facet was predicted to deliver significantly higher rates than flat facets due to the preferred *CO protonation on the stepped facet.⁸² Regarding C₂₊ products, the processes are very complex due to the involvement of multiple proton and electron transfers. Goodpaster et al. built microkinetic modeling on Cu(100) to predict ΔG of elementary steps for C₂₊ formation, where the electrochemical potential, solvent, and electrolyte were considered. This model revealed that C–C bond formation occurred via CO dimerization at low-applied potential, which shifted to the coupling between adsorbed CO and CHO at high overpotentials.⁸³ Peng et al. also established microkinetic modeling to identify the key steps toward CH₄ and C₂₊ products on Cu(100) (Fig. 2c), where two simple thermodynamic descriptors (ΔG_C* and ΔG_CO*) were proposed. The selectivity between C₂₊ and CH₄ was determined through the energetic difference between barriers for *CCO and *CH formation.⁸⁴

In terms of pH dependence on CH₄ and C₂₊ formation, in Peng's model (Fig. 3a), the dominant pathway for C₂₊ formation was *CO and *C coupling at neutral pH and moderate potential windows (e.g., −0.4 V to −1 V vs. reversible hydrogen electrode (RHE)), which shifted to the coupling between *CO and *COH at high pH. For CH₄ formation, it preferred the formation *COH first and then went through a *C intermediate under wider pH and potential windows. Moreover, some other pathways have been proposed for CH₄ (e.g., *CHO pathways) and C₂₊ (e.g., *OC–CH pathway and *CH₂CH₂ pathway).⁸⁴ Xiao et al. used an implicit solvation model on Cu(111) to interpret the reaction pathways and examine the pH effects, showing that higher pH favored C–C coupling through the dimerization of *CO and the common intermediate *COH was shared by CH₄ and C₂H₄ at neutral pH.⁸⁵ Hahn and co-workers also performed microkinetic analysis using a constant-potential model to understand the Tafel slopes and pH dependences on the formation of C₂₊ and CH₄.^86,87 They found that their different rate-limiting steps lead to distinctive potential dependent Tafel slope and pH effects on C₂₊ and CH₄ formation, as shown in Fig. 3b and c, respectively. Clearly, C₂₊ formation at low overpotentials (>−1.4 V vs. standard hydrogen electrode (SHE)) was limited by the first proton–electron transfer to the *OCCO using H₂O as proton source at pH = 7 and 13, while the corresponding RDS was shifted to CO dimerization at high overpotentials with increased Tafel slopes. In contrast, H₃O⁺ was the predominant proton donor and CO–CO dimerization was the RDS throughout the considered potential range at pH = 3. For CH₄ formation at pH = 13, the RDS shifted from the *CH–OH formation with a Tafel slope of 28 mV dec⁻¹ at low overpotentials (>−1.45 V vs. SHE) to the *CO–H formation with a Tafel slope of 96 mV dec⁻¹ at high overpotential. Reducing the electrolyte pH led to a change of RDS to the first protonation step using H₃O⁺ as the proton donor through the investigated potential range.⁸⁶ Given the challenge of large uncertainties in calculated rates, the theoretical models need to be verified by experimental results.

Fig. 3 (a) Product distributions of CO electroreduction on Cu(100) under different bulk pH conditions. Reproduced with permission.⁸⁴ Copyright 2021, from RSC Publishing. (b) Simulated current densities using a microkinetic model based on the DFT-calculated energetics on Cu(100). (c) Simulated current densities using a microkinetic model based on the DFT-calculated reaction energetics on Cu(211). Reproduced with permission.⁸⁶ Copyright 2022, American Chemical Society.

2.3 Theoretically calculated kinetic parameters

Conventionally, the microkinetic analysis helps to obtain three important physical parameters: the Tafel slope, reaction order, and KIE (hydrogen/deuterium). The Tafel slope (b) has been empirically defined in eqn (1):

η = a + b log(j) (1)

which relates reaction rates (j) to electrochemical driving force (η). Meanwhile, simple charge-transfer redox reaction kinetics (eqn (2), where O and R denote oxidant and reductant, respectively) are described using the Butler–Volmer eqn (3):⁸⁸

O + ne⁻ ↔ R (2)

where k⁰_b and k⁰_f denote the back and forward rate constant, respectively; a_O and a_R are the concentrations of oxidant and reductant, respectively; α is the transfer coefficient, F is the Faraday's constant, R is the universal gas constant, and T defines the absolute temperature. Eqn (4) and (5) are derived from eqn (3) at equilibrium conditions and high overpotential conditions, respectively:Hence, the empirical Tafel slope can be obtained by combining eqn (1), (4) and (5) based on the mean-field approximation. Based on the theoretical understanding of electrocatalytic CO₂ reduction, all possible elementary steps were simplified in Fig. 4, with assumptions that the intermediate surface coverage is potentially independent and the symmetry factor is 0.5 or 0 when the RDS involves one electron transfer or not, respectively, the corresponding Tafel slopes were theoretically calculated, as shown in Tables 1–3.

Fig. 4 The scheme of all possible reaction pathways for various products in electrocatalytic CO₂ reduction. Formation of (a) CO, (b) COOH⁻, (c) CH₄, and (d) C₂₊.

Table 1 Reaction kinetic parameters under the assumption of various rate-determining steps (RDSs) for CO evolution during electrocatalytic CO₂ reduction

Proton donor		RDS	Tafel mV dec^−1^a		Reaction order			KIE
					H^+	P CO2	c HCO3^−
a M: active site; a: assuming α = 0.5; T = 298 K.
H2O	Type 1	CO2 + e^− + M → M-CO2^− (a1)	2.3RT/F(α−1)	118	0	0–1	0	N
		M-CO2^− + H2O → M-COOH + OH^− (a2)	2.3RT/F	59	0			Y
		M-COOH+ e^− → M-COOH^− (a5)	2.3RT/F(α−2)	39	1			N
		M-COOH^− → M-CO+ OH^− (a8)	2.3RT/2F	30	1			Y
		M-CO → CO + M (a9)	2.3RT/2F	30	2			N
	Type 2	CO2 + H2O + e^− + M → M-COOH + OH^− (a3)	2.3RT/F(α−1)	118	0			Y
		M-COOH + H2O → M-COOH–H^+ + OH^− (a4)	2.3RT/F	59	1			Y
		M-COOH–H^+ + e^− → M-CO+ H2O (a7)	2.3RT/F(α−2)	39	2			N
		M-CO → CO + M (a9)	2.3RT/2F	30	2			N
	Type 3	CO2 + H2O + e^− + M → M-COOH + OH^− (a3)	2.3RT/F(α−1)	118	0			Y
		M-COOH+ H2O + e^− → M-CO + H2O + OH^−(a6)	2.3RT/F(α−2)	39	1			Y
		M-CO → CO + M (a9)	2.3RT/2F	30	2			N
HCO3^−	Type 4	CO2 + e^− + M → M-CO2^− (a1)	2.3RT/F(α−1)	118	0	0–1	0	N
		M-CO2^− + HCO3^− → M-COOH + CO3^2− (a2)	2.3RT/F	59	1		1	Y
		M-COOH + e^− → M-COOH^− (a5)	2.3RT/F(α−2)	39	1		0	N
		M-COOH^− + HCO3^− → M-CO + H2O + CO3^2− (a8)	2.3RT/2F	30	2		1	Y
		M-CO→ CO + M (a9)	2.3RT/2F	30	2		−2	N
	Type 5	CO2 + HCO3^− + e^− + M → M-COOH + CO3^2− (a3)	2.3RT/F(α−1)	118	1		1	Y
		M-COOH + HCO3^− → M-COOH–H^+ + CO3^2− (a4)	2.3RT/F	59	1		1	Y
		M-COOH–H^+ + e^− → M-CO+ H2O (a7)	2.3RT/F(α−2)	39	2		0	N
		M-CO → CO + M	2.3RT/2F	30	2		−2	N
	Type 6	CO2 + HCO3^− + e^− + M → M-COOH + CO3^2− (a3)	2.3RT/F(α−1)	118	0		1	Y
		M-COOH + HCO3^− + e^− → M-CO + H2O + CO3^2− (a6)	2.3RT/F(α−2)	39	1		1	Y
		M-CO → CO + M	2.3RT/2F	30	2		−2	N
Mixed	Type 7	CO2 + HCO3^− +H2O + 2e^− → CO + OH^− + CO3^2− +H2O					0.5

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Table 2 Reaction kinetic parameters under the assumption of various rate-determining steps (RDSs) for CH₄ formation during electrocatalytic CO reduction

Proton donor		RDS	Tafel mV dec^−1^a		Reaction order		KIE
					H^+	P CO
a M: active site; a: assuming α = 0.5; T = 298 K.
H2O	Type 1	H2O + e^− + M → M-H + OH^− (c3)	2.3RT/F(α−1)	118	0	0–1	Y
		M-H + M-CO → M-CO(H) +M (c4)	2.3RT/F	59	1
		M-H +M-CO(H) → M-CO(H)2 + M (c5)	2.3RT/2F	30	2
		H2O + e^− + M-CO → M-CO(H) + OH^− (c1)	2.3RT/F(α−1)	118	0
		H2O + e^− + M-CO(H) → M-CO(H)2 + OH^− (c2)	2.3RT/F(α−2)	39	1
H^+	Type 2	H^+ + e^− + M → M-H (c3)	2.3RT/F(α−1)	118	1
		M-H + M-CO → M-CO(H) +M (c4)	2.3RT/F	59	1
		M-H +M-CO(H) → M-CO(H)2 + M (c5)	2.3RT/2F	30	2
		H^+ + e^− + M-CO → M-CO(H) + OH^− (c1)	2.3RT/F(α−1)	118	1
		H^+ + e^− + M-CO(H) → M-CO(H)2 + OH^− (c2)	2.3RT/2F	30	2

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Table 3 Reaction kinetic parameters under the assumption of various rate-determining steps (RDSs) for C₂₊ formation during electrocatalytic CO reduction

	RDS	Tafel mV dec^−1^a		Reaction order		KIE
				H^+	P CO
a M: active site; a: assuming α = 0.5; T = 298 K.
Type 1	M-CO + M-CO + e^− → M-C2O2^− +M (d1)	2.3RT/F(α−1)	118	0	0–2	N
	M-CO + CO(g) + e^− → M-C2O2^− +M (d2)	2.3RT/F(α−1)	118	0	0–2	N
	H2O + e^− + M-CO → M-CO(H) + OH^− (d3)	2.3RT/F(α−1)	118	0	0–2	Y
Type 2	H^+ + e^− + M → M-H (d3)	2.3RT/F(α−1)	118	1	0	Y
	H^+ + e^− + M-CO → M-CO(H) (d4)	2.3RT/F(α−1)	118	0	0–2	Y
	M-CO + e^− + M-CO(H) → M-C2O2(H)^− (d5)	2.3RT/F(α−2)	39	1	0–2	Y
	M-CO(H) + e^− + M-CO(H) → M-C2O2 (H)2^−	2.3RT/F(α−2)	39	1	0–2	Y
	M-H + M-CO → M-CO(H) +M (d4)	2.3RT/2F	59	1	0–1	Y
	M-H +M-CO(H) → M-CO(H)2 + M	2.3RT/2F	59	2	0–1	Y

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The derivation of electrochemical reaction orders is very complex, particularly for conditions under Langmuir or Temkin isotherm governing the adsorption of electrochemically formed intermediates (e.g., *CO₂, *CO, *CH_x, *H) in electrocatalytic CO₍₂₎ reduction. Recently, Baz et al. extended the concept of “degree of rate control” (X_RC,i) to electrochemistry, indicating that the X_RC,i in electrochemistry is the weighting factor quantitating the number of electrons transferred to generate each intermediate or product species, which is significantly related to the coverages of intermediates and local microenvironment.⁸⁹ Then, the experimentally apparent reaction order with respect to reactant species j (δ_j) is a function of the generalized X_RC,i, and the corresponding expression is as follows:

where is the stoichiometric coefficient to form intermediate or product species i from reactant j. For further details regarding the degree of rate control, readers are referred to previous reviews.^90–93 Herein, we just show the range of reaction order values when assuming different RDSs due to the complexity, as shown in Tables 1–3.

Therefore, the RDS can be identified by comparing the experimentally-obtained and theoretically calculated results. However, Tables 1–3 clearly show that different RDSs could give the same Tafel slopes. To provide deeper insight into reaction pathways, the reaction orders with respect to pH, c_HCO3⁻, and P_CO(2) are needed to further support the proposed reaction mechanism. For example, the proton donor type could be distinguished by combining the reaction orders with respect to pH and c_HCO3⁻. Moreover, the isotopic effect could serve as valuable supplementary information in proposing RDS, where the KIE is not unity if the proton was involved (detailed discussions in next section).

3. Microkinetic analysis of diverse experimental results in electrocatalytic CO₍₂₎ reduction

In the past decades, the quantitative measurements of kinetic observables (e.g., Tafel slope and reaction order) were performed in electrocatalytic CO₍₂₎ reduction, which was utilized to screen the electrocatalysts and interpret the corresponding reaction pathways. However, great discrepancies often arise in interpreting the experimental kinetic results, related to the complexity of electrocatalytic CO₍₂₎ reduction occurring on the electrode. This section systematically reviews the experimentally obtained kinetic observables, the proposed RDSs, and reaction mechanisms over various electrocatalysts in electrocatalytic CO₍₂₎ reduction (Tables 4 and 5). The analysis of Tafel slopes and reaction orders will be discussed, and reasonable explanations of the discrepancies in the literature will be analyzed. Later, the discussions were further extended to KIE and temperature-sensitivity analysis. It should be explicitly mentioned that the macroscopic observables in the literature including Tafel slope, reaction order, isotopic effect, and activation energy are “apparent”, which does not have to reflect the intrinsic kinetics of electrocatalysts. Their values are usually convoluted by non-kinetic effects, electrolyte effects, mass transport, different proton donors, homogeneous reactions, and bubbles. Hence, the macroscopic observables in this review represent the apparent ones unless otherwise specified. More detailed discussions can be found in the following section.

Table 4 Reported Tafel slopes, reaction orders, and proposed RDSs in electrocatalytic CO₂ reduction to CO over various electrocatalysts at different conditions

No.	Catalyst	Electrolyte	Potential range (V vs. RHE)	Tafel mV dec^−1	Proton	Reaction orders cHCO3^−	RDS	Ref.
Note PC: polycrystalline; CoQC: cobalt quaterpyridine complexes; Ni–N3-NCNFs: N atoms and coordinatively unsaturated Ni–N3 moieties co-anchored carbon nanofiber; c1: electrolyte 0.04–0.1 M NaHCO3 for reaction order of HCO3^−; c2: electrolyte 0.1–1.0 M NaHCO3 for reaction order of HCO3^−; LRC: local reaction environment control.
1	Au-needles	0.5 M KHCO3	−0.2 to −0.3	42			a5	94
	Au-rods		−0.3 to −0.5	80			a1
	Au-particles		−0.3 to −0.5	96			a1
2	OD-Au	0.5 M NaHCO3	−0.2 to −0.3	56	HCO3^−	0.96	a2	95
	PC-Au		−0.4 to −0.6	114			a1
3	Au foil	1.0 M NaHCO3	−0.3 to −0.4	56	HCO3^−	0.9^c1	a4	26
	Au foil		−0.4 to −0.43	134			a1
4	PC-Au	0.1 M KHCO3	>−0.3	42	H2O	0	a6	32
	PC-Au		<−0.3	101		0	a1
5	Cl-modified Au	0.2 M KHCO3	−0.3 to −0.5	61			a2	98
	CN-modified Au		−0.35 to −0.5	45			a2
6	PC-Ag	0.5 M KHCO3	>−0.5 V	67	H2O	0	a2	26
7	PC-Ag	0.5 M NaHCO3	−0.6 to −0.9	134	H2O/HCO3^−	0.5^c2	a1	31
	Nanoporous Ag		−0.2 to −0.4	58			a4
	Ag Nanoparticle		−0.3 to −0.5	64			a4
	PC-Ag	0.1 M KHCO3	>−0.4	51	H2O	0	a4	97
8			−0.65 < <−0.4	138		0	a1
			<−0.65	163		0	a1 + LRC
9	PC-Ag	0.1 M KHCO3	−0.2 to −0.6	157	H2O	0	a1	99
10	Molecular film@Ag	0.1 M KHCO3	−0.3 to −0.6	91	HCO3^−	1	a3
11	Ag3Au	0.1 M KHCO3	−0.2 to −0.3	48	H2O	0	a6	100
12	Au6Ag2Cu2	0.5 M KHCO3	−0.2 to −0.3	94			a1	101
13	CoPc/CNT	0.1 M NaHCO3	−0.5 to −0.7	118	H2O	0.17	a1	102
	CoPc/CNT	1 M NaHCO3			HCO3^−	1.4	a3
14	CoPc/CNT	1 M NaHCO3	−0.4 to −0.6	120	H2O	0	a1	34
15	Co-QC	0.1 M KHCO3	−0.2 to −0.35	119	H2O	0	a1	103
16	Ni-TAPc	0.5 M KHCO3	−0.4 to −0.6	72	HCO3^−	0.8	a2	104
17	ZrO2@NiNC	0.5 M KHCO3	−0.4 to −0.5	72	H2O		a2	105
18	Ni–N–C	0.5 M KHCO3	−0.5 to −0.6	132		0	a1	106
	Ni–N3-NCNFs		−0.3 to −0.6	71			a2
19	Sn doped Cu	0.1 M KHCO3	−0.3 to −0.5	109	HCO3^−	1	a3	107
		0.3 M KHCO3		112
		0.7 M KHCO3		116
20	PC Sn	1 M NaHCO3	−0.5 to −0.8	116			b1	108
21	In2S3	0.5 M KHCO3	−0.6 to −0.7	233	H2O		b1	109
	AgIn5S8			77.4			b2
	AgInS2			74			b2

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Table 5 Summary of experimentally kinetic observables over Cu-based electrocatalysts in electrochemical CO reduction to C₂₊ products

No.	Cell	P CO(kPa)	Kinetic order to PCO					pH dependence	Tafel	Ref.
			C2+	C2H4	C2H5OH	Acetate	n-PrOH	C2+	mV dec^−1
Note: Cu-based GDE was examined in a gas-fed flow cella High-pressure H-type cell.
22	H-Cell									57
23	H-Cell	<50	2	1	0			No	118	134
		≥50	2	2	0
24	H-Cell	<60	Positive	Positive	Positive	Positive	Positive		118	135,136
		≥60	0	0	0	0	0
25	H-Cell	<60	Positive	1	Positive	Positive	Positive		118	137
		≥60	0	0	0	0	0
27	GDE Cu70Pd30	<63				0.5			118	138
		≥63				0			118
	GDE^2 Cu49Pd51	<25				1			124
		≥25				0
	H-cell^a	>1010	0.82	0.5	0.87					139
28	GDE	≥30				0.58			118	140
		<30				0.86
29	GDE	<20.2	1.1	1.1	1.1	0.6 ± 0.2	0.7 ± 0.2		120
		≥20.2	0	−0.2	−0.3	0.6 ± 0.3	0.7 ± 0.3

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3.1 Summary of Tafel slopes and reaction orders for CO evolution over various electrocatalysts in the literature

3.1.1 Experimentally obtained Tafel slopes of CO evolution. Beginning with the simplest two-electron transfer for CO evolution in electrocatalytic CO₂ reduction, Table 4 summarizes the reaction conditions, kinetic observables, and proposed RDSs over TMs, molecular complexes, and MNC_S. The as-obtained Tafel slopes and reaction orders are rather scattered due to different experimental conditions and nano-structured electrocatalysts. For example, the reported Tafel slopes for CO evolution on Ag and Au clearly showed various values from ca. 50 to 140 mV dec⁻¹, yielding different RDSs. Sargent and co-workers examined different nano-structured Au in electrocatalytic CO₂ reduction toward CO using conventional H-type cell and different Tafel slopes of 42 mV dec⁻¹, 80 mV dec⁻¹, and 96 mV dec⁻¹ were obtained on Au needles, rods, and particles, respectively (No. 1).⁹⁴ The RDS was proposed to change from the first-electron transfer step on Au particles to the second-electron transfer step on Au needles, and the enhancement of electron transfer was ascribed to the sharp-tip enhancement effect.⁹⁴

The Chan group also reported similar results that a lower Tafel slope of close to 59 mV dec⁻¹ on oxide-derived Au electrodes was obtained than that of ca. 118 mV dec⁻¹ on polycrystalline (PC) Au foil, resulting from more stabilization of proposed *CO₂⁻ intermediate on oxide-derived (OD) Au surface (No. 2).⁹⁵ However, the Xu group reported that even bulk PC-Au electrode also could deliver a Tafel slope of ca. 59 mV dec⁻¹ at sufficiently low overpotentials (>−0.4 V vs. RHE), and an obvious transient was observed at −0.4 V vs. RHE, ascribed to the mass transport limitations (No. 3).²⁶ Recently, Chan and co-workers revisited the long-standing controversy surrounding the RDS on Au and developed multi-scale modeling integrating ab initio microkinetic kinetics, mass transport simulations, and the effect of charged electric double layer (No. 4) (Fig. 5a).³² Various experimentally reported Tafel slopes were ascribed to the adsorption of CO₂ modulated by the potential dependence of the surface-charge density and the corresponding electric double-layer field, where CO evolution was separately limited by the conversion of *COOH to *CO at very low overpotentials, CO₂ adsorption induced by the double-layer charging at higher overpotentials, and then CO₂ transportation limitation at <−1.0 V vs. SHE, in agreement with the findings in the literature (Fig. 5b).^33,35,95,96

Fig. 5 (a) Schematic illustration of the multi-scale system. (b) Multi-scale CO₂ electroreduction simulation results over Au compared with experiments. Reproduced with permission.³² Copyright 2020, Nature publishing group. (c) Schematic illustration of the model system. (d) Multi-scale CO₂ electroreduction simulation results over Ag compared with experiments. Reproduced with permission.⁹⁷ Copyright 2021, American Chemical Society.

Similarly, various Tafel slopes were reported over Ag-based electrocatalysts in the literature (No. 6–10) (Table 4). Zhu et al. developed a hierarchical model including intrinsic reaction kinetics, specific surface charging state at a given electrode potential, and mass transport effects to elucidate the reaction mechanism and kinetics of CO₂ electroreduction to CO on Ag electrode (No. 8) (Fig. 5c).⁹⁷ This study highlighted that the local reaction environment could significantly affect the experimental results and perturb the reaction mechanism. The scattered Tafel slopes in the literature were explained to be affected by the surface charging relation and mass transport effects (Fig. 5d). In addition, several reasons were proposed to decrease the reaction rate of CO evolution at high overpotentials, including the decreased CO₂ concentration, increased pH, surface charge effects, and lateral interactions between *HCOO, *COOH, and *H. This model well explained the reported Tafel slopes in other studies.^110–113 It is worth noting that rigorous analysis of the catalytic activity normalized to the electrochemical surface area (ECSA) likely suggested that nanostructuring does not significantly alter the intrinsic activity of the active sites.¹¹⁴ The as-obtained kinetic observables and reaction rates varied in the literature since they were affected not only by the intrinsic kinetics but also by the local reaction environments.

Additionally, the reaction rate of electrochemical CO₂ reduction is significantly affected by the adsorption energies of reaction intermediates,¹¹⁵ which are typically correlated through thermodynamic scaling relations.¹¹⁶ Strategies to break linear scaling relations and regulate reaction pathways are highly desired to tune the activity and selectivity,^117–119 for example, introducing additive modifications to electrode surfaces (e.g., molecular complexes or anion species) and alloying multi-metallic electrocatalysts.^{101,120–125} For example, the Tafel slopes of CN and Cl modified Au electrodes in electrocatalytic CO₂ reduction to CO were 45 and 61 mV dec⁻¹, respectively, lower than that of bare Au (ca. 120 mV dec⁻¹), indicating the shifted RDSs in the presence of adsorbed anion (No. 5).⁹⁸ Peters and co-workers introduced pyridinium-based additives on the PC-Ag surface, which exhibited a lower Tafel slope of 91 mV dec⁻¹ than that of bare PC-Ag, likely due to the suppression of proton but not CO₂ mass transport in electrochemical CO evolution (No. 10).⁹⁹ A similar improvement was reported over ethylenediamine tetramethylenephosphonic acid (EDTMPA) modified Pc-Cu for electrocatalytic CO₂ reduction to CH₄, where a reduced Tafel slope from 144 to 87 mV dec⁻¹ in the presence of EDTMPA was observed. Breaking linear scaling relations between the two coupled intermediates (*CO and *CHO) was confirmed by Tafel analysis and theoretical calculation of binding energy and free energy change, thus improving the kinetics of CH₄ formation.¹²⁶ In addition, the changed Tafel slopes over alloy electrocatalysts were also demonstrated on Ag and Au-based alloys in electrocatalytic CO₂ reduction to CO (No. 11 and No. 12).^100,101

For molecular complexes and atomically dispersed electrocatalysts (MNCs), the Tafel analysis is also widely utilized to elucidate their RDSs in electrocatalytic CO₂ reduction. For example, the heterogenized CoPc or CoTPP delivered a Tafel slope of 120 mV dec⁻¹ at low overpotential, indicating a slow first electron transfer as the RDS in electrocatalytic CO₂ reduction to CO (No. 13 and 14).^{34,102,127–129} Then, pyridine-functionalized cobalt complexes on a carbon substrate could reduce the Gibbs free energy of CO₂ activation further than its counterpart ones and exhibited smaller Tafel slopes, facilitating charge transfer and increasing the activity of CO evolution (No. 15).¹⁰³ Moreover, Zheng et al. reported that the electronegative N atoms and coordinatively unsaturated Ni–N₃ moieties co-anchored carbon nanofiber (Ni–N₃-NCNFs) catalyst exhibited the positive effect on facilitating CO₂ adsorption and H₂O dissociation steps, thus accelerating the reaction kinetics with a lower Tafel slope of 71 mV dec⁻¹ (No. 18).¹⁰⁶ The presence of ZrO₂ next to the atomically dispersed Ni–N₄ sites also could accelerate H₂O activation, resulting in a lower Tafel slope (ca. 72 mV dec⁻¹) (No. 17).¹⁰⁵

It is worth noting that since the Tafel slope is not sensitive to the variance of the proton donor type, the interpretation of RDS and reaction mechanism still needs more evidence, like other kinetic parameters, operando spectroscopies, or theoretical calculations.

3.1.2 Experimentally obtained reaction orders for CO₂ electroreduction. Beyond the Tafel slope, determining reaction orders is an important part of the microkinetic analysis concerning mechanistic interpretation. Especially, reaction order with respect to pH and c_HCO3⁻ (x) could distinguish a coupled or decoupled elementary proton-electron transfer mechanism in electrocatalytic CO₂ reduction. However, in Table 4, various reaction orders with respect to c_HCO3⁻ on the same electrolyte could be observed, which leads to different conclusions on the RDS and the roles of bicarbonate. For example, x = 0,^97,130 0.4,⁹⁷ and 0.5^31,33 were reported on PC-Ag; x = 0,²⁶ and 1^33,95 were obtained on PC-Au and Au nanoparticle, respectively; x = 0,³⁴ 0.1,¹⁰² and 0.6¹⁰² were delivered on CoPc/CNT. These studies lack agreement regarding the bicarbonate order dependence, impeding the rational catalyst design.

The obvious discrepancies largely arise from the different local reaction microenvironments and misleading interpretations. It is worth noting that the HCO₃⁻ can be involved in homogeneous reactions viaeqn (7) and (8) under electrochemical reaction conditions.

CO₂ + OH⁻ ⇄ HCO₃⁻ (7)

HCO₃⁻ + OH⁻ ⇄ CO₃²⁻ + H₂O (8)

Hence, the local reaction microenvironments at the electrode surface change when varying c_HCO3⁻, including local pH and cation concentration. The cation identity and concentration have been demonstrated to significantly impact on the catalytic performance of electrocatalytic CO₂ reduction due to the interfacial electric and nonelectric field components.^131–133 Moreover, the changed local pH will lead to different values of as-obtained reaction order with respect to c_HCO3⁻ on SHE and RHE scales. More specifically, the applied potential in RHE changes since the local concentration of c_H+ varies and their relationship is applied to the Nernst eqn (9).For example, when c_HCO3⁻ is increased from 0.1 M to 1.0 M at fixed ionic strength, the solution pH will shift from 6.8 to 7.7 and the corresponding driving force will increase by ca. 59 mV when neglecting the surface charging effect. Eikerling and co-workers rigorously investigated the dependence of c_HCO3⁻ on PC-Ag in electrocatalytic CO₂ reduction to CO, and the same ionic strength of electrolytes containing different c_HCO3⁻ was prepared by changing the ratio of KHCO₃ and KClO₄.⁹⁷ The j_CO was found to remain constant with varied c_HCO3⁻ irrespective of overpotential on SHE scale, as shown in Fig. 6a. However, the rate was sensitive to c_HCO3⁻, exhibiting positive orders (ca. 0.4) on c_HCO3⁻ at −0.7, −0.8, and −0.9 V vs. RHE (Fig. 6b), in agreement with the theoretical value (0.5), indicating HCO₃⁻ was involved in CO₂ reduction as proton source. These results revealed that misleading interpretations could yield contradictory conclusions even when rigorously performed experiments.

Fig. 6 Bicarbonate order dependence of electrocatalytic CO₂ reduction to CO over PC-Ag under fixed ionic strength and at (a) −1.1, −1.2, −1.3 V vs. SHE; (b) at −0.7, −0.8, −0.9 V vs. RHE; and (c) at −1.5 and −1.6 V vs. SHE. (d) Concentration distributions of CO₂ in solutions with 0.05, 0.1, and 0.5 M bicarbonate at −1.6 V vs. SHE. Reproduced with permission.⁹⁷ Copyright 2021, American Chemical Society.

In contrast to low overpotentials, the concentration of bicarbonate varied the CO evolution rate at higher overpotentials. In Fig. 6c, the j_CO increased as c_HCO3⁻ increased with a positive slope on an even SHE scale and the slope was larger when the overpotential was higher in the presence of mass transportation limitation. This enhancement was ascribed to bicarbonate contributions as buffer species to maintain the local pH and suppress the eqn (7) to consequently enhance the local CO₂ concentration, as shown in Fig. 6d.⁹⁷ Consistently, electrocatalytic CO₂ reduction over Co complexes exhibited positive and zeroth orders kinetics on bicarbonate on SHE scale in H-cell¹⁰² and gas-fed flow cell (irrespective of overpotential),³⁴ respectively, where mass-transfer limitation was circumvented due to the construction of three-phase interface in gas-fed flow cell under reaction conditions.

Following up on this point, the differences in the local reaction environment can explain the contradicting results of bicarbonate order dependence in the literature. For example, the reported bicarbonate dependences on PC-Ag were 0, and 0.5 in different studies, which were caused by using different potential scales. The first-order dependence on PC Au and Au nanoparticles likely resulted from the combined effects of different potential scales and mass transportation effects. It also should be noted that phosphate was selected to maintain the ionic strength, which may lead to specific adsorption of buffering anions to poison the CO evolution on PC-Au and promote the H₂ evolution.^33,130 Additionally, some abnormal values appeared when the experiments were conducted in electrolytes containing different c_HCO3⁻ with different ionic strength.^102,107

3.2 Tafel and reaction order analysis of electrocatalytic CO reduction over Cu

As described in Section 2, CO is a key and necessary intermediate during electrocatalytic CO₂ reduction to CH₄ and C₂₊ compounds.^57,141 Moreover, CO can alleviate the complexity of the multiple equilibrium reactions between CO₂ and aqueous electrolytes. Hence, the development of microkinetic analysis of electrochemical CO reduction has attracted tremendous attention in recent years, which can be extended to the rational design of efficient catalysts in electrocatalytic CO₂ reduction. This section reviews the studies on the Tafel and reaction order analysis for electrochemical CO reduction to C₂₊ and CH₄ formation, focusing on the reaction order with respect to P_CO (Table 5), where significant effects of reactor configuration on the reaction order measurements will be discussed.

A pioneering work was reported by Hori et al. in 1997, which demonstrated the efficient electrochemical CO reduction to CH₄ and C₂₊ on Cu in a H-type Cell and disclosed their molecular-level reaction pathways with regard to microkinetic analysis (No. 22). Their study revealed that the reaction rates of C₂H₄ and C₂H₅OH were insensitive to pH of electrolyte, where H₂O as the proton source for C₂H₄ and C₂H₅OH formation was proposed. In contrast to C₂H₄ and C₂H₅OH formation, the reaction rate of CH₄ was proportional to proton activity and followed the Tafel relationship. However, their Tafel slopes were abnormal (>118 mV dec⁻¹), likely arising from the mass transportation limitation. The different transfer coefficients (0.35 for C₂H₄ and 1.33 for CH₄) indicated the reaction paths of CH₄ and C₂H₄ formations were separated at an early stage of CO reduction.⁵⁷ Then, Lu and co-workers examined the rigorous electrokinetic analysis by employing a PTFE modified electrode to reduce the CO mass transport limitation in electrocatalytic CO reduction (No. 24 and 25). Based on the measured Tafel slopes (118 mV dec⁻¹ for both C₂₊ and each product) and reaction orders with respect to P_CO (first-order dependence for C₂₊ products) at P_CO < 60 kPa, they proposed that the formation rates of C₂₊ products are most likely limited by the hydrogenation of CO with adsorbed water in H-cell.^135–137 Moreover, the microkinetic analysis by integrating pH-dependent CH₄ formation rate, shifted Tafel slopes at different electrolytes with varied pH, and reaction orders with respect to P_CO suggested that the reaction rate of CH₄ was limited by the CO hydrogenation step via a PCET in weakly alkaline electrolytes (e.g., 7 < pH < 11) and a chemical hydrogenation step of CO by adsorbed hydrogen atom at pH > 11, respectively.^135,136 Additionally, Schreier et al. investigated the dynamics of CO on the Cu surface, providing deeper insight into the mechanism for the production of H₂, CH₄, and C₂H₄. It should be noted that these experiments were performed at low temperatures in an ethanol medium (−35 °C) to increase CO solubility and surface-affinity. Those findings showed that the reaction rate of C₂H₄ was insensitive to P_CO, concluding that C₂H₄ was likely formed via *CO dimerization on a *CO saturated surface; the reaction rate of CH₄ was suppressed by increasing P_CO, which was interpreted by the competition between *H and *CO on Cu surface to govern the product distributions.¹⁴²

Recently, Kastlunger et al. experimentally performed the rigorous Tafel analysis at a wide range of pH values (3–13) in conjunction with constant-potential DFT kinetics to analyze the RDS in CO electroreduction on Cu. Their measured current densities were in agreement with the literature data, as shown in Fig. 7.⁸⁶ The as-obtained partial current densities of C₂₊ products at pH 3 exhibited slightly decreased due to the specific adsorption of buffering anions (Fig. 7a), which could block active sites and/or compete with CH₄ and H₂ production.¹⁴³ The experimentally obtained Tafel slopes of C₂₊ (Fig. 7a) were consistent with the theoretically derived slopes assuming *CO dimerization as the RDS, where the potential response was considered. Contrary to C₂₊ formation, the partial current densities of CH₄ exhibited pH-dependent activity and did not overlap for these three pH values on the SHE scale, suggesting that the proton donor was different. By analyzing Tafel slopes (ca. 40–90 mV dec⁻¹), at lower pH values of 3 and 7, the first PCET step was proposed as the RDS for CH₄ formation; at pH = 13, and the second PCET step was identified as RDS, consistent with Hori's results.^54,57

Fig. 7 The partial current densities of (a) C₂₊ products and (b) methane at pH 3, 7, and 13 on PC–Cu foil. Reproduced with permission.⁸⁶ Copyright 2022, American Chemical Society.

Notwithstanding the conflicting results provided by microkinetic analysis, some proposed RDSs can be excluded easily. For example, the hydrogenation of *CO with H₂O¹³⁶ cannot be the RDS for C₂₊ formation since the j_C2+ were undoubtedly demonstrated to be insensitive to pH at ambient pressure and temperature. Only the Tafel slope of 118 mV dec⁻¹ and the first reaction order with respect to P_CO could not yield exclusive RDS in CO electroreduction. For CH₄ formation, the findings in ethanol media at low temperatures could not be extended to the aqueous conditions at room temperature, where a negligible *H coverage is expected on Cu since H₂ evolution on Cu¹⁴² is limited by the Volmer step. In this context, the competition between *H and *CO over Cu for CH₄ production seems unlikely in aqueous conditions. Moreover, the chemical hydrogenation step of *CO by *H on Cu at (pH > 11) was also proposed as RDS.¹³⁶ If so, the current–voltage relationship should overlap on the RHE scale, inconsistent with the experimental results.⁸⁶ Indeed, a significant influence of the local reaction environment has been emphasized for the catalytic performance evaluation of CO₍₂₎ electroreduction.¹⁴⁴ The activity and selectivity of electrocatalysts change significantly as a function of reaction rate due to local pH and CO₍₂₎ availability. It should be noted that all these studies were performed using a conventional aqueous H-cell, which fails to achieve an industrially relevant reaction rate because of the mass-transport limitations. Consequently, the proposed reaction mechanism may be interpreted wrongly due to the operating conditions.

Accordingly, performing a microkinetic assessment in a cell configuration with improved mass transfer flux is essential to understand and design an electrocatalyst. In recent work, an in-depth microkinetic investigation assessed under commercially relevant conditions was performed over Cu using a gas-fed flow cell.¹⁴⁵ Consistently, the reaction rates of C₂₊ products (e.g., C₂H₄, C₂H₅OH, and n-PrOH) over Cu exhibited zeroth orders kinetics on H⁺ and OH⁻ irrespective of P_CO and reactor configuration, indicated that water was used as the proton donor.^{57,134–136,142} Regarding the effect of P_CO, j_C2+ increased with increased P_CO below about 20.2 kPa with a slope of about 1, while above this partial threshold pressure, the rate remained unchanged, indicating that the formation of C₂₊ products shared some early intermediates. Moreover, this overall j_C2+–P_CO relationship was deconvoluted to each product, showing that higher P_CO favored n-PrOH and acetate at the expense of C₂H₄ and C₂H₅OH. This transition in the reaction orders was not observed in previous studies employing H-cells,^{57,134–136,142} most likely due to the distinct local reaction environment. The opposite P_CO-dependent trends for different C₂₊ products suggest that the plausible scenario was a shared RDS for all C₂₊ products. Meanwhile the selectivity-determining steps would result in various apparent reaction orders for different C₂₊ products. Overall, the electrokinetic results integrating the Tafel slope (ca. 120 mV dec⁻¹), reaction orders concerning pH and P_CO, and other kinetic observables suggested that the formation of C₂₊ was likely limited by the formation of M-CO(H)-CO⁻ species with the surface site predominantly comprising M-CO(H) and the product distribution was perturbed by coverage of *CO as a key intermediate at selectivity-controlling steps after the RDS.¹⁴⁵

Regarding the local reaction microenvironment created in different reactor configurations, four aspects must be considered. First, the CO availability on Cu is significantly different in conventional H-type cell and GDE-based electrolyzers. In a H-type cell, CO is supplied to Cu surface that was fully or partially immersed in a liquid electrolyte, while CO could be provided via the gas-phase using a gas-diffusion layer where a gas–liquid–solid interface is constructed. There is a roughly 3-order of magnitude reduction in CO diffusion pathways to the surface of the catalyst: from ca. 50 μm (H-cell) to 50 nm (GDE), resulting in different CO coverage.¹⁴⁴ It was reported that the surface coverage of CO on Cu affected the binding energy of CO and reaction intermediates due to adsorbate–adsorbate interactions, especially under higher coverages.^146,147 For the chemisorption of CO on Cu(111) at low temperatures, the initial heat of adsorption of 50 kJ mol⁻¹ was maintained up to one-third coverage and then dropped abruptly to 38 kJ mol⁻¹.^147,148 Second, local pH near the Cu surface varies significantly at low current density and industrially relevant conditions. Although the overall reaction rate of CO electroreduction showed less dependence on pH, it could result in altered energetics of reaction intermediates or reaction pathways as a function of current density. For example, the partial current density for acetate experienced a significant enhancement at elevated OH⁻ concentration during CO electroreduction.^149,150 It was also reported that local pH affected the adsorption configuration of reactants and intermediates (e.g., flat or vertical mode), resulting in different product distributions in electrocatalytic system.^151,152 Third, the influence of the potential dependence on the reaction energetics, intermediate adsorption, and reaction pathways should be taken into account. The applied potentials can change the binding enthalpy and entropy of reaction intermediate through electric field effects (e.g., the surface dipole moment or polarizability).¹⁵³ For example, the previous study revealed that the RDS on gold was *COOH to *CO at low overpotentials and CO₂ adsorption at intermediate overpotentials.³² Finally, it should be noted that the solvation effect (e.g., hydrogen bond) and bubble formation could also affect the reaction rates and reaction intermediates.^{148,154–156} For example, the *COH intermediate could also be stabilized via hydrogen bonding between its OH functional group and a neighboring *CO adsorbate.³⁷

3.3 KIE study in electrochemical CO₍₂₎ reduction

The quantification of KIE can provide deeper insight into whether isotopes are involved in the RDS in heterogeneous catalysis (Fig. 8a and b).¹⁵⁷ Generally, the elementary steps for electrochemical CO₍₂₎ reduction fall into one of the following categories: (1) chemical step, (2) ET, (3) CPET, and (4) PT. When the RDS is CPET, PT, or chemical steps with H-containing species, the presence of isotopes in the electrolyte will vary the reaction rate of electrochemical CO₍₂₎ reduction. More specifically, KIE is usually categorized into primary KIE, secondary KIE (Fig. 8c), and inverse KIE (Fig. 8d). The cleavage of bonds containing isotopes causes primary KIE, and its magnitude is related to the difference in zero-point energy (ZPE) between the C–H and C–D bonds (Fig. 8a). The maximum KIE is predicted to be 6.5 when the target bond is 100% split at the transition state. Some experimentally obtained KIEs are lower than 6.5 due to the incomplete cleavage of the target bond or the residual ZPE difference between C–H and C–D bonds at the transition state (Fig. 8b).^158–162 Secondary KIE indicates a change in bond hybridizations (e.g., from a sp³ hybridized C atom to a sp² hybridized C atom) rather than bond cleavage, resulting in a maximum value of approximately 1.41 (Fig. 8c).^163,164 On the other hand, a reverse secondary KIE (Fig. 8d) could occur, delivering a maximum value of approximately 0.7 when the C–H bond shows a larger activation energy and a slower reaction rate than the C–D bond.

Fig. 8 (a) A scheme of KIE in C-H bond activation. (b) A scheme showing the observed KIE related to the residual ZPE. Energy diagrams showing the (c) normal and (d) inverse secondary KIEs. Reproduced with permission.¹⁵⁷ Copyright 2020, RSC publication.

3.3.1 KIE study on inorganic electrocatalysts in electrochemical CO₍₂₎ reduction. KIE analysis is widely utilized as a complementary tool to identify the RDS and key intermediates in electrocatalytic CO₍₂₎ reduction. To understand the formation of C₁ products (e.g., CO and HCOOH), Deng et al. examined the KIE with respect to the reaction rate of electrochemical CO₂ reduction over TMs and p-block metals in H-type cell.¹⁶⁵ The j_CO (Au and Ag) and j_HCOO⁻ (Sn and In) remained identical using D₂O instead of H₂O in the 0.1 M K based-electrolyte, indicating PT or CPET step was not involved in RDS. As expected, HER rates were more severely suppressed by replacing H₂O with D₂O, in agreement with the previous results.¹⁶⁶ In conjunction with the results of pH dependency studies and Tafel slopes, they concluded that the adsorption of CO₂ coupled with one electron transfer was the RDS in electrochemical CO₂ reduction. Consistently, Wuttig et al. also observed that j_CO over Au exhibited zeroth order dependence on the ratio of hydrogen/deuterium (H/D) ratio under identical conditions.³⁵ However, the inverse KIE was also obtained on Au foil at a kinetically controlled potential of −0.4 V vs. RHE in H-type cell, likely resulting from the convoluting factors (e.g., CO₂ solubility) and competing reaction (HER).²⁶ For example, CO₂ solubility is changed from 33.8 mM in H₂O to 38.1 mM in D₂O.¹⁶⁷ The local microenvironment is again highlighted as perturbing KIE measurements. To avoid the limitation of CO₂ transportation, Verma et al. performed KIE studies of CO evolution over Au in a gas-fed flow cell using the 2.0 M NaOD as a catholyte. The nearly identical j_CO was observed in both the protonated and deuterated electrolytes, suggesting that H₂O was not involved in RDS over Au, consistent with those obtained in conventional H-type cell.⁶⁴

In conjunction with the deuterium labeling method, isotope measurements could provide more evidence of the reaction mechanism. For example, Ma et al. examined the isotopic effects over sulfur-doped In catalyst using H-type cell in electrocatalytic CO₂ reduction to formate. j_HCOO⁻ in K₂SO₄/H₂O was larger than that in the K₂SO₄/D₂O system and the corresponding KIE was around 1.9, consistent with the primary KIE.¹⁶⁸ Additionally, almost 98% of the produced formate was in the form of DCOO⁻ when D₂O was used to replace H₂O in 0.5 M KHCO₃ electrolyte, suggesting the origin of H in formate coming from H₂O rather than HCO₃⁻. Taken together, these findings revealed that the dissociation of H₂O was involved in the RDS for electrochemical CO₂ reduction to formate over sulfur-doped In catalyst.¹⁶⁹

In electrochemical CO reduction to C₂₊, Chang et al. performed isotopic effects on PC–Cu in H-type cell. j_C2H4 was reduced by a factor of ca. 3.5 by switching the electrolyte from 1.0 M NaOH/H₂O to 1.0 M NaOH/D₂O, consistent with a primary KIE, concluding that H transfer was involved in the RDS for C₂H₄ formation.¹³⁷ However, Lu et al. carried out similar KIE studies on Cu using a gas-fed flow cell and found that j_C2H4 in KOH/H₂O was indeed larger than that in the KOD/D₂O system across the potential window. However the apparent KIE was only around 1.2–1.4, likely consistent with a secondary KIE, indicating a change in bond hybridizations rather than bond formation or cleavage. These results indicate that the decrease in the vibrational frequency after the bond-hybridization for a C–H bond was more significant than that for a C–D bond, leading to smaller activation energy and a faster reaction rate of C₂H₄ formation. The determined KIE (ca. 5–7) for HER perfectly agree with the literature data, giving audiences confidence in the accurate KIE of C₂H₄ being measured.¹³⁷ The difference between as-obtained KIE in the H-type and gas-fed flow cells likely resulted from the local reaction conditions. These finding could not exclude the RDS involved in hydrogen-containing species (e.g., *CO(H)). More evidence from spectroscopic studies and theoretical calculations is needed to interpret the detailed reaction pathways for CO electroreduction.

Isotopic effects have been widely utilized not only in mechanistic studies, but also in the ration design of catalysts. Wang and co-workers studied the isotopic effect on electrocatalytic CO₂ reduction to C₂H₄ over nano-structured Cu using a gas-fed flow cell. The formation rate of C₂H₄ was suppressed by the replacement of H₂O with D₂O in 1 M KOH electrolyte, and an apparent KIE of up to ca. 2 was obtained, indicating that the dissociation of H₂O was involved in the rate-determining step. Interestingly, halogen (I, Br, Cl, and F)-modified copper catalysts resulted in the decreased KIE, delivering values of 1.8, 1.5, 1.3 and 1.2 over I–Cu, Br–Cu, Cl–Cu and F–Cu catalysts, respectively. The KIE over the F–Cu catalyst close to 1 indicated that the presence of F on copper accelerated H₂O activation, which was no longer the rate-determining step over this catalyst.¹⁷⁰ Moreover, Guo et al. observed a primary KIE (ca. 2.9) in electrocatalytic CO₂ reduction to CO over layered bismuth subcarbonate (Bi₂O₂CO₃; BOC) in a gas-fed flow cell, which indicated that the O–H cleavage of water was involved in the RDS. When Ag was introduced to adjacent Bi sites (Ag/BOC), the corresponding KIE was decreased to ca. 2.5, suggesting that Ag could accelerate the dissociation of H₂O to supply reactive hydrogen species to facilitate CO₂ hydrogenation to produce formate.¹⁷¹

3.3.2 KIE study on molecular complexes in electrochemical CO₂ reduction. Apart from inorganic electrocatalysts, KIE analysis is also a powerful tool to identify the RDS and key intermediates for molecular complexes in homogeneous and heterogeneous CO₂ electroreduction. For example, Dey et al. examined the isotopic effects on cobalt pyridine thiolate complexes in homogeneous CO₂ electroreduction to formate and reported a primary KIE of 8.2, indicating that the RDS involved a proton transfer and provided indirect evidence on the formation of Co-H species.¹⁷² Khadhraoi et al. collected isotopic effects on a series of tetra-, di- and mono-substituted iron porphyrins with cationic imidazolium functions (named as 4-im-Fe, 2-im-Fe, and 1-im-Fe) in homogeneous CO₂ electroreduction to CO.¹⁷³ KIE was measured for each imidazolium derivative using H₂O or D₂O as a proton or deuterium source, exhibiting a strong inverse KIE of 0.43 over 4-im-Fe and unity KIE over 2-im-Fe (1.1) and 1-im-Fe (1). The inverse KIE over 4-im-Fe indicated a pre-equilibrium step preceding the rate-determining protonation significantly contributed to the overall kinetic of CO₂ electroreduction, likely resulting from the H₂O molecular nanoconfinement, which was reported in various ionic liquids, including those based on imidazolium groups.^174–176 These findings suggested that through-structure inductive effects and through-space electrostatic interactions could induce the change of RDS in iron porphyrins during CO₂ electroreduction. Similarly, Tignor et al. investigated the isotopic effects over various manganese electrocatalysts bearing pendant H-bond donors at different positions in CO₂ electroreduction to CO. Isotope effects associated with CO₂ electroreduction were observed over electrocatalysts substituted bipyridine ligands with phenol and anisole at the 4-and 6-positions, indicating that intramolecularly hydrogen bond perturbed both the reactive intermediates and the transition state associated with cleavage of the carbon dioxide C–O bond.¹⁷⁷

For heterogeneous molecular catalysts, McCrory and co-workers conducted KIE studies to investigate the influence of axial ligand coordination on CoPc during electrocatalytic CO₂ reduction in H-type cells. The parent CoPc exhibited identical KIE in H-based and D-based electrolytes, which was consistent with a rate-determining CO₂-coordination step.³⁴ Likewise, the unity of KIE was also observed when the CoPc was immobilized within a non-coordinating poly-2-vinylpyridine polymer (P2VP). On the contrary, the five-coordinate CoPc(py) exhibited a primary KIE of 3.1, indicating a changed RDS involved in proton transfer. Moreover, the primary KIE of ca. 2 was observed on the five-coordinate CoPc(py) immobilized within P2VP and parent CoPc immobilized within the coordinating polymer P4VP, suggesting that the PVP polymers moderated the extent of KIE for the five-coordinate CoPc systems with axially-ligated pyridyls. These findings prove that axial-coordination from the pyridyl moieties in P4VP to CoPc changes the RDS in CO₂ electroreduction, resulting in the increased activity.¹⁷⁸ Moreover, Guo and co-workers reported a primary KIE of 4 over copolymerization of CoPc supported on CNT in H-type cell in electrocatalytic CO₂ reduction to CO, indicating proton transfer was involved in RDS. Further modification with ultrathin conjugated microporous phthalocyanines (Pc) layer (CNT@CMP(H₂PCCoPc)) significantly decreased KIE to ca.1.8, signifying that the water dissociation was largely accelerated with the assistance of the H₂Pc layer. Interestingly, the as-prepared CNT@CMP(H₂PC-CoPc) enabled a maximum of 97% at a current density >200 mA cm⁻² at −0.9 V vs. RHE.¹⁷⁹ Similar strategies for CoPc were also reported in other studies.^128,180

3.3.3 The problems and challenges in KIE study. Although H/D KIE studies are widely investigated to provide useful information for reaction mechanisms, several problems and challenges in experimental design and measurement need to be addressed. For example, non-labeled proton sources (e.g., OH⁻ and HCO₃⁻) in the electrolyte must be replaced independently with deuterium to effectively and accurately conduct and interpret H/D KIE studies. It should be noted that the KIE effect can be intertwined with isotope effects related to mass transport, resulting from differences in the diffusion of D₂O or the acid dissociation constants. Hence, apart from H in the electrolyte, all other local reaction microenvironments (e.g., temperature, solvation effects, CO₂ transportation) should be identical. In this case, GDE is recommended for use. Finally, any presence of H₂O on the surface of the electrode from the atmosphere can affect the obtained KIE, especially in systems with secondary KIE or inverse KIE.

3.4 Temperature-sensitivity analysis of electrochemical CO₍₂₎ reduction

Until now, GDE has already been used to increase the current densities of CO₍₂₎ reduction to industrially relevant values. However, the energy efficiency of electrochemical reactions (defined by comparing the applied voltage with the thermodynamic voltage) is much worse than the required efficiency. Increasing operating temperature is an effective strategy, which generally results in higher rates and increases the energy efficiency.^181–183 However, systematical temperature-sensitivity analysis is rare and the temperature effect in electrocatalytic CO₂ reduction is complex, which can affect several reaction parameters, including local pH, the diffusion rate of reactants to the electrode surface, adsorbed intermediates, and solution resistance.

Hori et al. investigated the temperature dependent-performance over PC–Cu in electrocatalytic CO₂ reduction using a H-type cell, where the catalytic performance was evaluated at a constant current density of 5 mA cm⁻². The electrode potential was decreased from −1.39 to −1.33 V vs. SHE with an increase of temperature from 0 to 40 °C. The results showed that CH₄ was the dominant product at 0 °C, and its maximum faradaic efficiency (FE) was 60%. With increasing temperatures to 40 °C, the H₂ evolution and C₂H₄ formation dominated the surface reaction and the corresponding FE was 50% for H₂ and 20% for C₂H₄.¹⁸⁴ It is noteworthy that the catalytic performance was obtained during the first 15 min. Consistently, Ahn et al. also found that lower temperature (2 °C) favored CH₄ formation (ca. 50%) at the expense of C₂H₄ (only 10%) and H₂ evolution dominated the electrochemical reaction in H-type cell, reaching >50% FE_H2 at >30 °C at an applied potential of −1.60 V vs. Ag/AgCl. Temperature effects were explained mainly by the changes in CO₂ concentration rather than changes in pH.¹⁸⁵ However, it was also reported that lower temperature favored CH₄ and C₂H₄ generation over Cu-based catalysts in KOH + methanol due to the higher CO₂ solubility.^184,186,187

The temperature-sensitivity analysis will be more interesting at commercially-relevant current densities, which can be used for system design. Löwe et al. investigated the temperature dependence on tin oxide loaded GDE in electrocatalytic CO₂ reduction to formate. As the temperature increased, the total current density was enhanced (Fig. 9a). Considering the selectivity, the best performance was achieved at a temperature of 50 °C, which allowed a current density of 1000 mA cm⁻² with FE_HCOO⁻ >80%. Lower or higher operating temperatures both show an increased HER at total current density of 1000 mA cm⁻², likely resulting from the opposing influence of temperature on CO₂ diffusion coefficients and solubility in the water layer on Cu. Note that electrowetting needs not to be considered when increasing the temperature for electrolysis.¹⁸⁸

Fig. 9 (a) The temperature dependence on GDE of SnOx in a gas-fed flow cell. Reproduced with permission.¹⁸⁸ Copyright 2019, Wiley-VCH. (b) The temperature-dependent cell voltage and CO selectivity over Au under a current density of 0.1 A cm⁻². Reproduced with permission.¹⁸⁹ Copyright 2019, RSC publishing group. The activation energy for CO₂ reduction to CO (blue) and the HER (red) on (c) Ag and (d) Ni–N/C electrodes. Reproduced with permission.¹⁹⁵ Copyright 2021, American Chemical Society.

In addition, Zhuang and co-workers investigated the temperature-dependent performance of PC-Au and the efficiency of CO₂ reduction to CO in an alkaline polymer electrolyte using pure water as the electrolyte. When the operating temperature was increased from 30 °C to 80 °C, the FE_CO declined from 96% to 72% and the cell voltage decreased from 2.5 V to 2.2 V (Fig. 9b), resulting from the kinetic effects of both the electrode reactions and the ionic conduction.¹⁸⁹ Since solution resistance was less than 0.4 Ω cm⁻², the corresponding voltage reduction was only 0.04 V. Hence, the improvement mainly comes from the reaction kinetics rather than the ionic conduction. The decline in the FE_CO upon increased temperature indicated the change in the catalytic selectivity between CO₂ reduction and HER. These studies suggest that exploring the optimal operating temperature is essential to increase energy efficiency. Importantly, GDE restructuring and membrane failures were observed at high operating temperatures, leading to severe deactivation of the catalytic systems.^190–194

Although the trend of CO₂ electroreduction has been examined in some systems, the apparent activation energy (E_a) was rarely calculated, which is a quantitative analysis of temperature effects. Kim et al. examined the temperature-dependent performance from 303 to 343 K over Ag NPs and Ni–N/C separately in a membrane electrode assembly electrolyzer (MEA) and the corresponding E_a of CO₂ reduction to CO and HER was calculated. The E_a of HER (45–60 kJ mol⁻¹) was always higher than that of CO evolution (10–20 kJ mol⁻¹) on both electrocatalysts (Fig. 9c and d). Moreover, Ni–N/C delivered a lower E_a of CO evolution (10 kJ mol⁻¹) than Ag NPs (20 kJ mol⁻¹), indicating that CO₂ activation was easier than that on Ag.¹⁹⁵ Of note, the E_a value was calculated by Arrhenius plots of j_CO and j_H2 at fixed −2.75 V, where the precise potentials were not well calibrated. In another study using H-type cell equipped with a reference electrode, the E_a of CO₂ reduction to CO over Ag NPs was 72 kJ mol⁻¹, much higher than that in MEA. Moreover, they found that different nano-structured Ag delivered different E_a values, where 44 kJ mol⁻¹ and 21 kJ mol⁻¹ were extracted for Ag rods and needles, respectively. These differences likely come from the local reaction environment and the calibration of applied potentials.

Koper and co-workers examined the temperature effects on rotating ring gold disk electrode in cation-promoted CO₂ reduction, disclosing that both activity and selectivity of CO₂ reduction to CO increased with temperature. However, the enhancement of kinetics of CO evolution was counteracted by the lower CO₂ solubility, which yielded an optimized working temperature of ca. 55 °C. The apparent activation energy was obtained to be 61 kJ mol⁻¹ in 0.1 M NaHCO₃, whose value decreased with cation concentration and identity. In addition, the transfer coefficient was also derived to be ca. 0.3, which indicated that the enthalpy contribution played a dominant role in this process and entropy was non-negligible.¹⁹⁶

Recently, Lu et al. investigated the temperature-sensitivity analysis of electrochemical CO reduction on Cu in a gas-fed flow cell equipped with a well-defined reference electrode, revealing a pronounced influence on the catalytic activity of CO reduction at increased temperatures. The calculated E_a values for all C₂₊ products fall within 60 ± 12 kJ mol⁻¹, comparable to the reported theoretical value for the C–C coupling process of 30–75 kJ mol⁻¹.¹⁵⁴ It should be explicitly noted that the E_a value for each product did not have to reflect the (apparent) activation energy of the RDS, especially when the RDS followed selectivity-determining steps. Likewise, in reaction order analysis, various E_a values for each product were obtained, although they shared with a common RDS because the product distribution was perturbed by CO coverage as a key intermediate at selectivity-controlling steps after the RDS.

4. Challenges and limitations of microkinetic analysis in electrocatalytic CO₍₂₎ reduction

4.1 The existing challenges in microkinetic analysis

4.1.1 The measurements of intrinsic reaction kinetics. To accurately interpret microkinetic analysis, the intrinsic reaction rates must be extracted under experimental reactions, where the contribution of mass transport should be effectively isolated. Moreover, ECSA-normalized current densities are highly recommended in microkinetic analysis, which is the closest to the intrinsic activity without mass transport limitations.^197,198 Regarding the configuration of electrochemical reactors, the partial current densities of electrocatalytic CO reduction over Cu-based GDEs^{145,149,150,199,200} when normalized to the geometric area or ECSA in the gas-fed flow cell or MEA are more than one or two orders of magnitude higher than that examined in H-type cell at similar overpotentials and bulk electrolyte conditions, ascribed to circumventing the gaseous CO diffusion-limitation, as shown in Fig. 10a. Moreover, the electrocatalytic performance of CO reduction and as-obtained reaction orders with respect to P_CO in the gas-fed flow cell and MEA are comparable (Fig. 10b), indicating the similarity of their local environment. Accordingly, a conventional H-type cell is not a good choice for microkinetic analysis in electrochemical CO₍₂₎ reduction. Although GDE likely mediates mass transport limitation during CO reduction, more detailed work is necessary to quantitively describe the diffusions of gas and electrolyte at electrode/electrolyte interfaces. Overall, an advanced model system with well-defined electrode and mass transport is highly desired for fundamental kinetic analysis. Voltage matters for intrinsic performance measurements and the readers are referred to the previous perspective for further details.²⁰¹ In addition, it should be highlighted that iR correction needs to be done appropriately to present the intrinsic activity and then use the polarization curves for Tafel analyses.²⁰² In the literature, different catalytic performances over the same electrocatalysts under similar reaction conditions were observed for electrocatalytic CO₂ reduction.¹⁹⁸ For example, FE_CH4 varied significantly from ca. 6% at −1.0 V vs. RHE to 40% at −1.1 V vs. RHE in H-type cell,^203,204 indicating that small differences of tens of mV between the reported potential and the actual electrode potential could result in significant variations in the product distribution. Moreover, ionic conductivity also changed during electrolysis experiments due to the ion-selective membranes, indicating that the actual electrode potential always changed unless continuous iR measurement and compensation were applied.²⁰⁴ Additionally, various types of iR compensation on PC–Cu for electrocatalytic CO₂ reduction in the same setup would also lead to different potentials at the cathode, they also have a direct impact on the observed current density (Fig. 10c). Both the current interrupt and the 80% positive feedback by software correction methods were shown to keep the actual potential closest to the desired potential throughout the potentiostatic electrolysis runs. Thus, using the most robust approach to measuring and reporting the results is necessary.

Fig. 10 (a) Comparison of C₂₊ partial current densities normalized to ECSA of CO electroreduction on Cu in a gas-fed flow cell and H-type cell. The data is adapted from ref. 145, 149, 150, 199, 200 (b) Comparison of as-obtained kinetic results (j_C2+ and reaction orders) in a gas-fed flow cell and MEA under the same reaction conditions during CO electroreduction over Cu. The data is adapted from ref. 145. (c) Product distributions with different iR compensations at −1.7 V vs. Ag|AgCl in CO₂ saturated 0.1 M KHCO₃. Reproduced with permission.²⁰⁴ Copyright 2022, American Chemical Society. (d) Dynamic changes in the structure and catalytic selectivity of Cu nanocubes in CO₂ electroreduction. Reproduced with permission.²⁰⁵ Copyright 2018, Wiley-VCH.

4.1.2 Dynamically restructured electrocatalysts. An additional challenge in the microkinetic analysis is that dynamic changes in the structure and catalytic selectivity of electrocatalysts occur in CO₂ electroreduction (Fig. 10d).²⁰⁵ In electrocatalytic CO₍₂₎ reduction, both the negatively applied potentials and the strong surface–absorbate interactions can lead to catalyst surface restructuring and further significant changes, resulting in changing performance.^206–208 For example, the dynamic morphological changes of Cu have been demonstrated via operando X-ray absorption spectroscopy (XAS) and in situ/ex situ transmission electron microscopy (TEM),²⁰⁹ where some under-coordinated sites formed to accelerate CO₂ reduction and induce different selectivity.^210,211 The dynamical evolution of edge and stepped sites facilitated the electroreduction of CO₂ to C₂H₄ and ethanol over Cu₂O film.²¹² Potential-induced active-sites formation was also reported on CuPc via operando XAS, where a reversible transformation between isolated Cu atoms and Cu_n clusters was captured.²¹³ Similar surface-morphological-change induced active-sites formation have been also observed on non-copper-based catalysts, including S-doped Cu, Ag, and InN.^214–216 Overall, electrocatalysts may undergo morphology reconstructions, such as dissolution, redeposition, and leaching, and further lead to changes in selectivity and activity. To obtain reasonable microkinetic results, electrocatalyst stability must be carefully considered.

4.1.3 The complexity of electrolyte effects. Beyond the identity of the electrocatalyst materials, the nature of the electrolyte including cation identity and pH can strongly affect the catalytic performance (activity, selectivity, and stability in GDE electrolyzers). Several potential contributions of cations during CO₍₂₎ reduction have been put forward.^{37,63,133,217–222} One commonly accepted pathway is that cations facilitate the surface-mediated CO₍₂₎ reduction by modifying the interfacial electric field via cation-noncovalent interactions on the electrode surface.^131,133 For example, Ringe et al. established a multi-scale modeling approach that combined size-modified Poisson–Boltzmann theory with ab initio simulations of field effects on key reaction intermediates to provide quantitative evidence for the impact of cations on the interfacial electric field, as shown in Fig. 11a.¹³¹ Besides this interfacial electric field effect, Koper and coworkers extensively investigated cation effects in CO₂ electroreduction to CO under well-defined mass transport conditions.²²¹ Their results coupling online product-detection experiments and theoretical calculations suggested that the partially desolvated metal cations participate in the formation of CO₂–intermediate via a short-range electrostatic interaction (Fig. 11b), which provided definitive evidence that cations could stabilize the crucial reaction intermediate on gold, silver, or copper. In addition, Xu and coworkers utilized in situ surface-enhanced spectroscopic investigations to suggest that not only the change of interfacial electric field strength with cation size but also the presence of a nonelectric field strength component, especially the environment around alkali metal cations (chelating agent, hydration shells, or the interfacial water, affected the CO reduction on Cu.¹³³ Spectroscopic investigations with cation chelating agents and organic molecules (Fig. 11c) exhibited that the electric and nonelectric field components of the cation effect could be affected by both cation identity and composition of the electrochemical interface.¹³³

Fig. 11 (a) Illustration of the origin of cation effects in field-driven electrocatalysis. Reproduced with permission.¹³¹ Copyright 2019, RSC publishing group. (b) Schematic representation of the interaction of the cation with the negatively charged CO₂⁻ intermediate together with a proposed reaction mechanism. Reproduced with permission.²²¹ Copyright 2021, nature publishing group. (c) The impact of organics on attenuated total reflection surface-enhanced infrared absorption spectroscopy.¹³³ (d) Summary of electrolyte effects on electrochemical CO₂ reduction.

Furthermore, the nature and concentration of anions also influence the performance by their effects on the competing HER, especially in the presence of mass-transportation conditions.^223–226 Since the different branches of HER (e.g., proton, buffer mediated species, or water reduction) on electrocatalysts could occur depending on the local reaction microenvironments including applied potentials, the concentration of electrolyte, and CO₍₂₎ concentration. For more detailed reviews of electrolyte effects on CO₂ electrochemical reduction, the reader is referred to specialized recent reviews.²²²

Overall, the last decades have witnessed intensive research efforts dedicated to the exploration of electrolyte effects on CO₍₂₎ reduction, as illustrated in Fig. 11d. Nevertheless, their main role (especially cation) is still under discussion. For example, Li et al. revealed that a small change in interfacial electric field strength was not likely the main contribution to the as-obtained difference in the performance of CO reduction, where the varying interaction between adsorbed CO and interfacial water was interpreted to change performance in presence of different organic cations.²²⁷ Similarly, Xu and co-workers reported that the cation concentration (e.g., 0.1 M or 1 M Na⁺) and cation identity (e.g., K⁺, Rb⁺, Cs⁺) during CO reduction did not change the Stark tuning rate or the interfacial electric field strength;^133,220 on the contrary, they significantly changed the catalytic performance. Then, nonelectric field strength components of the cation effect were proposed to affect CO dimerization.¹³³ However, the definitive evidence of nonelectric field strength components is still missing. Thus, systematic studies of the cation effects on CO₍₂₎ reduction and the correspondingly quantitative expression in kinetic equations are necessary for further study.

4.2 Limitations of the microkinetic analysis

4.2.1 The coverage of the adsorbed species assumptions in microkinetic analysis. In the Tafel slope and reaction order analysis, extreme coverage of the adsorbed species (θ  ≈ 0 or ≈1) is generally assumed, although, in practice, the slopes are coverage dependent. For example, it is widely accepted that the Tafel slopes of 120, 40, and 30 mV dec⁻¹ are observed for the Volmer, Heyrovsky, and Tafel determining rate steps, respectively, during HER. However, for the Heyrovsky rate-determining step, a Tafel slope of 120 mV dec⁻¹ was observed in the higher adsorbed hydrogen atom coverage (θ_H > 0.6). Similarly, the Tafel slope of CO₂ electroreduction toward CO could be simplified to eqn (10), when a3 is assumed to be RDS.where θ₁ and θ₂ are the coverage of *COOH and *COOH–H⁺, respectively. Hence, the first electron transfer limiting the reaction rate does not necessarily showing an experimentally observed slope of 120 mV dec⁻¹ and there are other potential scenarios.

In addition, reaction order is also coverage dependent. For example, it has been demonstrated that the coverage of the intermediates was changing based on the applied potentials in CO electro-oxidation, resulting in the changed reaction orders along with the applied potentials. Therefore, it is challenging to use only the Tafel slope and reaction order to affirm or exclude a proposed mechanism without any information on the coverage of intermediates under operating conditions in electrocatalytic CO₍₂₎ reduction.

To understand the coverage of intermediates, future work involving surface-sensitive spectroscopies mirroring GDE based-conditions is needed. For example, Lu and co-workers developed a custom-designed spectra-electrochemical cell allowing CO pressures up to 6 MPa to investigate surface enhanced infrared spectroscopy over PC–Cu in electrocatalytic CO reduction. They found the upper bound of the CO coverage under electrocatalytic conditions to be 0.05 monolayer at atmospheric pressure and the saturation CO coverage to be ∼0.25 monolayer.¹³⁹ Apart from operando spectroscopies, DFT is another convenient and powerful tool to understand the adsorption isotherm for relevant adsorbates and thermodynamic parameters related to the adsorption equilibrium constant.

It is also worth mentioning that the effect of the applied potential on the free energy of intermediates is important since the enthalpy and entropy of intermediates are potential-dependent in many cases.¹⁵³ Wang and co-workers developed a continuous-flow Raman electrochemical cell for electrocatalytic CO₂ reduction, which enabled to monitor the local pH variation near the surface of GDE under reaction conditions,²²⁸ verifying the presence of the apparent overpotential reduction on the RHE scale in alkaline electrolyte due to the Nernst potential of the strong pH gradient layer at the cathode/electrolyte interface. This change in the electric fields can significantly alter the binding energies of intermediates and solvent molecules at the electrode/electrolyte interface. In addition, the adsorption energy of intermediates can also be affected by the coverages of adsorbates on the surface.

Overall, the coverage of various intermediates should not be overlooked in both the Tafel slope and apparent reaction order analysis, where the electric field effects and adsorbate–adsorbate interactions may be necessary to consider.

4.2.2 Other assumptions in the microkinetic analysis. In microkinetic analysis, we always assume a transfer coefficient α of 0.5 in an elementary charge-transfer step. Indeed, it is not necessary to be 0.5²²⁹ and has been identified as a function of the electrode potential, especially in organic systems using acetonitrile or dimethylformamide as a solvent containing quaternary ammonium salt as supporting electrolytes.²³⁰ For the reduction of benzaldehyde in ethanol, the potential dependent α examined by potential-sweep voltammetry can be written as the following eqn (11):where λ is the solvent reorganization factor, relating to the transition state; φ_r is the potential difference between the reaction site and the solution.²³⁰ Similarly, the α can also be predicted from 1D potential energy surface based on Marcus theory of outer-sphere electron transfer, where the reactant and product do not interact directly with the electrode surface; the detailed expression can be simplified as follows:^29,88Consistently, α is mainly governed by overpotential and solvent reorganization. Only when λ ≫ η, α = 0.5. In addition to the organic system, some significant deviation values from 0.5 have been observed in some aqueous electrochemical reactions, for example, Cr(ethylenediamine)₃³⁺ reduction on the Pb–Ag surface.²³¹ Moreover, the electrode properties (e.g., pore structures and ionic or electronic conductivity) and a high exchange current density were reported to cause α to be <0.5 in the oxygen reduction reaction.²³² These will dramatically impact the calculated values of the Tafel slopes and apparent activation energy.

An additional assumption in microkinetic analysis pertains to the quasi-equilibrium, where the reactions are assumed to occur rapidly in both forward and reverse directions before the RDS step. Actually, the non-rate-determining step can be nonequilibrium. In oxygen evolution reaction, Marshall et al. have demonstrated that models using the quasi-equilibrium assumption fail to predict some Tafel regions.²³³ Hence, the steady-state assumption is recommended for predicting Tafel slopes.

4.2.3 Theoretical models in DFT calculation. Although DFT calculations have been widely utilized to probe all intermediates at the atomic level and provide deeper insights into reaction pathways, varied theoretical models always present different pictures for CO₍₂₎ reduction.^234–236 The last decades have witnessed intensive research efforts dedicated to this direction. For example, the pioneering work by Nørskov et al. proposed the computational hydrogen electrode (CHE) model, pushing the huge development of modeling electrochemical processes. Based on this model, Peterson et al. first reported the detailed reaction mechanism on Cu(211) surface, in agreement with the experimentally as-obtained onset potentials of the major products.²³⁷ However, owing to the oversimplification in this model, computational results sometimes deviated from the experiments.²³⁸ Afterwards, the implicit and explicit solvent models were established to describe solvent effects in the catalyst surface/electrolyte interfaces.²³⁹ To exhibit more dynamic water layers in the electrochemical interface, ab initio molecular dynamics (AIMD) simulations have been widely used to improve the reliability of modeling.^238,240 Furthermore, the applied potential was considered in the homogeneous background or linearized Poisson–Boltzmann model to simulate electrified interfaces.²⁴¹ Alternatively, the quantum mechanics(QM)/molecular mechanics (MM) model was reported to describe the interface between QM and MM regions, which became the key to obtaining accurate results.^242,243 Despite great progress, it still remains challenging that the complexity of the electrochemical interface between electrocatalysts and electrolyte remains elusive and could not be taken into consideration in one model. For example, CO₍₂₎ reduction under practical conditions generally occurs at a constant potential with an infinite reservoir of electrons; however, in the simulations, the reservoir of electrons is finite; the cations have unknown and non-negligible effects on CO₍₂₎ reduction at the electrochemical interface. Fortunately, the huge development of machine learning is likely to be a revolutionary approach for the simulation of CO₍₂₎ reduction on a larger space and time scale.^244,245

5. Conclusion and outlook

Microkinetic studies are powerful tools in electrocatalytic CO₍₂₎ reduction, helping to identify critical reaction intermediates and RDSs. This information is crucial for designing improved electrocatalysts and scaling up CO₍₂₎ electrochemical reactors. This review systematically summarizes DFT-based microkinetic models for various products (e.g., CO, HCOOH, CH₄, and C₂₊) and numerous experimentally-derived kinetic observables associated with the complex processes occurring in electrocatalytic CO₍₂₎ reduction. Although Tafel and reaction order analyses, combined with KIE and temperature sensitivity analyses, are widely used to gain mechanistic insights, the kinetic observables obtained under incorrect operating conditions or oversimplified models often fail to accurately describe surface electrocatalysis. The experimentally scattered results have been systematically analyzed, and the effect of the local reaction microenvironment on the obtained kinetic observables has been highlighted. Moreover, the challenges in intrinsic kinetic measurements and the limitations of microkinetic analysis are carefully discussed.

With these challenges and limitations in mind, we now consider the opportunities for more effectively using microkinetic analysis to understand the reaction mechanism. The detailed strategies are as follows:

(1) Accurate interpretation of catalytic performance

Selecting well-defined electrodes, suitable experimental conditions, and reactor configurations is critical for intrinsic kinetic measurements, where the measured rates are governed by kinetics rather than being convoluted by mass transport limitations. And no mass transport limitation in the system and potential/time-independent electrodes are required. In this context, the rotating ring disk electrode (RRDE) is a potential candidate to extract the true intrinsic kinetics of electrocatalysts, which has been successfully demonstrated to control and improve mass transport for oxygen reduction reaction. For CO₂ reduction to CO, Koper and coworkers have utilized RRDE to perform microkinetic analysis over Au in various electrolytes under well-defined mass transport conditions and using online analytical techniques.^71,222–224 In further work, the well-defined RRDE equipped with online product identification (e.g., H₂, HCOO⁻, CH₄, C₂₊) and quantification techniques with high resolution will be the next system challenges. Alternatively, the exploration of well-defined electrodes with the gas-diffusion layer will be another direction, which allow for higher current densities. Accordingly, the commonly used online analytical techniques can be used. It should be noted that this configuration absolutely will introduce non-kinetic effects including the complexities of electrode/electrolyte interfaces and diffusion processes of liquid and gas, where it needs more efforts to quantitatively describe their corresponding contributions to the as-obtained catalytic performance. Moreover, as Koper and co-workers recommended in the rigorous Tafel analysis of oxygen evolution reaction, Tafel slopes should be plotted versus current densities or applied potentials to yield a kinetically meaningful Tafel slope value, which was obtained from a horizontal Tafel slope region.²⁴⁶

In addition, special care is also needed that catalytic performance of electrocatalyst material will be assessed at commercially relevant current densities, not only substantially lower ones since it is strongly influenced by not only the identity of the electrocatalyst materials but also the local steady-state environment perturbed by reaction micro-environment and the operating current density. Apparent kinetic parameters are necessary to be utilized to establish the relationship between the intrinsic kinetics and “apparent kinetics” at commercially-relevant conditions, which could accelerate the technology toward large-scale CO₍₂₎ reduction. Consequently, the GDE with a well-defined reference electrode is needed to alleviate mass transport limitations and record the actual applied potentials. It should be mentioned that the stability of electrocatalysts during electrolysis is of great importance. Electrocatalysts with structural simplicity, ease of handling, and robustness are particularly attractive for fundamental kinetic studies. To enhance stability, one recent example involves placing an inactive material atop the catalyst layer as a current collector, preventing both catalyst restructuring and deposition of contaminants on the active catalyst surface.²⁴⁷ Finally, accurate and precise reporting for electrochemical CO₂ reduction studies is essential. For instance, iR compensation should be carefully corrected, and the measured rates should be normalized to the geometric electrode area as well as the ECSA. For further details on standards and protocols for data acquisition and reporting, readers are referred to previous studies.^114,248

(2) Utilizing operando spectroscopies as complementary tools

Due to the complexities and uncertainties of microkinetic analysis, it remains challenging to rely solely on microkinetic models to fully uncover reaction mechanisms. Operando spectroscopies can be employed as complementary tools to increase reliability, such as attenuated total reflection surface-enhanced infrared spectroscopy (ATR-SEIRAS),^136,137,139 operando Raman spectroscopy,^249–251 ambient-pressure X-ray photoelectron spectroscopy,²⁵² and differential electrochemical mass spectroscopy.²⁵³ In particular, in situ/operando techniques with high resolution in the time frame of below second is helpful to capture the short-lifetime intermediate. In addition, surface-sensitive spectroscopies that mirror GDE based-conditions (at high rates) are highly desirable, requiring a careful design and fabrication of dedicated devices.

(3) Improving DFT-based microkinetic simulation

Significant process in DFT calculations has been made in recent years. Electrochemical activation barriers and potential contributions from electric fields and solvation effects are now considered in microkinetic modeling to enhance computational accuracy. However, an additional constraint stemming from DFT relates to the assumptions of the microkinetic model itself, such as the quasi-equilibrium assumption and random distribution of adsorbed species on the catalytic surface in mean-field models.^12,233 As discussed, oversimplified assumptions can lead to misleading conclusions about reaction mechanisms. Therefore, the steady-state assumption is recommended. Several non-idealities should be addressed in future microkinetic modeling, including interfacial structures, solvent dynamics, potential-dependent symmetry factors, potential-dependent intermediate coverage, and adsorbate–adsorbate interactions.

(4) Strengthening the connection between DFT and experiments

Strengthening the connection between DFT and experiments is crucial for accurately understanding the mechanistic details of electrocatalytic CO₍₂₎ reduction and designing novel electrocatalysts,^25,254 which could accelerate the data-driven screen and design of electrocatalysts. Accordingly, the iterative model refinement can be employed as a general methodology. First, the establishment of a model system is an important part of DFT calculation where the correspondingly active site is needed to construct based on fundamental physical and chemical characterizations, but also the electrochemical microenvironment around active sites as well as electrode potential is crucial to be considered. To reduce the complexity in formulating electrochemical processes, single-crystal materials^{76,196,221–223,226,255} and surface-immobilized single-site catalysts^256,257 are highly recommended. Second, kinetic observables are extracted from experiments without mass transport limitation to optimize DFT-based parameters and achieve consistency between computation and experiments. Finally, the feedback gained from the parameter adjustment process is used to revise the original hypothesis for the reaction mechanism and iterate all steps until consistency between experiments and computation is achieved, thereby accurately describing the nature of electrocatalysts.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

Asahi Kasei Corporation financially supported this work.

References

1. I. E. Stephens, K. Chan, A. Bagger, S. W. Boettcher, J. Bonin, E. Boutin, A. K. Buckley, R. Buonsanti, E. R. Cave and X. Chang, J. Phys.: Energy, 2022, 4, 042003.
2. H. Shin, K. U. Hansen and F. Jiao, Nat. Sustain., 2021, 4, 911–919.
3. P. De Luna, C. Hahn, D. Higgins, S. A. Jaffer, T. F. Jaramillo and E. H. Sargent, Science, 2019, 364, eaav3506.
4. S. Nitopi, E. Bertheussen, S. B. Scott, X. Y. Liu, A. K. Engstfeld, S. Horch, B. Seger, I. E. L. Stephens, K. Chan, C. Hahn, J. K. Norskov, T. F. Jaramillo and I. Chorkendorff, Chem. Rev., 2019, 119, 7610–7672.
5. Y. Y. Birdja, E. Pérez-Gallent, M. C. Figueiredo, A. J. Göttle, F. Calle-Vallejo and M. T. M. Koper, Nat. Energy, 2019, 4, 732–745.
6. D. H. Nam, P. De Luna, A. Rosas-Hernandez, A. Thevenon, F. W. Li, T. Agapie, J. C. Peters, O. Shekhah, M. Eddaoudi and E. H. Sargent, Nat. Mater., 2020, 19, 266–276.
7. J. Resasco and A. T. Bell, Trends Chem., 2020, 2, 825–836.
8. J. A. Rabinowitz and M. W. Kanan, Nat. Commun., 2020, 11, 5231.
9. A. Ozden, Y. H. Wang, F. W. Li, M. C. Luo, J. Sisler, A. Thevenon, A. Rosas-Hernandez, T. Burdyny, Y. W. Lum, H. Yadegari, T. Agapie, J. C. Peters, E. H. Sargent and D. Sinton, Joule, 2021, 5, 706–719.
10. T. Alerte, J. P. Edwards, C. M. Gabardo, C. P. O'Brien, A. Gaona, J. Wicks, A. Obradovi, A. Sarkar, S. A. Jaffer, H. L. MacLean, D. Sinton and E. H. Sargent, ACS Energy Lett., 2021, 6, 4405–4412.
11. O. S. Bushuyev, P. De Luna, C. T. Dinh, L. Tao, G. Saur, J. van de Lagemaat, S. O. Kelley and E. H. Sargent, Joule, 2018, 2, 825–832.
12. A. Baz, S. T. Dix, A. Holewinski and S. Linic, J. Catal., 2021, 404, 864–872.
13. A. T. Marshall, Curr. Opin. Electrochem., 2018, 7, 75–80.
14. G. A. Cerrón-Calle, T. P. Senftle and S. Garcia-Segura, Curr. Opin. Electrochem., 2022, 101062.
15. T. Shinagawa, A. T. Garcia-Esparza and K. Takanabe, Sci. Rep., 2015, 5, 1–21.
16. S.-Q. Xiang, S.-T. Gao, J.-L. Shi, W. Zhang and L.-B. Zhao, J. Catal., 2021, 393, 11–19.
17. Q. Li, Y. Ouyang, S. Lu, X. Bai, Y. Zhang, L. Shi, C. Ling and J. Wang, Chem. Commun., 2020, 56, 9937–9949.
18. J. T. Mefford, Z. Zhao, M. Bajdich and W. C. Chueh, Energy Environ. Sci., 2020, 13, 622–634.
19. G. O. Larrazábal, A. J. Martín and J. Perez-Ramirez, J. Phys. Chem. Lett., 2017, 8, 3933–3944.
20. Q. Lu and F. Jiao, Nano Energy, 2016, 29, 439–456.
21. F. Dattila, R. R. Seemakurthi, Y. Zhou and N. López, Chem. Rev., 2022, 122, 11085–11130.
22. M. R. Singh, J. D. Goodpaster, A. Z. Weber, M. Head-Gordon and A. T. Bell, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, E8812–E8821.
23. A. Bagger, W. Ju, A. S. Varela, P. Strasser and J. Rossmeisl, Chem. Phys. Chem., 2017, 18, 3266–3273.
24. M. Zhong, K. Tran, Y. M. Min, C. H. Wang, Z. Y. Wang, C. T. Dinh, P. De Luna, Z. Q. Yu, A. S. Rasouli, P. Brodersen, S. Sun, O. Voznyy, C. S. Tan, M. Askerka, F. L. Che, M. Liu, A. Seifitokaldani, Y. J. Pang, S. C. Lo, A. Ip, Z. Ulissi and E. H. Sargent, Nature, 2020, 581, 178–183.
25. J. Resasco, F. Abild-Pedersen, C. Hahn, Z. Bao, M. Koper and T. F. Jaramillo, Nat. Catal., 2022, 5, 374–381.
26. M. Dunwell, W. Luc, Y. Yan, F. Jiao and B. Xu, ACS Catal., 2018, 8, 8121–8129.
27. C. W. Lee, N. H. Cho, S. W. Im, M. S. Jee, Y. J. Hwang, B. K. Min and K. T. Nam, J. Mater. Chem. A, 2018, 6, 14043–14057.
28. T. Shinagawa, A. T. Garcia-Esparza and K. Takanabe, Sci. Rep., 2015, 5, 13801.
29. Y.-H. Fang and Z.-P. Liu, ACS Catal., 2014, 4, 4364–4376.
30. S. Fletcher, J. Solid State Electrochem., 2009, 13, 537–549.
31. J. Rosen, G. S. Hutchings, Q. Lu, S. Rivera, Y. Zhou, D. G. Vlachos and F. Jiao, ACS Catal., 2015, 5, 4293–4299.
32. S. Ringe, C. G. Morales-Guio, L. D. Chen, M. Fields, T. F. Jaramillo, C. Hahn and K. Chan, Nat. Commun., 2020, 11, 33.
33. M. Dunwell, Q. Lu, J. M. Heyes, J. Rosen, J. G. Chen, Y. Yan, F. Jiao and B. Xu, J. Am. Chem. Soc., 2017, 139, 3774–3783.
34. X. Lu, B. Dereli, T. Shinagawa, M. Eddaoudi, L. Cavallo and K. Takanabe, Chem Catal., 2022, 2, 1143–1162.
35. A. Wuttig, M. Yaguchi, K. Motobayashi, M. Osawa and Y. Surendranath, Proc. Natl. Acad. Sci. U. S. A., 2016, 113, E4585–E4593.
36. B. A. Zhang, T. Ozel, J. S. Elias, C. Costentin and D. G. Nocera, ACS Central Sci., 2019, 5, 1097–1105.
37. L. D. Chen, M. Urushihara, K. Chan and J. K. Nørskov, ACS Catal., 2016, 6, 7133–7139.
38. M. R. Singh, J. D. Goodpaster, A. Z. Weber, M. Head-Gordon and A. T. Bell, Proc. Natl. Acad. Sci. U. S. A., 2017, 114, E8812–E8821.
39. N. Gupta, M. Gattrell and B. MacDougall, J. Appl. Electrochem., 2006, 36, 161–172.
40. A. S. Varela, M. Kroschel, T. Reier and P. Strasser, Catal. Today, 2016, 260, 8–13.
41. M. Dunwell, X. Yang, B. P. Setzler, J. Anibal, Y. Yan and B. Xu, ACS Catal., 2018, 8, 3999–4008.
42. J. Ryu, A. Wuttig and Y. Surendranath, Angew. Chem., Int. Ed., 2018, 57, 9300–9304.
43. Z. Mao and C. T. Campbell, J. Catal., 2020, 381, 53–62.
44. H. Prats and K. Chan, Phys. Chem. Chem. Phys., 2021, 23, 27150–27158.
45. X. Chang, S. Vijay, Y. Zhao, N. J. Oliveira, K. Chan and B. Xu, Nat. Commun., 2022, 13, 2656.
46. X. Song, L. Xu, X. Sun and B. Han, Sci. Chin. Chem., 2023, 66, 315–323.
47. B. Hasa, Y. Zhao and F. Jiao, Annu. Rev. Chem. Biomol. Eng., 2023 DOI:10.1146/annurev-chembioeng-101121-071735.
48. E. Pérez-Gallent, M. C. Figueiredo, F. Calle-Vallejo and M. T. M. Koper, Angew. Chem., Int. Ed., 2017, 56, 3621–3624.
49. I. V. Chernyshova, P. Somasundaran and S. Ponnurangam, Proc. Natl. Acad. Sci. U. S. A., 2018, 115, E9261–E9270.
50. J. Gao, H. Zhang, X. Guo, J. Luo, S. M. Zakeeruddin, D. Ren and M. Grätzel, J. Am. Chem. Soc., 2019, 141, 18704–18714.
51. P. Wei, D. Gao, T. Liu, H. Li, J. Sang, C. Wang, R. Cai, G. Wang and X. Bao, Nat. Nanotechnol., 2023, 1–8.
52. N. J. Firet and W. A. Smith, ACS Catal., 2017, 7, 606–612.
53. Y. Hori, H. Wakebe, T. Tsukamoto and O. Koga, Electrochim. Acta, 1994, 39, 1833–1839.
54. Y. I. Hori, Mod. Aspects Electrochem., 2008, 89–189.
55. Y. Hori, I. Takahashi, O. Koga and N. Hoshi, J. Mol. Catal. A: Chem., 2003, 199, 39–47.
56. Y. Hori, I. Takahashi, O. Koga and N. Hoshi, J. Phys. Chem. B, 2002, 106, 15–17.
57. Y. Hori, R. Takahashi, Y. Yoshinami and A. Murata, J. Phys. Chem. B, 1997, 101, 7075–7081.
58. D. Xu, Y. Xu, H. Wang and X. Qiu, Chem. Commun., 2022, 58, 3007–3010.
59. Z. Bitar, A. Fecant, E. Trela-Baudot, S. Chardon-Noblat and D. Pasquier, Appl. Catal., B, 2016, 189, 172–180.
60. H. Wu, Z. Li, Y. Liu, X. Zou, L. Yin and S. Lin, Sustainable Energy Fuels, 2021, 5, 5798–5803.
61. E. Nurlaela, T. Shinagawa, M. Qureshi, D. S. Dhawale and K. Takanabe, ACS Catal., 2016, 6, 1713–1722.
62. S. Rasul, D. H. Anjum, A. Jedidi, Y. Minenkov, L. Cavallo and K. Takanabe, Angew. Chem., Int. Ed., 2015, 54, 2146–2150.
63. M. Liu, Y. Pang, B. Zhang, P. De Luna, O. Voznyy, J. Xu, X. Zheng, C. T. Dinh, F. Fan and C. Cao, Nature, 2016, 537, 382–386.
64. S. Verma, Y. Hamasaki, C. Kim, W. Huang, S. Lu, H.-R. M. Jhong, A. A. Gewirth, T. Fujigaya, N. Nakashima and P. J. A. Kenis, ACS Energy Lett., 2018, 3, 193–198.
65. J. Li, I. Maresi, Y. Lum and J. W. Ager, J. Chem. Phys., 2021, 155, 164701.
66. S. Hanselman, M. T. Koper and F. Calle-Vallejo, ACS Energy Lett., 2018, 3, 1062–1067.
67. F. Calle-Vallejo and M. T. M. Koper, Angew. Chem., Int. Ed., 2013, 52, 7282–7285.
68. K. J. P. Schouten, Y. Kwon, C. J. M. Van der Ham, Z. Qin and M. T. M. Koper, Chem. Sci., 2011, 2, 1902–1909.
69. X. Liu, J. Xiao, H. Peng, X. Hong, K. Chan and J. K. Nørskov, Nat. Commun., 2017, 8, 1–7.
70. X. Y. Liu, P. Schlexer, J. P. Xiao, Y. F. Ji, L. Wang, R. B. Sandberg, M. Tang, K. S. Brown, H. J. Peng, S. Ringe, C. Hahn, T. F. Jaramillo, J. K. Norskov and K. R. Chan, Nat. Commun., 2019, 10, 32.
71. M. T. M. Koper, A. Z. Weber, K. Chan and J. Cheng, Chem. Rev., 2022, 122, 10579–10580.
72. H. H. Heenen, J. A. Gauthier, H. H. Kristoffersen, T. Ludwig and K. Chan, J. Chem. Phys., 2020, 152, 144703.
73. S. Vijay, W. Ju, S. Brückner, S.-C. Tsang, P. Strasser and K. Chan, Nat. Catal., 2021, 4, 1024–1031.
74. J. T. Feaster, C. Shi, E. R. Cave, T. Hatsukade, D. N. Abram, K. P. Kuhl, C. Hahn, J. K. Nørskov and T. F. Jaramillo, ACS Catal., 2017, 7, 4822–4827.
75. M. T. Tang, H. Peng, P. S. Lamoureux, M. Bajdich and F. Abild-Pedersen, Appl. Catal., B, 2020, 279, 119384.
76. A. J. Göttle and M. T. M. Koper, J. Am. Chem. Soc., 2018, 140, 4826–4834.
77. X. Min and M. W. Kanan, J. Am. Chem. Soc., 2015, 137, 4701–4708.
78. K. M. Waldie, F. M. Brunner and C. P. Kubiak, ACS Sustainable Chem. Eng., 2018, 6, 6841–6848.
79. Y. Y. Birdja, J. Shen and M. T. M. Koper, Catal. Today, 2017, 288, 37–47.
80. C. Ye, S. J. Raaijman, X. Chen and M. T. M. Koper, ACS Appl. Mater. Inter., 2022, 14, 45263–45271.
81. O. Koga, T. Matsuo, H. Yamazaki and Y. Hori, Advances in Chemical Conversions for Mitigating Carbon Dioxide, 1998, vol. 114, pp. 569–572.
82. X. Liu, J. Xiao, H. Peng, X. Hong, K. Chan and J. K. Nørskov, Nat. Commun., 2017, 8, 15438.
83. J. D. Goodpaster, A. T. Bell and M. Head-Gordon, J. Phys. Chem. Lett., 2016, 7, 1471–1477.
84. H. Peng, M. T. Tang, X. Liu, P. Schlexer Lamoureux, M. Bajdich and F. Abild-Pedersen, Energy Environ. Sci., 2021, 14, 473–482.
85. H. Xiao, T. Cheng, W. A. Goddard, III and R. Sundararaman, J. Am. Chem. Soc., 2016, 138, 483–486.
86. G. Kastlunger, L. Wang, N. Govindarajan, H. H. Heenen, S. Ringe, T. Jaramillo, C. Hahn and K. Chan, ACS Catal., 2022, 12, 4344–4357.
87. X. Liu, P. Schlexer, J. Xiao, Y. Ji, L. Wang, R. B. Sandberg, M. Tang, K. S. Brown, H. Peng, S. Ringe, C. Hahn, T. F. Jaramillo, J. K. Nørskov and K. Chan, Nat. Commun., 2019, 10, 32.
88. A. J. Bard, L. R. Faulkner and H. S. White, Electrochemical methods: fundamentals and applications, John Wiley & Sons, 2022.
89. A. Baz and A. Holewinski, J. Catal., 2021, 397, 233–244.
90. C. T. Campbell, J. Catal., 2001, 204, 520–524.
91. C. T. Campbell, ACS Catal., 2017, 7, 2770–2779.
92. C. A. Wolcott, A. J. Medford, F. Studt and C. T. Campbell, J. Catal., 2015, 330, 197–207.
93. B. L. Foley and A. Bhan, J. Catal., 2020, 384, 231–251.
94. M. Liu, Y. Pang, B. Zhang, P. De Luna, O. Voznyy, J. Xu, X. Zheng, C. T. Dinh, F. Fan, C. Cao, F. P. G. de Arquer, T. S. Safaei, A. Mepham, A. Klinkova, E. Kumacheva, T. Filleter, D. Sinton, S. O. Kelley and E. H. Sargent, Nature, 2016, 537, 382–386.
95. Y. Chen, C. W. Li and M. W. Kanan, J. Am. Chem. Soc., 2012, 134, 19969–19972.
96. Y. Hori, A. Murata, K. Kikuchi and S. Suzuki, J. Chem. Soc., Chem. Commun., 1987, 728–729,  10.1039/C39870000728.
97. X. Zhu, J. Huang and M. Eikerling, ACS Catal., 2021, 11, 14521–14532.
98. M. Cho, J. T. Song, S. Back, Y. Jung and J. Oh, ACS Catal., 2018, 8, 1178–1185.
99. A. Thevenon, A. Rosas-Hernández, A. M. Fontani Herreros, T. Agapie and J. C. Peters, ACS Catal., 2021, 11, 4530–4537.
100. L. Xiong, X. Zhang, H. Yuan, J. Wang, X. Yuan, Y. Lian, H. Jin, H. Sun, Z. Deng and D. Wang, Angew. Chem., 2021, 133, 2538–2548.
101. J. L. Hitt, Y. C. Li, S. Tao, Z. Yan, Y. Gao, S. J. L. Billinge and T. E. Mallouk, Nat. Commun., 2021, 12, 1114.
102. M. Zhu, R. Ye, K. Jin, N. Lazouski and K. Manthiram, ACS Energy Lett., 2018, 3, 1381–1386.
103. L. Sun, V. Reddu, S. Xi, C. Dai, Y. Sheng, T. Su, A. C. Fisher and X. Wang, Adv. Energy Mater., 2022, 12, 2202108.
104. S. Liu, H. B. Yang, S.-F. Hung, J. Ding, W. Cai, L. Liu, J. Gao, X. Li, X. Ren, Z. Kuang, Y. Huang, T. Zhang and B. Liu, Angew. Chem., Int. Ed., 2020, 59, 798–803.
105. X. Wang, S. Feng, W. Lu, Y. Zhao, S. Zheng, W. Zheng, X. Sang, L. Zheng, Y. Xie, Z. Li, B. Yang, L. Lei, S. Wang and Y. Hou, Adv. Funct. Mater., 2021, 31, 2104243.
106. W. Zheng, Y. Wang, L. Shuai, X. Wang, F. He, C. Lei, Z. Li, B. Yang, L. Lei, C. Yuan, M. Qiu, Y. Hou and X. Feng, Adv. Funct. Mater., 2021, 31, 2008146.
107. T. Li, C. Yang, J.-L. Luo and G. Zheng, ACS Catal., 2019, 9, 10440–10447.
108. G. S. Prakash, F. A. Viva and G. A. Olah, J. Power Sources, 2013, 223, 68–73.
109. J. Zhang, T. Fan, P. Huang, X. Lian, Y. Guo, Z. Chen and X. Yi, Adv. Funct. Mater., 2022, 32, 2113075.
110. E. L. Clark, S. Ringe, M. Tang, A. Walton, C. Hahn, T. F. Jaramillo, K. Chan and A. T. Bell, ACS Catal., 2019, 9, 4006–4014.
111. N. Hoshi, M. Kato and Y. Hori, J. Electroanal. Chem., 1997, 440, 283–286.
112. T. Hatsukade, K. P. Kuhl, E. R. Cave, D. N. Abram and T. F. Jaramillo, Phys. Chem. Chem. Phys., 2014, 16, 13814–13819.
113. M. R. Singh, Y. Kwon, Y. Lum, J. W. Ager, III and A. T. Bell, J. Am. Chem. Soc., 2016, 138, 13006–13012.
114. E. L. Clark, J. Resasco, A. Landers, J. Lin, L.-T. Chung, A. Walton, C. Hahn, T. F. Jaramillo and A. T. Bell, ACS Catal., 2018, 8, 6560–6570.
115. A. A. Peterson and J. K. Nørskov, J. Phys. Chem. Lett., 2012, 3, 251–258.
116. F. Abild-Pedersen, J. Greeley, F. Studt, J. Rossmeisl, T. R. Munter, P. G. Moses, E. Skúlason, T. Bligaard and J. K. Nørskov, Phys. Rev. Lett., 2007, 99, 016105.
117. J. Pérez-Ramírez and N. López, Nat. Catal., 2019, 2, 971–976.
118. M. L. Pegis, C. F. Wise, B. Koronkiewicz and J. M. Mayer, J. Am. Chem. Soc., 2017, 139, 11000–11003.
119. Y. Li and Q. Sun, Adv. Energy Mater., 2016, 6, 1600463.
120. D.-H. Nam, P. De Luna, A. Rosas-Hernández, A. Thevenon, F. Li, T. Agapie, J. C. Peters, O. Shekhah, M. Eddaoudi and E. H. Sargent, Nat. Mater., 2020, 19, 266–276.
121. Z. Cao, J. S. Derrick, J. Xu, R. Gao, M. Gong, E. M. Nichols, P. T. Smith, X. Liu, X. Wen, C. Copéret and C. J. Chang, Angew. Chem., Int. Ed., 2018, 57, 4981–4985.
122. S. Ahn, K. Klyukin, R. J. Wakeham, J. A. Rudd, A. R. Lewis, S. Alexander, F. Carla, V. Alexandrov and E. Andreoli, ACS Catal., 2018, 8, 4132–4142.
123. Y. Fang and J. C. Flake, J. Am. Chem. Soc., 2017, 139, 3399–3405.
124. Z. Cao, D. Kim, D. Hong, Y. Yu, J. Xu, S. Lin, X. Wen, E. M. Nichols, K. Jeong, J. A. Reimer, P. Yang and C. J. Chang, J. Am. Chem. Soc., 2016, 138, 8120–8125.
125. R. Reske, H. Mistry, F. Behafarid, B. Roldan Cuenya and P. Strasser, J. Am. Chem. Soc., 2014, 136, 6978–6986.
126. Z. Han, D. Han, Z. Chen, J. Gao, G. Jiang, X. Wang, S. Lyu, Y. Guo, C. Geng, L. Yin, Z. Weng and Q.-H. Yang, Nat. Commun., 2022, 13, 3158.
127. X. F. Lu, H. A. Ahsaine, B. Dereli, A. T. Garcia-Esparza, M. Reinhard, T. Shinagawa, D. X. Li, K. Adil, M. R. Tchalala, T. Kroll, M. Eddaoudi, D. Sokaras, L. Cavallo and K. Takanabe, ACS Catal., 2021, 11, 6499–6509.
128. Z.-W. Yang, J.-M. Chen, L.-Q. Qiu, W.-J. Xie and L.-N. He, Angew. Chem., Int. Ed., 2022, 61, e202205301.
129. J. S. Zeng, N. Corbin, K. Williams and K. Manthiram, ACS Catal., 2020, 10, 4326–4336.
130. A. Wuttig, Y. Yoon, J. Ryu and Y. Surendranath, J. Am. Chem. Soc., 2017, 139, 17109–17113.
131. S. Ringe, E. L. Clark, J. Resasco, A. Walton, B. Seger, A. T. Bell and K. Chan, Energy Environ. Sci., 2019, 12, 3001–3014.
132. A. Murata and Y. Hori, Bull. Chem. Soc. Jpn., 1991, 64, 123–127.
133. A. S. Malkani, J. Li, N. J. Oliveira, M. He, X. Chang, B. Xu and Q. Lu, Sci. Adv., 2020, 6, eabd2569.
134. L. Wang, S. A. Nitopi, E. Bertheussen, M. Orazov, C. G. Morales-Guio, X. Y. Liu, D. C. Higgins, K. R. Chan, J. K. Norskov, C. Hahn and T. F. Jaramillo, ACS Catal., 2018, 8, 7445–7454.
135. J. Li, K. Chang, H. Zhang, M. He, W. A. Goddard, J. G. Chen, M.-J. Cheng and Q. Lu, ACS Catal., 2019, 9, 4709–4718.
136. J. Li, X. Chang, H. Zhang, A. S. Malkani, M.-J. Cheng, B. Xu and Q. Lu, Nat. Commun., 2021, 12, 1–11.
137. X. Chang, J. Li, H. Xiong, H. Zhang, Y. Xu, H. Xiao, Q. Lu and B. Xu, Angew. Chem., Int. Ed., 2022, 61, e202111167.
138. H. Shen, Y. Wang, T. Chakraborty, G. Zhou, C. Wang, X. Fu, Y. Wang, J. Zhang, C. Li and F. Xu, ACS Catal., 2022, 12, 5275–5283.
139. J. Hou, X. Chang, J. Li, B. Xu and Q. Lu, J. Am. Chem. Soc., 2022, 144, 22202–22211.
140. X. Fu, Y. Wang, H. Shen, Y. Yu, F. Xu, G. Zhou, W. Xie, R. Qin, C. Dun, C. W. Pao, J. L. Chen, Y. Liu, J. Guo, Q. Yue, J. J. Urban, C. Wang and Y. Kang, Mater. Today Phys., 2021, 19, 100418.
141. Y. Zheng, A. Vasileff, X. Zhou, Y. Jiao, M. Jaroniec and S.-Z. Qiao, J. Am. Chem. Soc., 2019, 141, 7646–7659.
142. M. Schreier, Y. Yoon, M. N. Jackson and Y. Surendranath, Angew. Chem., Int. Ed., 2018, 57, 10221–10225.
143. O. Koga, Y. Watanabe, M. Tanizaki and Y. Hori, Electrochim. Acta, 2001, 46, 3083–3090.
144. T. Burdyny and W. A. Smith, Energy Environ. Sci., 2019, 12, 1442–1453.
145. X. Lu, T. Shinagawa and K. Takanabe, ACS Catal., 2023, 13, 1791–1803.
146. A. C. Lausche, A. J. Medford, T. S. Khan, Y. Xu, T. Bligaard, F. Abild-Pedersen, J. K. Nørskov and F. Studt, J. Catal., 2013, 307, 275–282.
147. P. Hollins and J. Pritchard, Surf. Sci., 1979, 89, 486–495.
148. R. B. Sandberg, J. H. Montoya, K. Chan and J. K. Nørskov, Surf. Sci., 2016, 654, 56–62.
149. M. Jouny, G. S. Hutchings and F. Jiao, Nat. Catal., 2019, 2, 1062–1070.
150. M. Jouny, W. Luc and F. Jiao, Nat. Catal., 2018, 1, 748–755.
151. X. Wang, Y. Jiao, L. Li, Y. Zheng and S.-Z. Qiao, Angew. Chem., Int. Ed., 2022, 61, e202114253.
152. S. Narayanaru, J. Chinnaiah, K. L. Phani and F. Scholz, Electrochim. Acta, 2018, 264, 269–274.
153. F. Che, J. T. Gray, S. Ha, N. Kruse, S. L. Scott and J.-S. McEwen, ACS Catal., 2018, 8, 5153–5174.
154. J. H. Montoya, C. Shi, K. Chan and J. K. Nørskov, J. Phys. Chem. Lett., 2015, 6, 2032–2037.
155. Y. Lum, T. Cheng, W. A. Goddard, III and J. W. Ager, J. Am. Chem. Soc., 2018, 140, 9337–9340.
156. K. Obata and F. F. Abdi, Sustainable Energy Fuels, 2021, 5, 3791–3801.
157. Y. Lin, C. Deng, L. Wu, Y. Zhang, C. Chen, W. Ma and J. Zhao, Energy Environ. Sci., 2020, 13, 2602–2617.
158. G. Liu, W. S. Y. Wong, M. Kraft, J. W. Ager, D. Vollmer and R. Xu, Chem. Soc. Rev., 2021, 50, 10674–10699.
159. C. Balzarek, T. J. R. Weakley and D. R. Tyler, J. Am. Chem. Soc., 2000, 122, 9427–9434.
160. W. H. Bernskoetter, E. Lobkovsky and P. J. Chirik, J. Am. Chem. Soc., 2005, 127, 14051–14061.
161. P. Hauwert, R. Boerleider, S. Warsink, J. J. Weigand and C. J. Elsevier, J. Am. Chem. Soc., 2010, 132, 16900–16910.
162. C. L. Perrin, T. J. Dwyer and P. Baine, J. Am. Chem. Soc., 1994, 116, 4044–4049.
163. S. E. Scheppele, Chem. Rev., 1972, 72, 511–532.
164. M. Gómez-Gallego and M. A. Sierra, Chem. Rev., 2011, 111, 4857–4963.
165. W. Deng, P. Zhang, B. Seger and J. Gong, Nat. Commun., 2022, 13, 803.
166. X. Tian, P. Zhao and W. Sheng, Adv. Mater., 2019, 31, 1808066.
167. Y. Pocker and D. W. Bjorkquist, J. Am. Chem. Soc., 1977, 99, 6537–6543.
168. E. D. German and M. Sheintuch, J. Phys. Chem. C, 2010, 114, 3089–3097.
169. W. Ma, S. Xie, X.-G. Zhang, F. Sun, J. Kang, Z. Jiang, Q. Zhang, D.-Y. Wu and Y. Wang, Nat. Commun., 2019, 10, 892.
170. W. Ma, S. Xie, T. Liu, Q. Fan, J. Ye, F. Sun, Z. Jiang, Q. Zhang, J. Cheng and Y. Wang, Nat. Catal., 2020, 3, 478–487.
171. X. Guo, S.-M. Xu, H. Zhou, Y. Ren, R. Ge, M. Xu, L. Zheng, X. Kong, M. Shao, Z. Li and H. Duan, ACS Catal., 2022, 12, 10551–10559.
172. S. Dey, T. K. Todorova, M. Fontecave and V. Mougel, Angew. Chem., Int. Ed., 2020, 59, 15726–15733.
173. A. Khadhraoui, P. Gotico, W. Leibl, Z. Halime and A. Aukauloo, ChemSusChem, 2021, 14, 1308–1315.
174. K. Saihara, Y. Yoshimura, S. Ohta and A. Shimizu, Sci. Rep., 2015, 5, 10619.
175. A. Verma, J. P. Stoppelman and J. G. McDaniel, Int. J. Mol. Sci., 2020, 21, 403.
176. C. Ma, A. Laaksonen, C. Liu, X. Lu and X. Ji, Chem. Soc. Rev., 2018, 47, 8685–8720.
177. S. E. Tignor, T. W. Shaw and A. B. Bocarsly, Dalton Trans., 2019, 48, 12730–12737.
178. Y. Liu and C. C. L. McCrory, Nat. Commun., 2019, 10, 1683.
179. R. Wang, X. Wang, W. Weng, Y. Yao, P. Kidkhunthod, C. Wang, Y. Hou and J. Guo, Angew. Chem., Int. Ed., 2022, 61, e202115503.
180. J. Chen, M. Zhu, J. Li, J. Xu and Y.-F. Han, J. Phys. Chem. C, 2020, 124, 16501–16507.
181. E. J. Dufek, T. E. Lister and M. E. McIlwain, J. Appl. Electrochem., 2011, 41, 623–631.
182. S. M. A. Kriescher, K. Kugler, S. S. Hosseiny, Y. Gendel and M. Wessling, Electrochem. Commun., 2015, 50, 64–68.
183. B. Endrődi, G. Bencsik, F. Darvas, R. Jones, K. Rajeshwar and C. Janáky, Prog. Energy Combust. Sci., 2017, 62, 133–154.
184. H. Yoshio, K. Katsuhei, M. Akira and S. Shin, Chem. Lett., 1986, 897–898.
185. S. T. Ahn, I. Abu-Baker and G. T. R. Palmore, Catal. Today, 2017, 288, 24–29.
186. M. Azuma, K. Hashimoto, M. Hiramoto, M. Watanabe and T. Sakata, J. Electrochem. Soc., 1990, 137, 1772.
187. T. Mizuno, K. Ohta, M. Kawamoto and A. Saji, Energy Sources, 1997, 19, 249–257.
188. A. Löwe, C. Rieg, T. Hierlemann, N. Salas, D. Kopljar, N. Wagner and E. Klemm, ChemElectroChem, 2019, 6, 4497–4506.
189. Z. Yin, H. Peng, X. Wei, H. Zhou, J. Gong, M. Huai, L. Xiao, G. Wang, J. Lu and L. Zhuang, Energy Environ. Sci., 2019, 12, 2455–2462.
190. J. Wei, X. Jian, C. Wu, S. Zhang and C. Yan, J. Membr. Sci., 2005, 256, 116–121.
191. J. Wang, G. Chen and S. Song, Electrochim. Acta, 2020, 330, 135322.
192. Shalu, S. K. Chaurasia, R. K. Singh and S. Chandra, J. Phys. Chem. B, 2013, 117, 897–906.
193. M. Chandesris, V. Médeau, N. Guillet, S. Chelghoum, D. Thoby and F. Fouda-Onana, Int. J. Hydrogen Energy, 2015, 40, 1353–1366.
194. J.-B. Vennekötter, T. Scheuermann, R. Sengpiel and M. Wessling, J. CO2 Util., 2019, 32, 202–213.
195. D. Kim, W. Choi, H. W. Lee, S. Y. Lee, Y. Choi, D. K. Lee, W. Kim, J. Na, U. Lee, Y. J. Hwang and D. H. Won, ACS Energy Lett., 2021, 6, 3488–3495.
196. R. E. Vos and M. T. M. Koper, ChemElectroChem, 2022, 9, e202200239.
197. H. H. Heenen, H. Shin, G. Kastlunger, S. Overa, J. A. Gauthier, F. Jiao and K. Chan, Energy Environ. Sci., 2022, 15, 3978–3990.
198. O. Christensen, S. Zhao, Z. Sun, A. Bagger, J. V. Lauritsen, S. U. Pedersen, K. Daasbjerg and J. Rossmeisl, ACS Catal., 2022, 12, 15737–15749.
199. E. Bertheussen, T. V. Hogg, Y. Abghoui, A. K. Engstfeld, I. Chorkendorff and I. E. L. Stephens, ACS Energy Lett., 2018, 3, 634–640.
200. D. S. Ripatti, T. R. Veltman and M. W. Kanan, Joule, 2019, 3, 240–256.
201. D. Salvatore and C. P. Berlinguette, ACS Energy Lett., 2019, 5, 215–220.
202. S. Anantharaj and S. Noda, J. Mater. Chem. A, 2022, 10, 9348–9354.
203. Y. Hori, A. Murata and R. Takahashi, J. Chem. Soc., Faraday Trans. 1, 1989, 85, 2309–2326.
204. A. R. Heenan, J. Hamonnet and A. T. Marshall, ACS Energy Lett., 2022, 7, 2357–2361.
205. P. Grosse, D. Gao, F. Scholten, I. Sinev, H. Mistry and B. Roldan Cuenya, Angew. Chem., Int. Ed., 2018, 57, 6192–6197.
206. S. Popović, M. Smiljanić, P. Jovanovič, J. Vavra, R. Buonsanti and N. Hodnik, Angew. Chem., Int. Ed., 2020, 59, 14736–14746.
207. P. Sebastián-Pascual, S. Mezzavilla, I. E. L. Stephens and M. Escudero-Escribano, ChemCatChem, 2019, 11, 3626–3645.
208. J. Vavra, T.-H. Shen, D. Stoian, V. Tileli and R. Buonsanti, Angew. Chem., Int. Ed., 2021, 60, 1347–1354.
209. X. Wang, K. Klingan, M. Klingenhof, T. Möller, J. Ferreira de Araújo, I. Martens, A. Bagger, S. Jiang, J. Rossmeisl, H. Dau and P. Strasser, Nat. Commun., 2021, 12, 794.
210. H. Jung, S. Y. Lee, C. W. Lee, M. K. Cho, D. H. Won, C. Kim, H.-S. Oh, B. K. Min and Y. J. Hwang, J. Am. Chem. Soc., 2019, 141, 4624–4633.
211. W. Zheng, J. Yang, H. Chen, Y. Hou, Q. Wang, M. Gu, F. He, Y. Xia, Z. Xia, Z. Li, B. Yang, L. Lei, C. Yuan, Q. He, M. Qiu and X. Feng, Adv. Funct. Mater., 2020, 30, 1907658.
212. D. Ren, Y. Deng, A. D. Handoko, C. S. Chen, S. Malkhandi and B. S. Yeo, ACS Catal., 2015, 5, 2814–2821.
213. B. Mei, C. Liu, J. Li, S. Gu, X. Du, S. Lu, F. Song, W. Xu and Z. Jiang, J. Energy Chem., 2022, 64, 1–7.
214. A. Zhang, Y. Liang, H. Li, B. Zhang, Z. Liu, Q. Chang, H. Zhang, C.-F. Zhu, Z. Geng, W. Zhu and J. Zeng, Nano Lett., 2020, 20, 8229–8235.
215. X. Wu, Y. Guo, Z. Sun, F. Xie, D. Guan, J. Dai, F. Yu, Z. Hu, Y.-C. Huang, C.-W. Pao, J.-L. Chen, W. Zhou and Z. Shao, Nat. Commun., 2021, 12, 660.
216. B. Qin, Y. Li, H. Wang, G. Yang, Y. Cao, H. Yu, Q. Zhang, H. Liang and F. Peng, Nano Energy, 2019, 60, 43–51.
217. B. Huang, K. H. Myint, Y. Wang, Y. Zhang, R. R. Rao, J. Sun, S. Muy, Y. Katayama, J. Corchado Garcia and D. Fraggedakis, J. Phys. Chem. C, 2021, 125, 4397–4411.
218. G. Hussain, L. Pérez-Martínez, J.-B. Le, M. Papasizza, G. Cabello, J. Cheng and A. Cuesta, Electrochim. Acta, 2019, 327, 135055.
219. S. Ringe, E. L. Clark, J. Resasco, A. Walton, B. Seger, A. T. Bell and K. Chan, Energy Environ. Sci., 2019, 12, 3001–3014.
220. J. Li, D. Wu, A. S. Malkani, X. Chang, M.-J. Cheng, B. Xu and Q. Lu, Angew. Chem., Int. Ed., 2020, 59, 4464–4469.
221. M. C. O. Monteiro, F. Dattila, B. Hagedoorn, R. García-Muelas, N. López and M. T. M. Koper, Nat. Catal., 2021, 4, 654–662.
222. G. Marcandalli, M. C. O. Monteiro, A. Goyal and M. T. M. Koper, Acc. Chem. Res., 2022, 55, 1900–1911.
223. A. Goyal, G. Marcandalli, V. A. Mints and M. T. M. Koper, J. Am. Chem. Soc., 2020, 142, 4154–4161.
224. M. C. O. Monteiro, F. Dattila, N. López and M. T. M. Koper, J. Am. Chem. Soc., 2022, 144, 1589–1602.
225. A. Goyal, G. Marcandalli, V. A. Mints and M. T. Koper, J. Am. Chem. Soc., 2020, 142, 4154–4161.
226. C. J. Bondue, M. Graf, A. Goyal and M. T. Koper, J. Am. Chem. Soc., 2020, 143, 279–285.
227. J. Li, X. Li, C. M. Gunathunge and M. M. Waegele, Proc. Natl. Acad. Sci. U. S. A., 2019, 116, 9220–9229.
228. X. Lu, C. Zhu, Z. Wu, J. Xuan, J. S. Francisco and H. Wang, J. Am. Chem. Soc., 2020, 142, 15438–15444.
229. H. H. Bauer, J. Electroanal. Chem. Interfacial Electrochem., 1968, 16, 419–432.
230. J.-M. Savéant and D. Tessier, Faraday Discuss. Chem. Soc., 1982, 74, 57–72.
231. J. T. Hupp, H. Y. Liu, J. K. Farmer, T. Gennett and M. J. Weaver, J. Electroanal. Chem. Interfacial Electrochem., 1984, 168, 313–334.
232. J. N. Soderberg, A. C. Co, A. H. C. Sirk and V. I. Birss, J. Phys. Chem. B, 2006, 110, 10401–10410.
233. A. T. Marshall and L. Vaisson-Béthune, Electrochem. Commun., 2015, 61, 23–26.
234. Y. J. Sa, C. W. Lee, S. Y. Lee, J. Na, U. Lee and Y. J. Hwang, Chem. Soc. Rev., 2020, 49, 6632–6665.
235. J. A. Gauthier, S. Ringe, C. F. Dickens, A. J. Garza, A. T. Bell, M. Head-Gordon, J. K. Nørskov and K. Chan, ACS Catal., 2019, 9, 920–931.
236. X. Zhang and Z. Zhou, J. Phys. Chem. C, 2022, 126, 3820–3829.
237. A. A. Peterson, F. Abild-Pedersen, F. Studt, J. Rossmeisl and J. K. Nørskov, Energy Environ. Sci., 2010, 3, 1311–1315.
238. X. Zhao and Y. Liu, J. Am. Chem. Soc., 2020, 142, 5773–5777.
239. Y. Basdogan, A. M. Maldonado and J. A. Keith, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 2020, 10, e1446.
240. X. Hu, S. Yao, L. Chen, X. Zhang, M. Jiao, Z. Lu and Z. Zhou, J. Mater. Chem. A, 2021, 9, 23515–23521.
241. J.-B. Le, Q.-Y. Fan, J.-Q. Li and J. Cheng, Sci. Adv., 2020, 6, eabb1219.
242. S. Naserifar and W. A. Goddard, Proc. Natl. Acad. Sci. U. S. A., 2019, 116, 1998–2003.
243. S. Naserifar, Y. Chen, S. Kwon, H. Xiao and W. A. Goddard, Matter, 2021, 4, 195–216.
244. L. Chen, X. Zhang, A. Chen, S. Yao, X. Hu and Z. Zhou, Chin. J. Catal., 2022, 43, 11–32.
245. A. Chen, X. Zhang and Z. Zhou, InfoMat, 2020, 2, 553–576.
246. O. van der Heijden, S. Park, J. J. J. Eggebeen and M. T. M. Koper, Angew. Chem., Int. Ed., 2023, 62, e202216477.
247. Y. C. Li, D. Zhou, Z. Yan, R. H. Gonçalves, D. A. Salvatore, C. P. Berlinguette and T. E. Mallouk, ACS Energy Lett., 2016, 1, 1149–1153.
248. K. P. Kuhl, E. R. Cave, D. N. Abram and T. F. Jaramillo, Energy Environ. Sci., 2012, 5, 7050–7059.
249. Y. Zhao, X. Chang, A. S. Malkani, X. Yang, L. Thompson, F. Jiao and B. Xu, J. Am. Chem. Soc., 2020, 142, 9735–9743.
250. X. Chang, Y. Zhao and B. Xu, ACS Catal., 2020, 10, 13737–13747.
251. A. S. Malkani, M. Dunwell and B. Xu, ACS Catal., 2019, 9, 474–478.
252. A. Eilert, F. Cavalca, F. S. Roberts, J. R. Osterwalder, C. Liu, M. Favaro, E. J. Crumlin, H. Ogasawara, D. Friebel and L. G. Pettersson, J. Phys. Chem. Lett., 2017, 8, 285–290.
253. B. Hasa, M. Jouny, B. H. Ko, B. Xu and F. Jiao, Angew. Chem., Int. Ed., 2021, 60, 3277–3282.
254. S. Bhandari, S. Rangarajan and M. Mavrikakis, Acc. Chem. Res., 2020, 53, 1893–1904.
255. J. Resasco, F. Abild-Pedersen, C. Hahn, Z. Bao, M. T. M. Koper and T. F. Jaramillo, Nat. Catal., 2022, 5, 374–381.
256. P. Hutchison, C. J. Kaminsky, Y. Surendranath and S. Hammes-Schiffer, ACS Central Sci., 2023, 9, 927–936.
257. S. Oh, J. R. Gallagher, J. T. Miller and Y. Surendranath, J. Am. Chem. Soc., 2016, 138, 1820–1823.

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