# Benchmarking three-dimensional metal foam electrodes for the electrochemical reduction of nitrate

Yenny Maribel Allcca Castillo, Yanis Adjez, Victor Raul Jauja Ccana, Gabriel Cerrón-Calle, Sergi Garcia-Segura, Adolfo La Rosa-Toro Gómez, Carlos M Sánchez-Sánchez

► To cite this version:

Yenny Maribel Allcca Castillo, Yanis Adjez, Victor Raul Jauja Ccana, Gabriel Cerrón-Calle, Sergi Garcia-Segura, et al.. Benchmarking three-dimensional metal foam electrodes for the electrochemical reduction of nitrate. Journal of Electroanalytical Chemistry, 2024, 968, pp.118499. 10.1016/j.jelechem.2024.118499. hal-04679801

HAL Id: hal-04679801

https://hal.sorbonne-universite.fr/hal-04679801v1

Submitted on 28 Aug 2024

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Distributed under a Creative Commons Attribution 4.0 International License
1 Benchmarking Three-Dimensional Metal Foam Electrodes for the

2 Electrochemical Reduction of Nitrate

3 Yenny Maribel Allcca Castilloª, Yanis Adjezᵇ, Victor Raul Jauja Ccanaª, Gabriel A. Cerrón-

4 Calleᶜ, Sergi Garcia-Seguraᶜ, Adolfo La Rosa-Toro Gómezª*, Carlos M. Sánchez-Sánchezᵇ**

5

6 ªLaboratorio de Investigación de Electroquímica Aplicada, Facultad de Ciencias, Universidad

7 Nacional de Ingeniería, Av. Túpac Amaru 210, Rimac, Lima, Perú.

8 ᵇSorbonne Université, CNRS, Laboratoire Interfaces et Systèmes Electrochimiques, LISE, 4

9 Place Jussieu, 75005, Paris, France.

10 cNanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment,

11 School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe,

12 AZ 85287-3005, USA

13

14

15 Review article to be published in Journal of Electroanalytical Chemistry

16

17

18

19 Corresponding authors:

20 * [toro@uni.edu.pe](mailto:toro@uni.edu.pe)

21 ** [carlos.sanchez@sorbonne-universite.fr](mailto:carlos.sanchez@sorbonne-universite.fr)

22
23 Abstract

The electrochemical reduction of nitrate (ERN) is a promising and sustainable strategy for addressing the critical issue of nitrate pollution in water sources. The rational design of electrocatalysts has spotlighted metal-based three-dimensional (3D) electrodes such as Cu and Ni foams. Metallic Cu foam showcases promising kinetics for ERN conversion, while Ni foam serves as a robust support material for self-standing catalyst evaluation. This review underscores the nuances and challenges in ERN research when exploiting 3D electrodes, emphasizing the overlooked roles of pore per inch (PPI) and electrochemically active surface area (ECSA), as well as the lack of standardization and inconsistent reporting practices, which hinders direct performance comparison among different ERN studies. The PPI value controls reactant and products mass transfer at the interface and impacts on the magnitude and extension of the solution alkalinization at the electrode surface, while ECSA is essential for accurately comparing the specific electrocatalytic activity of metal foam electrodes for ERN. Particular attention is devoted to modified self-standing 3D electrode materials, where the catalytic performance on either Cu or Ni foams is altered by adding other metals and/or metal oxides (Cu<sub>foam</sub>/M) and (Ni<sub>foam</sub>/M), respectively. A detailed analysis of selected modified Cu and Ni foam electrocatalysts available in the literature is provided to demonstrate the lack of standardization reporting ERN performance at present. Thus, we propose the adoption of more rigorous characterization and reporting practices to advance the development of efficient and scalable ERN systems for sustainable water treatment. This includes PPI value, ECSA determination and additional engineering figures of merit encompassing both selectivity and conversion such as NH<sub>3</sub> generation efficiency (%) and ERN energy efficiency (%). Parameters such as nitrate conversion, faradaic efficiency and selectivity, often used as benchmarks, are considered not suitable enough for comparing different ERN studies because they depend on the initial nitrate concentration, the total charge circulated and the type of electrolysis (galvanostatic or potentiostatic).

48 **Keywords:** Nitrate reduction; Electrocatalysis; Cu foam; Ni foam; Benchmark; ECSA

49
# 1. Introduction

The never-ending search for water treatment alternatives to secure access to potable water has advanced our understanding of technologies in the water-energy nexus [1,2]. Electrocatalysis has been positioned as one of the most promising emerging sustainable technologies, transforming pollutants into harmless compounds or added-value products without introducing extra chemicals [3]. Electrified technologies use electrons to drive reactions of interest. Electrons are deemed green chemicals when derived from renewable and clean sources of electricity. Therefore, electrocatalysis offers appealing advantages compared to the conventional physical and biochemical removal of pollutants [4,5].

Nitrate ($NO_3^-$) pollution is one of the top ten water quality violations worldwide [6,7]. The treatment of this challenging oxyanion is not trivial. Nitrate, as a ubiquitous contaminant, has been treated by centralized large-scale methods such as reverse osmosis, ion exchange, and bioremediation [8–11]. However, further treating brines and sludge from these approaches increases operational costs by more than 30% [12]. In this context, electrochemistry may open new treatment avenues for sustainable management of nitrate pollution [13]. The electrochemical reduction of nitrate (ERN) has two main goal products depending on the control on selective transformation: nitrogen gas ($N_2$) and ammonia ($NH_3$). Product selectivity might be attained by controlling specific experimental conditions, particularly the choice of electrode material utilized [14–18] and the electrolyte composition [19,20]. For drinking water applications, the focus lies on the obtention of harmless $N_2$ gas [21]. However, recent efforts have concentrated on producing $NH_3$ as an added-value product [22,23] in line with ongoing efforts in the electrochemical reduction of nitrogen [24,25]. This resource recovery approach has become a hot topic in environmental research. Although this approach is in its infancy, the development of feasible applications is becoming more achievable by incorporating $NO_3^-$ pre-concentration systems, $NO_3^-$ conversion systems, and $NH_3$ recovery units.
Concerning $NO_3^-$ electro-conversion systems, current research focuses on identifying materials that exhibit high performance in $NO_3^-$ conversion and demonstrate superior selectivity toward $NH_3$ production [26–29]. While significant progress has been made in this aspect, fewer studies have concentrated on scaling this process for real-world applications. To align this approach on a large scale, several critical challenges must be addressed, including the utilization of free-standing electrodes [30], stability against inorganic scaling produced in real water matrices [31], designing suitable reactor configurations to maximize electrode utilization [32], and achieving high $NH_3$ yield (measured in mmol $NH_3$ $g_{cat}^{-1}$ h$^{-1}$ or mmol $NH_3$ $cm_{cat}^{-2}$ h$^{-1}$) [33]. In response to these essential requirements, open-pore metal foam electrodes have emerged as a promising and viable configuration to overcome these challenges. Metallic foams offer various advantages, including enhanced mass transport, excellent electrical conductivity, high electrochemical surface area, and adaptability to different reactor configurations compared to two-dimensional electrodes [34–36]. For instance, a comparison between Cu and Ni plate and foam electrodes performance after an identical amount of charge transferred during ERN electrolysis showed higher nitrate conversion (%), calculated using Equation 1, on Cu foam than on plate, but almost identical values were reached on Ni foam and plate electrodes, as indicated in Table 1.

$$Nitrate conversion (\%) = \frac{C_{NO_3^- ,0}}{C_{NO_3^- ,0} - C_{NO_3^- ,t}} \times 100 \quad (1)$$

where $C_{NO_3^- ,0}$ is the initial nitrate concentration (mg $NO_3^-$-N L$^{-1}$), and $C_{NO_3^- ,t}$ is the nitrate concentration at time t (mg $NO_3^-$-N L$^{-1}$).

Thus, the role of metal foams during the ERN may vary depending on their composition, which influences their characterization and activity evaluation. Metal foam applied for ERN can be classified into three types: i) active materials (such as Cu foam), ii) support materials (inactive current collectors, like Ni foam), and iii) modified materials (catalyst-supported electrodes, where foam electrodes—whether active or inactive—are fully or partially coated with an additional catalytic material, denoted as $Cu_{foam}$/M and $Ni_{foam}$/M, respectively).
**Table 1.** Comparison of ERN galvanostatic electrolysis results on Cu and Ni electrodes using plate and foam electrodes. Plate electrodes were evaluated at 20 mA cm⁻² during 90 min on 6.0 cm² electrodes (Q = 648 C). Initial solution composition: 100 mg L⁻¹ NO₃⁻-N in 50 mM Na₂SO₄. Foam electrodes were evaluated at 40 mA cm⁻² during 120 min on 2.25 cm² electrodes (Q = 648 C). Initial solution composition: 30 mg L⁻¹ NO₃⁻-N in 12.5 mM Na₂SO₄.

<table><thead><tr><th>Element</th><th>Electrode Configuration</th><th>Nitrate Conversion (%)</th></tr></thead><tbody><tr><td rowspan="2">Cu</td><td>Plate [37]</td><td>22 ± 2</td></tr><tr><td>Foam [38]</td><td>55 ± 4</td></tr><tr><td rowspan="2">Ni</td><td>Plate [37]</td><td>8 ± 2</td></tr><tr><td>Foam [39]</td><td>10 ± 2</td></tr></tbody></table>

106

Independently of the initial pH solution, the ERN mechanism yields hydroxide ions that increase solution pH, reaching in some instances values as high as pH 11. The solution alkalinization during ERN promotes the generation of oxides and hydroxides on the metallic electrode surface. Consequently, the coexistence of metal, oxide, and hydroxide sites on the foam electrode surface increases entropy and facilitates the provision of adsorption sites during ERN. Table 2 provides a summary of the bulk resistivity of various materials, including C, Ni, Cu, Cu-based oxides, Ni-based oxides, Al₂O₃, and SiO₂. These values underscore the exceptional electrical conductivity of Cu and Ni foams and exhibit much lower bulk resistivity (Ω cm) for passive Cu and Ni-based oxides than for insulating-type oxides such as Al₂O₃ and SiO₂. Therefore, the presence of Cu- and Ni-based oxides on the foam surface does not significantly impact the overall conductivity of the electrode. On the contrary, the presence of these oxides in small quantities can be advantageous for increasing the overall electrochemically active surface area (ECSA), number of adsorption sites, and electrode stability [40]. In fact, some studies suggest that these metal oxides may have higher electrocatalytic activity on the reduction of oxyanions [41–43].
122 **Table 2. Bulk resistivity (Ω cm) of various materials considered as conductors, semiconductors, and insulators.**
123

<table><thead><tr><th>Material</th><th>Bulk resistivity<br>(Ω cm)</th></tr></thead><tbody><tr><td>Carbon foam*</td><td>32.3 x 10<sup>-2</sup></td></tr><tr><td>Nickel foam**</td><td>6.9 x 10<sup>-6</sup></td></tr><tr><td>Cu foam*</td><td>65.2 x 10<sup>-6</sup></td></tr><tr><td>Cu<sub>2</sub>O[44]</td><td>3.7 x 10<sup>3</sup></td></tr><tr><td>CuO[44]</td><td>1.1 x 10<sup>3</sup></td></tr><tr><td>NiO<sub>x</sub>[45]</td><td>1.0 x 10<sup>2</sup> – 1.0 x 10<sup>9</sup></td></tr><tr><td>Ni(OH)<sub>x</sub>[46]</td><td>1.0 x 10<sup>5</sup> – 1.0 x 10<sup>6</sup></td></tr><tr><td>Al<sub>2</sub>O<sub>3</sub>[47]</td><td>1.0 x 10<sup>15</sup> – 1.0 x 10<sup>17</sup></td></tr><tr><td>SiO<sub>2</sub>[47]</td><td>1.0 x 10<sup>14</sup> – 1.0 x 10<sup>16</sup></td></tr></tbody></table>

124 *Values provided from the manufacturer: ERG Aerospace Corporation

125 **Values provided from the manufacturer: ATT Advanced elemental materials

126 This critical review delves into the fundamental parameters of three-dimensional (3D) foam electrodes and underscores the importance of accurate interpretation for reporting ERN performance. It looks into the multifaceted role of metal foams, exploring its functionality as an active material, support material, and modified foam. The review encompasses pertinent examples of 3D modified electrodes aimed at enhancing electrocatalytic performance in ERN, along with a discussion of the most recent modification strategies. By presenting these materials, the review offers an updated overview of the current state-of-the-art in this specialized research domain. Furthermore, the review extends its perspective to the future by outlining potential directions and identifying gaps that require attention for the further development of 3D foam-based electrocatalysts in ERN applications.

136
137

## 2. Defining key characterization parameters of metal-based three-dimensional foam electrodes

138

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148

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150

Considering their distinctive features, 3D metal foam electrodes (e.g., Cu, Ni, Co, Al) have emerged as the primary substrate or active material for electrocatalysis in many fields [48–50]. The 3D configuration provides excellent electrical properties and a smart spatial configuration that results in outstanding ERN performance. The choice of conductive material depends on the role of the electrode during the ERN, which may be a support electrode, active electrode, or both [51–53]. This classification is determined by the electrocatalytic response of the pristine foams and modified materials in the presence of $NO_3^-$, as evidenced by the increase in current density, as illustrated in Figure 1. The most determining properties to characterize in these materials are pore per inch (PPI) and electrochemically active surface area (ECSA). These physical descriptors play a relevant impact in catalytic activity evaluation as they drive the comparisons in terms of mass transfer efficiency and applied current density (j, mA cm$^{-2}$), respectively.
<figure><img src="image_2.png" /></figure>

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151

152 **Figure 1.** Classification of foam electrode: (a) active material, (b) support material, and (c) active or support modified material. Linear sweep voltammetry of (d) Cu<sub>foam</sub>, (e) Ni<sub>foam</sub>, and (f) Cu<sub>foam</sub>/Co(OH)<sub>x</sub> conducted in Na<sub>2</sub>SO<sub>4</sub> solution in absence and presence of NaNO<sub>3</sub> at 50 mV s<sup>-1</sup>.

153

155 Adapted from [17,38,39].

156

157
## 2.1 On the relevance of emptiness: describing pore per inch (PPI)

The PPI is a commonly overlooked but crucial parameter to characterize metal foam materials. The PPI refers to the number of pores present per linear inch of the foam structure, giving a direct average value of emptiness contained in the 3D structure [54]. The number of pores can indirectly indicate average pore size/diameter since smaller pores can be encapsulated with a higher density in a linear inch. Thus, higher PPI values are generally associated with notoriously smaller pore sizes and less efficient mass transfer. Therefore, the PPI value is essential for the possible implementation of 3D foam electrodes in flow-through and flow-by electrochemical reactor designs, as it controls the hydrodynamic behavior of solution flowing through the interconnected pores in the foam [55,56]. For instance, the electrochemical characterization of reactors (flow-through and flow-by) can be performed using the volumetric mass transport coefficient ($k_m A$) for a process controlled by mass transfer. This parameter, typically evaluated using the limiting current from a monoelectronic redox mediator in solution such as ferrocyanide ion over various electrolyte mean linear flow velocities, provides both mass transfer coefficient ($k_m$, m s⁻¹) and electroactive specific area ($A_s$, m⁻¹) properties in flow-by reactors [57–62]. The $k_m A$ (s⁻¹) is determined from the limiting current $I_L$ (A) according to Equation (2)

$$k_m A_s = \frac{I_L}{n F C V_c} \qquad (2)$$

where n is the number of electrons involved in the reaction (n = 1, considering the redox couple Fe(CN)₆³⁻/Fe(CN)₆⁴⁻), F is the Faraday's constant (96485 C mol⁻¹), C is the bulk concentration of Fe(CN)₆³⁻ (mol m⁻³), and $V_c$ is the active volume (m³).

On the other hand, flow-through reactors can be also characterized using the $k_m A$ vs. $I_L$ relationship, according to Equation (3)

$$I_L = n F C u A_r \left[ 1 - e^{-\left(\frac{L k_m A_s}{u}\right)} \right] \qquad (3)$$
where *n* is the number of electrons involved in the electrochemical reaction, *u* is the flow rate (m s⁻¹), *A*ᵣ is the cross-sectional area (m²), and *L* is the thickness of the porous electrode (m) [63,64].

Figure 2 illustrates three Cu foams with increasing *PPIs* of 20, 40, and 110. The optical images (Figures 2a, b, and c) clearly illustrate the polydisperse nature of interconnected pores and the decreasing pore size distribution with increasing *PPI* number. Figures 2d, e, and f illustrate the heterogeneous morphology of the pores by scanning electron microscope (SEM) at low magnification (74X) and 5 keV. It can be observed that the average diameter of interconnected pores decreases in size with increasing *PPI*, going from an average value of 1.55 mm at 20 *PPI* down to 236 µm at 110 *PPI*.

In porous structures such as 3D metal foams, surface area (cm²) can be normalized in two distinct ways. Firstly, by areal density, which is related to the mass or amount of material (cm² g⁻¹). Secondly, the specific surface area (SSA) is expressed relative to the volume of the 3D foam (cm² cm⁻³). Both parameters hold particular significance in electrocatalysis, where processes are primarily driven by interfacial interactions. Notably, both areal density and *SSA* exhibit a linear increase with rising *PPI*.

Furthermore, the inherent tortuosity of foams with varying *PPI* significantly affects reactants and product mass transfer close to the foam surface. Higher *PPI* values lead to increased tortuosity, impacting the renewal of reactant from the bulk solution and the accumulation of products at the electrode surface. Conversely, lower *PPI* values can enhance mass transfer by facilitating concentration equilibrium between interfacial and bulk solution species [65,66]. In particular, the interfacial pH can drastically increase during ERN and differ from the bulk solution pH depending on operational conditions. Thus, the *PPI* value impacts the magnitude and extension of the solution alkalinization at the electrode surface. Subsequently, the local alkaline pH can exacerbate additional issues in complex water matrices due to Mg²⁺ and Ca²⁺ ions, resulting in inorganic scaling on the foam surface [31,67]. Consequently, different *PPI* values may influence the decrease in ERN activity on the foam due to inorganic scaling on the electrode surface.
<figure><img src="image_8.png" /><figcaption>**Figure 2.** Cu foam at 20, 40, and 110 PPI at different conditions: (a, b, c) optical imaging, and (d, e, f) SEM images to determine surface morphology. Cu foam 20 and 40 *PPI* were acquired from ERG Aerospace Corporation and 110 *PPI* from Futt.</figcaption></figure>

## 2.2 The interconnection of electrocatalytic response and the electrochemically active surface area (ECSA)

For applications in electrocatalysis, the reactive surface area is one of the most important parameters. In the case of 3D metal foams, the actual electroactive surface area is significantly different from the geometrical area. Thus, the specific surface area of a foam is the amount of surface area within a given bulk volume of foam. Consequently, various methods have been employed to estimate this relevant parameter. One commonly used technique for surface area measurement in catalyst's characterization is the gas adsorption, which is based on argon (Ar), krypton (Kr) or nitrogen ($N_2$) gas physisorption on the solid catalyst at cryogenic temperature by the Brunauer-Emmett-Teller (BET) approach [68,69]. However, this *ex-situ* technique faces challenges when examining samples that are predominantly macroporous (i.e., metal foams with low *PPI*) with a lower surface area in comparison with meso- and micro-porous materials (e.g., zeolites, activated carbons, and metal organic frameworks (MOFs)). Notably, metal foam electrodes typically represent low surface area samples (<1 $m^2$/g), rendering $N_2$ physisorption
unsuitable for most cases. Alternatively, Kr physisorption at 77 K has been successfully employed for evaluating surface area in metal foam electrodes [70]. Furthermore, mercury porosimetry has proposed as an alternative approach to assess the size distribution and porosity of metal-based 3D electrodes [71]. Giving that metal foam electrodes operate within an electrochemical solid/liquid interface during ERN, estimating and comparing their ECSA, which may not necessary equivalent to the total surface area estimated by gas physisorption or other physical methods [51], represents a major point of attention to benchmark different studies.

The ECSA denotes the active surface area actually involved in the electrochemical process [40,51,72,73]. The larger the ECSA, the greater the number of active sites available for electrochemical reactions to occur, leading to enhanced reaction rates and improved efficiency in a shorter treatment time. Although 3D metal foam electrode substrates can play different roles (i.e., active material, support material, or modified material), the ECSA can be evaluated regardless of that role. Thus, different single or multi-component foam-based materials can be benchmarked as electrocatalysts by normalizing their electrochemical response via their corresponding ECSA. The ECSA should be determined for the actual electrode under study since the bare metal foam electrodes might either diminish or increase their ECSA after incorporating an additional catalytic material on top.

Although multiple methods have been suggested for determining ECSA [72], discrepancies and errors persist in electrocatalyst research and make the present discussion timely. A common flaw is the misuse of the Randles-Sevcik equation to evaluate the ECSA for 3D materials. Randles-Sevcik relies on the use of an outer-sphere redox mediator in solution (i.e. $K_3Fe(CN)_6$) to estimate ECSA. However, Randles-Sevcik is not applicable to the case of metal foam electrodes since this approach is only strictly pertinent for evaluating planar electrodes or those with semi-infinite diffusion [74]. Hence, when used to determine the ECSA of 3D materials, the results are unreliable and mainly correspond to the 2D geometric surface area evaluation.

The ECSA of 3D metal foam electrodes can be estimated instead from the double layer capacitance ($C_{dl}$) and the specific capacitance ($C_s$) values, according to Equation (4):
$$ECSA (cm^2) = \frac{C_{dl}}{C_s} \qquad (4)$$

where $C_{dl}$ represents the double layer capacitance (µF) obtained either from cyclic voltammetry (CV) or electrochemical impedance spectroscopy (EIS); and $C_s$ the specific capacitance value (µF cm⁻²). The $C_s$ is generally defined for an ideal flat surface and depends on the nature of the catalyst (metal or metal oxide), as well as the solution pH (alkaline or acid). Different $C_s$ values can be found in the literature in the range from 20 to 80 µF cm⁻² [51,75], being in most cases around 40 µF cm⁻² for both Ni and Cu oxide in alkaline solution [40,51,73,75] and 20 µF cm⁻² for the case of metallic Ni in alkaline solution [36]. Thus, ECSA comparison is only possible between similar electrocatalysts evaluated under similar experimental conditions [72], which is the case of comparing bare metal foam electrodes (Cu or Ni foams) and multi-component foam-based electrodes (Cufoam/M or Nifoam/M electrodes) during ERN. Nevertheless, ECSA estimation contains a significant range of uncertainty due to the wide dispersion in $C_s$ values. The use of Equation (4) it is useful mainly for comparison purposes and not for obtaining absolute values [72].

The formation of an electrical double layer (EDL) at the electrode-electrolyte interface is a fundamental phenomenon in electrochemistry [76,77]. When an electrode is immersed in an electrolyte solution, ions from the solution are attracted to the electrode surface due to electrostatic forces. This phenomenon results in the formation of two layers of charge: one layer of ions adsorbed onto the electrode surface (the inner Helmholtz plane) and another layer of oppositely charged ions in the solution adjacent to the electrode surface (the outer Helmholtz plane). Under the assumption that no Faradaic processes take place within this narrow potential range (i.e., no electron transfer reactions involving redox electroactive species), the behavior of the double layer resembles that of a capacitor. In other words, the electrode-electrolyte interface defined by the double layer behaves like a capacitor. Consequently, the estimated $C_{dl}$ is directly connected to the ECSA, since an extended surface area facilitates increased ion adsorption, thus amplifying the electrical double layer and its capacitance. The most common electrochemical approach to estimate the $C_{dl}$ is based on capacitive current evaluation by CV as a function of scan rate in a
narrow non-Faradaic potential range, using Equation (5), which assumes an ideal capacitor behavior [78].

$$C_{dl} = \frac{\Delta I / 2}{v} \qquad (5)$$

where $\Delta I/2$ (A) denotes the half-current difference between the anodic and cathodic currents at central potential, and $v$ stands for the scan rate (V s⁻¹). The slope from the corresponding graphical representation provides the $C_{dl}$ value.

Despite the seemingly direct calculation of ECSA from CV, the procedure requires meticulous care. Several factors need consideration to obtain accurate $C_{dl}$ values, even for flat 2D electrodes. For the case of 3D metal foam-based electrodes, the task becomes more complicated. Essential aspects to be considered during the electroanalytical procedure include but are not limited to the electrical connections, effects of trapped air, electrode wettability, and surface tension. Note that several of these aspects are PPI-dependent. As reported by Morales et. al several steps should be considered in order to obtained reliable ECSA values [79]. Figure 3 details four main steps and electrochemical considerations essential for obtaining ECSA comparison among electrocatalysts.

Step 1: Initially it is crucial to perform CV scans over a wide potential range to identify regions where non-Faradaic current dominates and there is an absence of peaks or evidence of Faradaic current. Neglecting this step could lead to an inaccurate depiction of capacitor characteristics. As presented in Figure 3a, the non-Faradic region (enclosed by the green dotted square) can be identified in the wide potential range. It is important to recognize that the specific range varies based on the material electrochemical properties. Thus, it should be estimated in each individual case. While many studies typically employ the open circuit potential (OCP) value as the central potential, this approach is not universally applicable. For example, Cu foam presents a Faradaic oxidation process in close proximity to the OCP in 0.1 mol L⁻¹ Na₂SO₄ [17]. Moreover, when defining the range, it is essential to ensure that both cathodic and anodic currents overlap in two subsequent potential scans, typically conducted within a window of 100 and 500 mV (see
Figure 3b). This overlapping also depends on the electrode material and the scanned potential range.

Step 2: Researchers should optimize the data collection parameters since calculated capacitance values are sensitive to variations of these parameters. While recording CV at different scan rates, it is crucial for the operator to consider parameters such as current range and the size of voltage step. Although the current range can be set either automatically or manually, it may vary over several orders of magnitude depending on the range of scan rates employed. It is advisable to assess the noise in the measurement by comparing results obtained at different current range limits as presented in Figure 3c. An improper selection of the current range can introduce significant noise into the measurements. Therefore, conducting separate experiments for each scan rate, rather than using the same current range across both fast and slow scan rate, can be beneficial. Additionally, careful selection of the voltage step size is essential, especially when evaluations are carried out over a small range of 100 mV. Figure 3d shows the effect of choosing an unsuitable voltage step on the data collection during the assessment. The voltage step affects the CV profile near the upper and lower set potentials.

Step 3: Once the optimal data collection parameters have been identified, measurements should be carried out spanning a broad range of scan rates (i.e., from 500 to 5 mV s⁻¹). While higher scan rates might be applicable depending on the material being evaluated, slower rates can be time-consuming and may not provide significant information about capacitance of the system. Therefore, it is recommended to perform evaluations within the range of 250 to 5 mV s⁻¹ when working with metal foams. Figure 3e presents a CV evaluation without considering the optimal conditions, while Figure 3f denotes the CV performed at optimal conditions. Comparing both graphs, a current underestimation was produced when the CV conditions were not optimized. The improperly optimized parameters result in a CV profile with sharpened extremes, current noise, and lower current compared to the reference dotted line. Conversely, under optimized parameters, the CV profiles exhibit well-defined extremes, no evidence of noise within the selected current range, and higher current compared to the same reference dotted line.
Step 4 - Once the data are collected, the analysis can be undertaken by considering the cathodic current intensity, anodic current intensity, or the average of these two currents. The capacitance values are calculated as mentioned, using Equations (4) and (5). Figure 3g presents a comparison of capacitance values calculated using non-optimized and optimized conditions at different potential ranges. It is noteworthy that under non-optimized conditions, the calculated capacitance underestimates the system capacitance, whereas under optimized conditions, the capacitance is 1.6 times higher than non-optimized conditions. Hence, determining the ECSA appropriately is crucial for comparing electrocatalysts. It is important to realize that each material requires unique conditions, and ECSA should be reported by the most appropriate evaluation.

<figure><img src="image_9.png" /><figcaption>**Figure 3.** (a) Cyclic voltammetry of an electrochemical cell at 10 mV s⁻¹ to identify measurement window. (b) Cyclic voltammetry at 500 mV s⁻¹ considering different measurement windows. (c) Resistor-capacitor circuit evaluated at different maximum current parameter values and (d) evaluation of the step size in the data collection RC-circuit. Evaluation of ECSA by CVs under</figcaption></figure>
344 (e) non-optimized and (f) optimized electrochemical parameters. (g) Capacitance values obtained 345 under different conditions. All measurements were performed in an argon-purged 0.1 mol L⁻¹ 346 NaOH aqueous solution. Figures adapted from [79] (CC-BY 4.0 - IOP Publishing).

347 The EIS is an alternative electroanalytical technique to estimate ECSA from $C_{dl}$. In particular, 348 EIS exhibiting capacitive current in the high frequency domain allows to evaluate the interfacial 349 capacitance. The interfacial capacitance simultaneously embraces the $C_{dl}$ contribution and the 350 capacitance associated with the oxide and/or hydroxide layer formed on some metallic surfaces 351 during ERN. This inclusion is relevant in the case of metallic foams such as Cu and Ni, as it is 352 commonly reported the formation of oxide/hydroxide layers. This type of complex metal-oxide- 353 electrolyte interface is not fully described by an ideal capacitor behavior and requires the use of 354 a constant phase element (CPE). The CPE is a mathematical element that helps to fit the 355 equivalent circuit and account for the distribution of dielectric properties of the interface [78]. 356 Typically, after EIS assessment at potentials on the capacitive current domain in a frequency range 357 from 100 kHz to 0.01 Hz, either graphical analysis is performed [80] or an equivalent circuit is 358 modeled and fitted to the EIS data after solution resistance ($R_s$) correction. The simplest and most 359 common equivalent circuit involves the $R_s$, the charge transfer resistance ($R_{ct}$), and a CPE 360 representing the double layer capacitance. The frequency-dependent impedance ($Z$) for a CPE is 361 defined by Equation (6).

$$Z(\omega) = \frac{1}{Q(i\omega)^{\alpha}} \qquad (6)$$

362 where $Z$ is the impedance in Ω cm⁻², $i$ is the imaginary part of the complex number, and $\omega$ the 363 angular frequency in rad s⁻¹. The parameters related to the CPE are $Q$ and $\alpha$ ($1 \ge \alpha \ge 0$), which are 364 determined from the equivalent circuit fitting and are independent of the frequency. If $\alpha = 1$ the 365 CPE is a capacitor and $Q$ is the $C_{dl}$ and if $\alpha = 0$ the CPE is a resistor and $Q$ is the resistance. 366 Meanwhile, the $C_{dl}$ when $\alpha \ne 1$ and $\ge 0.7$ can be estimated by Equation (7) [81,82].

$$C_{dl} = \left[ Q \left( \frac{1}{R_s} + \frac{1}{R_{ct}} \right)^{\alpha-1} \right]^{1/\alpha} \qquad (7)$$
Various studies in the literature have compared ECSA values ascertained from CV and EIS, attempting to gauge the accuracy of each technique given significant differences that oscillate between ±15 % and ±40 % [40,75,79,82,83]. In studies evaluating different electrode compositions or modified materials, both CV and/or EIS can be applicable without preference, if comparison is conducted under similar experimental conditions. Regarding equipment capabilities, the CV approach is basic and simpler compared to EIS, which requires a specific module and further specialized data treatment. However, a comprehensive evaluation of 3D foam-based electrodes is still lacking, leaving unresolved the question of the preferred method in such scenarios.

Estimating the ECSA is an important step to benchmark electrocatalytic performance of 3D electrodes. The ECSA and the *PPI* value play a relevant role to ascertain an accurate activity evaluation under identically comparable current density. In order to enable direct benchmarking of electrocatalysts for ERN, authors should provide reliable metrics normalized by the electroactive area of the material in consideration. When considering the NH₃ yield from ERN, outcomes are barely reported in intrinsically comparable intensive metrics. A few articles that encompass the use of such relevant metrics for direct benchmarking report drastically different key indicator parameters such as the catalytic activity per mass or the catalytic activity per surface area. It is important to remark that in the case of 3D foam-based electrodes, where the mass of the active material might be difficult to estimate, the intrinsic electrocatalytic activity normalized and compared using the ECSA seems to be a more suitable approach to enable transparent and direct comparison between electrocatalyst development outcomes in literature. While the ECSA can be used for comparison between electrocatalyst, the use of 3D metal foam in flow-through or flow-by reactors for ERN under mass transfer conditions should be evaluated using the *k<sub>m</sub>A* parameter to account for dimensional and structural differences between electrode configurations [63]. By combining the ECSA or the geometrical 3D area, the mass transfer coefficient can be estimated and compared between the two area determination approaches. Comparisons between electrodes
393 with different PPI values should be performed under the same hydrodynamic pattern (flow-
394 through or flow-by reactors) to clearly establish the benefits on mass transfer parameters.

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### 3. Understanding differences between scaffolds and active supports: Selected examples of novel modified foam-based electrocatalysts for ERN ($Cu_{foam}/M$ and $Ni_{foam}/M$)

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The rational design of electrocatalysts often prioritizes the use of earth-abundant materials, either in bulk or nano-enabled configurations, to minimize electrode capital costs. While platinum group materials (PGMs) are well-known for their exceptional catalytic performance, their high cost and scarcity limit their usage to ultra-low content or single-atom configurations [37,84]. Consequently, most bulk metal-based 3D electrodes discussed in the literature predominantly feature earth-abundant metals such as Cu and Ni. The mechanisms and fundamentals of ERN on those materials have been thoroughly discussed in several review articles [14,85–87]. In contrast, the benchmarking of ERN based on robust self-standing Cu- and Ni-based electrodes has been seldom explored. Thus, this section reviews some selected studies from the literature that used either active or scaffold modified materials ($Cu_{foam}/M$ and $Ni_{foam}/M$) as electrodes for the ERN.

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The modification of Cu and Ni foam electrodes has been mainly reported using two techniques: electrodeposition and solvothermal method. Electrodeposition, a well-established electrochemical technique, which utilizes a metal precursor in solution along with a supporting electrolyte and/or additives to control deposit characteristics. Electrodeposition is typically employed on flat electrode surfaces due to the homogeneity of the formed deposit. However, electrodeposition on 3D metal foam electrodes results in the formation of heterogeneous deposits across the thickness of the foam due to mass transfer limitations of the reactant within its 3D structure. In contrast, this synthetic approach facilitates *in-situ* nucleation and growth of the deposited active material, thereby providing an enhanced attachment to the foam substrate. Another commonly reported technique for modifying foam electrodes is the solvothermal approach, which involves crystal nucleation and growth under moderate to high temperature (100 – 1000 °C) and pressure (1 – 100 atm) conditions within a steel pressure vessel. In this case as well, solvent, metal precursor concentration, additives, and reaction time play all a significant
role in the synthesis. Moreover, the synthesized material is formed and deposited on the foam
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electrode, as well as on the solution. A direct comparison between these two electrode
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modification techniques has shown that electrodeposited materials exhibit higher mechanical and
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chemical stability than those deposited by solvothermal methods [88,89]. However, the total
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loading and distribution of the synthesized material by electrodeposition through the 3D structure
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of the foam significantly vary, reaching a more homogeneous distribution by the solvothermal
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deposition method. This fact may directly impact the type of reactor configuration selected for
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ERN. The flow-by configuration is well-adapted for modified foam electrodes synthesized by
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electrodeposition because only a narrow part of the total thickness of the foam electrode
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participates directly in the ERN, which is the one mainly containing the added material. In
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contrast, the flow-through configuration is well-adapted for more homogenous electrodes
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synthesized by the solvothermal deposition method because the whole thickness of the foam
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electrode participates in the ERN.
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Figure 4a illustrates a typical experimental setup for electrodeposition on foam electrodes, featuring a three-electrodes electrochemical cell comprising a reference electrode (e.g., Ag/AgCl, saturated calomel electrode (SCE)), a counter electrode (e.g., Pt plate, dimensionless stable anode), and a working electrode (e.g., Ni or Cu foam). As depicted in Figure 4a, different solution compositions can be employed to obtain various modified materials, with electrodeposition times typically within a few minutes for nanoparticles deposition. In particular, this example illustrates the modification of Cu foam by adding Cu₂O, Ni(OH)₂, SnO₂, or Co(OH)ₓ [90]. Figure 4b depicts the hydrothermal approach for modifying Cu foam, involving a pre-treatment to oxidize the Cu foam surface followed by a hydrothermal step using a solution of KMnO₄ at 180 °C for 2.5 h. Compared to electrodeposition, hydrothermal procedures of synthesis involve higher temperature and longer reaction times, typically in the range of a few hours for superficial modification. Figure 4c shows a SEM image of Cuₜfoam/Co(OH)ₓ obtained by electrodeposition, exhibiting the coexistence of Cu and Co(OH)ₓ nanodomains on the electrode surface. Figure 4d presents a transmission electron microscope image illustrating the solid-solid interface between MnO₂
447 particles obtained by hydrothermal treatment and previously generated CuO on the surface of Cu
448 foam.

449 In the subsequent sections, some selected studies on modified Cu and Ni foam electrodes are
450 addressed to demonstrate the lack of standardization reporting ERN performance at present and
451 some gaps in this research field are highlighted.
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 4.** (a) Sketch of experimental conditions considering electrolyte, applied potential, and time for electrodeposition of bimetallic electrodes using Cu foam as self-standing active</figcaption></figure>
material [90]. (b) Sketch of Cu foam modification with Cu(OH)₂ and hydrothermal procedure to obtain CuO@MnO₂/CF [91]. (c) Scanning electron microscopy image of Cu/Co(OH)ₓ prepared by electrodeposition [90]. (d) High resolution transmission electron microscopy images of CuO@MnO₂/CF [91].

### 3.1. Modified copper foam as an active material for ERN

Copper and copper-based materials are well-recognized catalysts in ERN for facilitating the conversion of NO₃⁻ to NO₂⁻. Cu foam is a self-standing electrode material classified as type i) active material, which plays both roles as an active catalyst and support material. Thus, Cu foam partially contributes to the ERN electrocatalytic response in Cuₜfoam/M modified materials acting as electrocatalysts for ERN. Cerrón-Calle et al. reported the electrochemical behavior of bare Cu foam in the presence of a NaNO₃ solution at different concentrations to identify the characteristic reduction peaks associated with the mechanistic steps in ERN [38]. As presented in Figure 5a, the oxidation (O1) and reduction (R1 & R2) peaks associated with Cu surface modifications taking place at the electrode surface do not increase in current when the NO₃⁻ concentration in solution increases. Meanwhile, the cathodic current displayed at more negative potential than -0.15 V vs RHE increases with the concentration of NO₃⁻ in the solution. Furthermore, Figure 5b demonstrates by linear sweep voltammetry the separated contributions from NO₃⁻ reduction to NO₂⁻ (at -0.4 V vs RHE) and the further reduction of NO₂⁻ to NH₃ (at -1.0 V vs RHE), according to Equations (8) and (9), respectively.

$$R_{NO_3^-}: NO_{3^-}(ads) + H_2O + 2e^- \rightarrow NO_{2^-}(ads) + 2OH^- \quad (8)$$

$$R_{NO_2^-}: NO_{2^-}(ads) + 5H_2O + 6e^- \rightarrow NH_3 + 7OH^- \quad (9)$$

The electrochemical characterization of pristine Cu foam underscores its potential to reduce NO₃⁻ to NH₃ directly in Cu foam without any additional modification. However, the main drawbacks of Cu-based materials for ERN are NO₂⁻ accumulation and low kinetic constants. To mitigate NO₂⁻ accumulation, modifying Cu foam with additional active materials to react synergistically in both reactions, Equations (8) and (9), have been extensively explored. Table 3 presents some selected examples of recent studies utilizing modified Cu foam electrodes,
including experimental conditions and key performance parameters for activity and selectivity evaluation. Additionally, Table 3 includes the ERN electrolysis results obtained by pristine Cu foam electrodes under identical experimental conditions in each case. This allows to evaluate the real impact of the modification incorporated into the electrode by subtracting the participation of the bare Cu foam electrode. In particular, only modified Cu foam materials where several consecutive cycles of electrolysis were performed to evaluate the catalyst stability are highlighted in Table 3. For example, Cu<sub>foam</sub>/Pt [38] and Cu<subfoam</sub>/Co(OH)<sub>x</sub> [17] electrocatalysts are compared under galvanostatic conditions with the bare Cu foam and provide a superior NO<sub>3</sub><sup>-</sup> conversion, ammonia selectivity (S<sub>NH3</sub>) and faradaic efficiency (FE<sub>NH3</sub>) 21-22%, as shown in Table 3. Alternatively, Changhui Zhou *et al.* described the modification of Cu foam by generating Cu(OH)<sub>2</sub> nanowires on its surface, thereby increasing the surface area available for adsorption and conversion of NO<sub>3</sub><sup>-</sup> to NO<sub>2</sub><sup>-</sup> [92]. In addition to this, to boost the NH<sub>3</sub> generation, Pd sites were incorporated as atomic hydrogen sources to achieve further reduction beyond NO<sub>2</sub><sup>-</sup>. Figure 5c illustrates the synergetic impact of these two combined modifications on the NO<sub>3</sub><sup>-</sup> conversion profile during ERN, since neither the increase in surface area produced by Cu(OH)<sub>2</sub> nanowires, nor the presence of Pd on bare Cu foam electrodes, provide the nitrate removal efficiency reached by Cu<subfoam</sub>/Cu(OH)<sub>2</sub>/Pd electrodes. It is proposed that the incorporation of Pd sites consume the NO<sub>2</sub><sup>-</sup> produced by Cu sites, freeing up sites and accelerating the overall reaction [92]. This is clearly highlighted in Table 3, where NO<sub>3</sub><sup>-</sup> conversion by potentiostatic electrolysis reaches 100% and S<sub>NH3</sub> 98.8% on Cu<subfoam</sub>/Cu(OH)<sub>2</sub>/Pd electrodes, meanwhile bare Cu foam gets blocked by the rate-limiting step reaction, Equation (8), at 60% NO<sub>3</sub><sup>-</sup> conversion and 27% S<sub>NH3</sub>. Moreover, this study proposes the complete removal of N from the solution by generating active chlorine species during electrolysis by adding NaCl in solution and using an undivided cell. This approach converts NO<sub>3</sub><sup>-</sup> into NH<sub>3</sub> and then in N<sub>2</sub> by homogeneous reaction of NH<sub>3</sub> and active chlorine species [15]. The approach of exploiting chemical conversion of NH<sub>3</sub> to N<sub>2</sub> has been also explored by other studies shown in Table 3 [93,94]. In another recent study, Wenyang Fu *et al.* evaluated the selectivity of ERN on Cu foam modified with electrodeposited CoO in a high conductivity solution (0.4 M Na<sub>2</sub>SO<sub>4</sub>) containig 40 mM of NO<sub>3</sub><sup>-</sup> [95]. The results of the corresponding
potentiostatic electrolysis are shown in Table 3 and demonstrate a net enhanced performance on Cu<sub>foam</sub>/CoO electrodes with respect to bare Cu foam. The modified electrodes display 87.6% NO<sub>3</sub><sup>-</sup> conversion, 97.3% S<sub>NH3</sub> and 96.7% FE<sub>NH3</sub>. Moreover, the same modification based on CoO electrodeposition is applied on Cu and Ni foams to compare them. Figure 5d compares the evolution of S<sub>NH3</sub> during ERN for bare Cu foam, Cu<sub>foam</sub>/CoO, as well as Ni<sub>foam</sub>/CoO electrodes, being clearly demonstrated in this case that the synergetic effect produced by combining CoO with Cu foam is much more relevant than the one displayed by CoO with Ni foam [95]. The Cu<sub>foam</sub>/CuO@MnO<sub>2</sub> [91] and Cu<sub>foam</sub>/Cu<sub>3</sub>P [96] electrocatalysts have been also evaluated for ERN by potentiostatic electrolysis exhibiting in both cases an enhanced performance in comparison with Cu foam.

<figure><img src="image_12.png" /><figcaption>**Figure 5.** (a) Cyclic voltammetry at 10 mV s⁻¹ of Cu foam at different NaNO₃ concentration: 5, 10, 20, and 50 mmol L⁻¹. (b) Linear sweep voltammetry at 10 mV s⁻¹ of Cu foam in presence of 0.1 mol L⁻¹ Na₂SO₄ support electrolyte and with the presence of 10 mmol L⁻¹ of NaNO₃ or NaNO₂ [38]. (c) NO₃⁻-N concentration evolution during ERN electrolysis on different Cu foam modified</figcaption></figure>
523 materials. Experimental conditions: 50 mg L⁻¹ NO₃⁻ at -1.2 V vs Ag/AgCl for 60 min [92]. (d)

524 NH₃ selectivity evolution during ERN electrolysis on Cu and Ni foam modified electrodes.

525 Experimental conditions: 0.4 mol L⁻¹ Na₂SO₄, 0.04 mol L⁻¹ NO₃⁻ at -1.2 V vs Ag/AgCl for 35 min

526 [95].
<table><caption>**Table 3.** Some selected examples of modified Cu foam electrodes for ERN applications.</caption><thead><tr><th>Electrode</th><th>PPI</th><th>Area (cm²)</th><th>Experimental Conditions</th><th>NO<sub>3</sub><sup>-</sup> conversion (%)</th><th>S<sub>NH3</sub> (%)</th><th>FE<sub>NH3</sub> (%)</th><th>Stability > 90%</th><th>Ref</th></tr></thead><tbody><tr><td>Cu foam</td><td>110</td><td>A<sub>G</sub> = 2.25<br/>*A<sub>ECSA</sub> = 1.2 m²</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 12.5 mM [NO<sub>3</sub><sup>-</sup>] = 2.1 mM<br/>j = 40 mA cm<sup>-2</sup><br/>Time = 2 h</td><td>55</td><td>55.3</td><td>11</td><td>-</td><td rowspan="2">[38]</td></tr><tr><td>Cu<sub>foam</sub>/Pt</td><td>110</td><td>A<sub>G</sub> = 2.25</td><td>Q = 648 C<br/>Undivided cell</td><td>94</td><td>84.0</td><td>22</td><td>5 cycles</td></tr><tr><td>Cu foam</td><td>-</td><td>A<sub>G</sub> = 4.0</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 2.1 mM<br/>[NaCl] = 70 mM<br/>E = -1.4 V vs Ag/AgCl</td><td>2.6</td><td>-</td><td>-</td><td>5 cycles</td><td rowspan="2">[93]</td></tr><tr><td>Cu<sub>foam</sub>/Cu nanobelt</td><td>-</td><td>A<sub>G</sub> = 4.0</td><td>Time = 2 h<br/>Undivided cell</td><td>≈100</td><td>-</td><td>-</td><td>5 cycles</td></tr><tr><td>Cu foam</td><td>-</td><td>A<sub>G</sub> = 4.0</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 3.6 mM<br/>E = -1.2 V vs Ag/AgCl</td><td>61.8</td><td>20</td><td>12.9</td><td>-</td><td rowspan="2">[96]</td></tr><tr><td>Cu<sub>foam</sub>/Cu<sub>3</sub>P</td><td>-</td><td>A<sub>G</sub> = 4.0</td><td>Time = 5 h<br/>Q = 900 C<br/>Undivided cell</td><td>97.7</td><td>82.7</td><td>26.2</td><td>8 cycles</td></tr><tr><td>Cu<sub>foam</sub>/CuO</td><td>-</td><td>A<sub>G</sub> = 2.0</td><td>[K<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 14.3 mM<br/>E = -1.3 V vs SCE</td><td>91.4</td><td>84.8</td><td>83.1</td><td>-</td><td rowspan="2">[91]</td></tr><tr><td>Cu<sub>foam</sub>/CuO@MnO<sub>2</sub></td><td>-</td><td>A<sub>G</sub> = 2.0<br/>*A<sub>ECSA</sub> = 465.2</td><td>Time = 2 h<br/>Divided cell</td><td>99.4</td><td>96.7</td><td>94.9</td><td>5 cycles</td></tr><tr><td>Cu foam</td><td>≈100</td><td>A<sub>G</sub> = 6.0</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 0.8 mM<br/>[NaCl] = 0.1 M<br/>E = -1.2 V vs Ag/AgCl</td><td>60</td><td>27.0</td><td>-</td><td>-</td><td rowspan="2">[92]</td></tr><tr><td>Cu<sub>foam</sub>/Cu(OH)<sub>2</sub>/Pd</td><td>≈100</td><td>A<sub>G</sub> = 6.0</td><td>Time = 0.75 h<br/>Undivided cell</td><td>≈100</td><td>98.8</td><td>-</td><td>4 cycles</td></tr><tr><td>Cu foam</td><td>-</td><td>A<sub>G</sub> = 6.0<br/>**A<sub>ECSA</sub> = 18.6</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 0.4 M [NO<sub>3</sub><sup>-</sup>] = 40 mM<br/>E = -1.2 V vs Ag/AgCl</td><td>33.9</td><td>67.1</td><td>72.4</td><td>-</td><td rowspan="2">[95]</td></tr><tr><td>Cu<sub>foam</sub>/CoO</td><td>-</td><td>A<sub>G</sub> = 6.0<br/>**A<sub>ECSA</sub> = 245</td><td>Time = 0.58 h<br/>Undivided cell</td><td>87.6</td><td>97.3</td><td>96.7</td><td>10 cycles</td></tr><tr><td>Cu foam</td><td>-</td><td>A<sub>G</sub> = 12.0</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 7.15 mM</td><td>24.3</td><td>-</td><td>-</td><td>-</td><td>[94]</td></tr></tbody></table>
<table><tr><td>Cu<sub>foam</sub>/Fe</td><td>-</td><td>A<sub>G</sub> = 12.0</td><td>j = 25 mA cm<sup>-2</sup><br/>Time = 1.5 h<br/>Q = 1620 C<br/>Undivided cell</td><td>98.6</td><td>-</td><td>-</td><td>8 cycles</td></tr><tr><td>Cu foam</td><td>110</td><td>A<sub>G</sub> =4.5<br/>*A<sub>ECSA</sub> = 1.7 m<sup>2</sup></td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 12.5 mM [NO<sub>3</sub><sup>-</sup>] = 2.1 mM</td><td>55.3</td><td>68</td><td>11</td><td>-</td></tr><tr><td>Cu<sub>foam</sub>/Co(OH)<sub>x</sub></td><td>110</td><td>A<sub>G</sub> =4.5<br/>*A<sub>ECSA</sub> = 1.2 m<sup>2</sup></td><td>j = 20 mA cm<sup>-2</sup><br/>Time = 2 h<br/>Q = 648 C<br/>Undivided cells</td><td>98.7</td><td>81</td><td>21</td><td>5 cycles</td></tr></table>

*ECSA calculated using the specific capacitance 40 µF cm⁻²

**ECSA calculated using the specific capacitance 60 µF cm⁻²

528

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### 3.2. Modified nickel foam as active material for ERN

Electrodes based on Ni foam materials represent another type of metal-based 3D electrodes (type ii), primarily serving as support material or inactive current collector. Unlike Cu foam materials, Ni materials lack significant electrocatalytic activity for ERN. Consequently, the ERN performance of bare Ni foam electrodes is seldom reported in the literature. As studied by Xiangdong Tan et al., [97], Figure 6a and Figure 6b depict the CV of bare Ni foam evaluating the electrochemical response of this material in the absence and the presence of $NO_3^-$, respectively. There is not a significant difference in current density when comparing both CV, which demonstrates the low electrocatalytic activity of Ni foam for ERN. Thus, the role for Ni foam is to serve as a scaffold material and facilitate the evaluation of other electrocatalytic materials on its surface [97]. Figure 6c illustrates the electrocatalytic behavior of various modified Ni foam electrodes ($Ni_{foam}/M$). In particular, $Ni_{foam}/Cu$ and $Ni_{foam}/Cu-Ni$ electrodes significantly improved kinetics for ERN. This is attributed to the intrinsic activity of these electrodeposited materials and not to the Ni foam itself [97]. This study also studies the complete removal of N from the solution by generating active chlorine species during electrolysis by adding NaCl in solution and using an undivided cell. This approach significantly improves the $N_2$ selectivity and has been also explored by other studies shown in Table 4. In particular, Jianan Gao et al., [86] obtained 100% $N_2$ selectivity in the presence of 2500 mg L$^{-1}$ $Cl^-$ in solution. Some selected examples of recent studies utilizing modified Ni foam electrodes are presented in Table 4, including experimental conditions and key performance parameters for activity and selectivity evaluation. In particular, modified Ni foam materials where several consecutive cycles of electrolysis were performed to evaluate the catalyst stability are highlighted in Table 4. For example, a recent study by Cerron-Calle et al. evaluated electrodeposition of Cu and Co sites separately or combined on Ni foam as a strategy to obtain modified $Ni_{foam}/M$ electrodes exhibiting a relevant effect on ERN activity and selectivity in several cases. Figure 6d showcases the electrolysis results on $Ni_{foam}/Cu_2O/Co(OH)_x$ electrode, highlighting the high $NO_3^-$ conversion and $NH_3$ production achieved [39], which are almost identical to the results displayed by $Cu_{foam}/Pt$ electrode under identical experimental conditions
for ERN [38]. This is clearly pointed out by comparing their corresponding results reported in Tables 3 and 4. In particular, the FE<sub>NH3</sub> 22% and the NO<sub>3</sub><sup>-</sup> conversion >90% are equivalent in both cases, only a slightly higher S<sub>NH3</sub> 94% on Ni<sub>foam</sub>/Cu<sub>2</sub>O/Co(OH)<sub>x</sub> electrode (Table 4) than on Cu<sub>foam</sub>/Pt electrode (S<sub>NH3</sub> 84%, Table 3) is reported. Thus, it is demonstrated that type *iii*) modified electrode materials either based on Ni or Cu foam allow both to increase the ERN activity and direct its selectivity towards NH<sub>3</sub> production in the same magnitude. Moreover, Table 4 includes other types of Ni<sub>foam</sub>/M electrocatalysts such as those based on the pyrolysis of MOF [98], as well as those based on the addition of non-metallic elements such as P (Ni<sub>foam</sub>/NiCoP [99] and Ni<sub>foam</sub>/CoP nanowire array [100], S (Ni<sub>foam</sub>/NiCo<sub>2</sub>S<sub>4</sub> [101]) or B (Ni<sub>foam</sub>/Ni-BO<sub>x</sub> [102]). The presence of a non-metallic element in addition to metals in the ERN electrocatalyst is demonstrated to produce a significant enhancement in NO<sub>3</sub><sup>-</sup> conversion, S<sub>NH3</sub> and FE<sub>NH3</sub> as is shown in Table 4.

<figure><img src="image_13.png" /><figcaption>**Figure 6.** Cyclic voltammetry of Ni foam at different scan rate in (a) absence and (b) presence of 100 mg L<sup>-1</sup> NO<sub>3</sub><sup>-</sup>-N in solution. (c) Pseudo-first order kinetic constants for ERN on different Ni</figcaption></figure>
573 foam-based electrodes [97]. (d) NO₃⁻-N conversion and products evolution during ERN

574 electrolysis using Ni<sub>foam</sub>/Cu<sub>2</sub>O/Co(OH)<sub>x</sub>. Experimental conditions: 30 mg L<sup>-1</sup> NO₃⁻-N and 12.5

575 mM Na<sub>2</sub>SO<sub>4</sub> solution at 40 mA cm<sup>-2</sup> for 120 min [39].

576
<table><caption>Table 4. Some selected examples of modified Ni foam electrodes for ERN applications.</caption><thead><tr><th>Electrode</th><th>PPI</th><th>Area (cm²)</th><th>Experimental Conditions</th><th>NO<sub>3</sub><sup>-</sup> conversion (%)</th><th>S<sub>NH3</sub> (%)</th><th>FE<sub>NH3</sub> (%)</th><th>Stability > 90%</th><th>Ref</th></tr></thead><tbody><tr><td>Ni<sub>foam</sub>/P-doped-Co<sub>3</sub>O<sub>4</sub></td><td>-</td><td>A<sub>G</sub>=8<br>**A<sub>ECSA</sub> = 1008</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 3.6 mM<br>E = -1.3 V vs SCE<br>Time = 2 h<br>Undivided cell</td><td>98</td><td>78</td><td>31.4</td><td>10 cycles</td><td>[86]</td></tr><tr><td>Ni foam</td><td>110</td><td>A<sub>G</sub> = 2.25<br>*A<sub>ECSA</sub> = 1913</td><td rowspan="4">[Na<sub>2</sub>SO<sub>4</sub>] = 12.5 mM [NO<sub>3</sub><sup>-</sup>] = 2.1 mM<br>j = 40 mA cm<sup>-2</sup><br>Time = 2 h<br>Q = 648 C<br>Undivided cell</td><td>9.6</td><td>95.8</td><td>2</td><td>-</td><td rowspan="4">[39]</td></tr><tr><td>Ni<sub>foam</sub>/Cu<sub>2</sub>O</td><td>110</td><td>A<sub>G</sub> = 2.0<br>*A<sub>ECSA</sub> = 4838</td><td>28.4</td><td>49.5</td><td>4</td><td>-</td></tr><tr><td>Ni<sub>foam</sub>/Co(OH)<sub>x</sub></td><td>110</td><td>A<sub>G</sub> = 2.0<br>*A<sub>ECSA</sub> = 2813</td><td>86.7</td><td>87.5</td><td>20</td><td>6 cycles</td></tr><tr><td>Ni<sub>foam</sub>/Cu<sub>2</sub>O/Co(OH)<sub>x</sub></td><td>110</td><td>A<sub>G</sub> = 2.0<br>*A<sub>ECSA</sub> = 3518</td><td>90.3</td><td>94</td><td>22</td><td>6 cycles</td></tr><tr><td>Ni<sub>foam</sub>/MnO<sub>2</sub>-Oxygen vacancy</td><td>110</td><td>A<sub>G</sub>=8</td><td rowspan="3">[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 1.6 mM<br>E = -0.85 V vs Ag/AgCl<br>Time = 6 h<br>Divided continuous-flow cell</td><td>15.9</td><td>34.5</td><td>62.2</td><td>-</td><td rowspan="3">[103]</td></tr><tr><td>Ni<sub>foam</sub>/Pd</td><td>110</td><td>A<sub>G</sub>=8</td><td>53.3</td><td>85</td><td>69.7</td><td>-</td></tr><tr><td>Ni<sub>foam</sub>/MnO<sub>2</sub>-Oxygen vacancy-Pd</td><td>110</td><td>A<sub>G</sub>=8</td><td>90.6</td><td>87.6</td><td>69.6</td><td>-</td></tr><tr><td>Ni<sub>foam</sub>/Fe@Fe<sub>2</sub>O<sub>3</sub></td><td>-</td><td>*A<sub>ECSA</sub> = 6.5</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 0.1 M [NO<sub>3</sub><sup>-</sup>] = 3.6 mM<br>E = -1.35 V vs Ag/AgCl</td><td>86.1</td><td>93</td><td>-</td><td>-</td><td>[98]</td></tr></tbody></table>
<table><tbody><tr><td>Ni<sub>foam</sub>/Co-Fe@Fe<sub>2</sub>O<sub>3</sub></td><td>-</td><td>*A<sub>ECSA</sub>= 37.75</td><td>Time = 10 h<br>Undivided cell</td><td>96.7</td><td>99</td><td>85.2</td><td>8 cycles</td><td></td></tr><tr><td>Ni<sub>foam</sub>/NiCo</td><td>-</td><td>A<sub>G</sub> = 5</td><td>[Na<sub>2</sub>SO<sub>4</sub>] = 50 mM [NO<sub>3</sub><sup>-</sup>] = 3.6 mM<br>j = 2 mA cm<sup>-2</sup><br>Time = 5 h</td><td>82</td><td>73.4</td><td>43.5</td><td>-</td><td rowspan="2">[99]</td></tr><tr><td>Ni<sub>foam</sub>/NiCoP</td><td>-</td><td>A<sub>G</sub> = 5</td><td>Q = 180 C<br>Divided cell</td><td>97.7</td><td>95.4</td><td>57.7</td><td>6 cycles</td></tr><tr><td>Ni<sub>foam</sub>/Co(OH)<sub>2</sub><br>nanowire array</td><td>-</td><td>A<sub>G</sub> = 1.5<br>*A<sub>ECSA</sub> = 17</td><td rowspan="3">[Na<sub>2</sub>SO<sub>4</sub>] = 0.2 M [NO<sub>3</sub><sup>-</sup>] = 7.14 mM<br>E = -0.7 V vs RHE<br>Time = 3 h<br>Undivided cell</td><td>7.8</td><td>86.9</td><td>-</td><td>-</td><td rowspan="3">[100]</td></tr><tr><td>Ni<sub>foam</sub>/Co<sub>3</sub>O<sub>4</sub><br>nanowire array</td><td>-</td><td>A<sub>G</sub> = 1.5<br>*A<sub>ECSA</sub> = 5</td><td>13.9</td><td>88.1</td><td>-</td><td>-</td></tr><tr><td>Ni<sub>foam</sub>/CoP nanowire<br>array</td><td>-</td><td>A<sub>G</sub> = 1.5<br>*A<sub>ECSA</sub> = 857</td><td>97.9</td><td>99.4</td><td>97</td><td>10 cycles</td></tr><tr><td>Ni<sub>foam</sub>/NiCo<sub>2</sub>O<sub>4</sub></td><td>-</td><td>A<sub>G</sub> = 2<br>A<sub>ECSA</sub> = 592.5</td><td rowspan="2">[KOH] = 1.0 M [NO<sub>3</sub><sup>-</sup>] = 3.6 mM<br>E = -1.4 V vs Ag/AgCl<br>Time = 2 h<br>Divided cell</td><td>60.5</td><td>76.9</td><td>-</td><td>-</td><td rowspan="2">[101]</td></tr><tr><td>Ni<sub>foam</sub>/NiCo<sub>2</sub>S<sub>4</sub></td><td>-</td><td>A<sub>G</sub> = 2<br>A<sub>ECSA</sub> = 1006.25</td><td>89.6</td><td>92.1</td><td>45.4</td><td>8 cycles</td></tr><tr><td>Ni foam</td><td>-</td><td>A<sub>G</sub> = 0.09<br>*A<sub>ECSA</sub> = 1.0</td><td rowspan="2">[Na<sub>2</sub>SO<sub>4</sub>] = 0.1 M [NO<sub>3</sub><sup>-</sup>] = 500 mM<br>E = -0.9 V vs RHE<br>Time = 0.5 h<br>Undivided cell</td><td>-</td><td>-</td><td>49</td><td>-</td><td rowspan="2">[102]</td></tr><tr><td>Ni<sub>foam</sub>/Ni-BO<sub>x</sub></td><td>-</td><td>A<sub>G</sub> = 0.09<br>*A<sub>ECSA</sub> = 1.7</td><td>-</td><td>-</td><td>94</td><td>30 cycles</td></tr></tbody></table>

*ECSA calculated using the specific capacitance 40 µF cm⁻²

**ECSA calculated using the specific capacitance 60 µF cm⁻²

578

579

580
The literature on metal-3D foam electrodes for ERN is extensive, but comparing different electrode materials remains challenging due to the lack of normalization of experimental conditions, reported parameters, and operation methods. Nevertheless, comparing the selected results shown in Tables 3 and 4 allows us to identify five keypoints to be improved in the future.

(1) The *PPI* metric is only reported in 30 % of the studies considered. This suggests that either only one value is commonly reported for this parameter or its importance is often overlooked. However, it is crucial to note that various *PPI* values ranging from 5 to 130 are available in the commercially available foams. As previously discussed, *PPI* plays a pivotal role in mass transfer at the electrode surface, making it crucial for controlling the interfacial pH. Therefore, by considering *PPI* as a fixed parameter, the benchmarking process is limited to a very few number of studies.

(2) The electrode area, whether geometrical or ECSA, is essential for a comprehensive comparison among the array of electrocatalysts prepared. However, according to the studies considered, geometric 2D area (W x L) is commonly used for reporting performance, overlooking the true effects of 3D structures. Consequently, parameters such as areal density (cm² g⁻¹) or specific surface area (SSA, cm² cm⁻³) are often neglected. Similarly, ECSA, crucial for intrastudy benchmarking of electrocatalysts and providing insight into modified material and their intrinsic activity (as suggested in Figure 1), is not consistently reported. A more meticulous electrochemical interstudy benchmarking of electrocatalysts requires the estimation of ECSA for a better understanding of the electrochemical system, which is urgently needed. Therefore, reporting both geometric area and ECSA should be promoted as a good practice to enhance the benchmarking of electrocatalysts both within and across studies.

(3) Variations in experimental conditions such as initial NO₃⁻ concentration and electrolyte composition make it difficult to compare different studies, despite most studies providing NO₃⁻ conversion and NH₃ selectivity evaluation. For instance, Cu foam (Table 3, [95]) exhibits a NO₃⁻ conversion of 33.9 % and a NH₃ selectivity of 67.1 %, while Niₜoam/Pd (Table 4, [103]) shows 53.3 % and 85%, respectively. Based solely on conversion values, Niₜoam/Pd appears
608 to exhibit higher activity, potentially leading to an increased NH₃ production. However, such
609 comparisons are inaccurate without considering the initial NO₃⁻ concentration. Using the reported
610 initial conditions (40 mM NO₃⁻ for Cu foam and 1.6 mM NO₃⁻ for Ni₈₀/PG), Cu foam generates
611 approximately 127 mg NH₃-N L⁻¹ and Ni₈₀/PG 10.14 mg NH₃-N L⁻¹. This highlights the
612 challenge of benchmarking electrodes using these parameters, which involves considering the
613 initial NO₃⁻ concentration and subsequent calculations to enable direct comparison. Furthermore,
614 the absence of standardized conditions results in a chaotic range of conditions, with initial NO₃⁻
615 concentrations varying from 1 to 40 mM for Cu-based electrodes (Table 3) and from 2 to 500 mM
616 for Ni-based electrodes (Table 4).

617 (4) Similarly to the wide range of initial NO3- concentrations, electrochemical conditions
618 are not standardized across studies. Under potentiostatic conditions, reported potentials in
619 electrolysis correspond to those determined during the evaluation of intrinsic activity. However,
620 key parameters such as supporting electrolytes, initial pH, and electrolysis time are often different.
621 A subset of studies performs electrolysis under galvanostatic conditions, employing current
622 densities ranging from 2 to 40 mA cm-2, but without considering a benchmark and constant
623 number of coulombs circulated during the electrolysis to facilitate subsequent results comparison.
624 Notably, comparing results from Tables 3 and 4 reveals some trends; for example, disparities in
625 FENH3 arise when comparing constant potential electrolysis (mostly FE>NH3 ≥70%) with constant
626 current electrolysis (mostly FE>NH3 <30%). The main reason for these disparities lies in the
627 competitive hydrogen evolution reaction (HER), which occurs at a similar potential to ERN. HER
628 can be modulated by controlling the applied potential, whereas this control is absent in constant
629 current electrolysis. Consequently, the quantification of H₂ gas produced during ERN electrolysis
630 represents a significant gap in most ERN studies, which is typically overlooked. Comparison
631 across operation methods is incorrect as it will not illustrate the competitiveness of
632 electrocatalysts under identical conditions. Furthermore, the reader should note that real upscale
633 applications generally operate under galvanostatic conditions in large electrochemically
engineered systems beyond the lab bench (which would correspond to the studies that report the lower FE<sub>NH3</sub> values in literature).

(5) The most commonly used benchmarking parameter for ERN in the literature, NH<sub>3</sub> yield (mmol NH<sub>3</sub> g<sup>-1</sup><sub>cat</sub> h<sup>-1</sup> or mmol NH<sub>3</sub> cm<sup>-2</sup><sub>cat</sub> h<sup>-1</sup>), directly incorporates NH<sub>3</sub> production normalized by catalyst mass or area, and time. However, the disparities between geometrical and ECSA areas introduce additional errors in reporting NH<sub>3</sub> yield. This parameter introduces another source of confusion since at high initial NO<sub>3</sub><sup>-</sup> concentrations, NH<sub>3</sub> yield produced on a poor electrocatalyst may appear higher than that obtained using an optimal electrode, but treating a lower initial NO<sub>3</sub><sup>-</sup> concentration. Therefore, there is an urgent need to incorporate additional parameters, which consider the initial NO<sub>3</sub><sup>-</sup> concentration to avoid misunderstandings and facilitate benchmarking, as well as evaluate the overpotential and, thus, the energy required to produce NH<sub>3</sub>. We have recently proposed NH<sub>3</sub> generation efficiency (Equation (10)) [90], which combines both concepts, selectivity and conversion.

$$NH_3 \text{ generation efficiency (\%)} = \frac{C_{NH_3,exp}}{C_{NH_3,theo}} \times 100 \qquad (10)$$

where C<sub>NH<sub>3</sub>,exp</sub> is the NH<sub>3</sub> concentration experimentally obtained from electrolysis in mmol L<sup>-1</sup> and C<sub>NH<sub>3</sub>,theo</sub> is the theoretical NH<sub>3</sub> concentration in mmol L<sup>-1</sup> if the entire initial amount of NO<sub>3</sub><sup>-</sup> is converted to NH<sub>3</sub> by ERN.

Concerning the overpotential evaluation in NO<sub>3</sub><sup>-</sup> conversion, we propose to adapt the parameter of energy efficiency (Equation (11)), which is commonly used to evaluate electrochemical CO<sub>2</sub> reduction performance [104,105].

$$NH_3 \text{ energy efficiency (\%)} = (E_T/E) \times FE_{NH_3} \qquad (11)$$

where E<sub>T</sub> is the thermodynamic potential in volts required for the electrocatalytic reduction of NO<sub>3</sub><sup>-</sup> to NH<sub>3</sub> in aqueous solution, whereas E and FE<sub>NH3</sub> represent the experimental cathode potential applied in volts and the NH<sub>3</sub> Faradaic Efficiency (%), respectively.
659 **4. Conclusions and outlook**

660 The ERN systems have emerged as a promising approach for resource recovery, particularly
661 in NH₃ production. Rational electrode design has guided researchers through various
662 configurations, with metal-based 3D electrodes showing considerable potential in material design.
663 Specifically, self-standing Cu and Ni foam electrodes have been extensively studied for their roles
664 as electrocatalytic active and support materials, respectively. This review addresses two critical
665 parameters characterizing foam electrodes often overlooked in ERN literature: pore per inch (PPI)
666 and electrochemically active surface area (ECSA), as well as proposes two additional engineering
667 figures of merit for benchmarking ERN by encompassing selectivity and conversion and
668 evaluating overpotential contribution.

669 The importance of PPI is revalued, emphasizing its significant role in reactants and products
670 mass transfer, and suggesting its crucial relevance in evaluating water matrices to prevent
671 inorganic and organic scaling. However, the lack of studies using full reactors (flow-through or
672 flow-by) conceals the benefits of 3D metal foam for ERN. Additionally, the indirect measurement
673 of ECSA is promoted, with main steps provided for reliable calculations. Techniques such as
674 cyclic voltammetry or electrochemical impedance spectroscopy can be utilized to estimate ECSA,
675 with consistency across studies recommended for proper evaluation of modified Cu and Ni foam
676 electrodes (Cu<sub>foam</sub>/M and Ni<sub>foam</sub>/M).

677 At present, benchmarking electrocatalysts for ERN presents more challenges than certainties.
678 Despite ERN's potential as a resource recovery approach, the lack of specific trends and variety
679 among studies creates a complex scenario where comparability is limited. Significant advances
680 have been made in electrocatalyst design, but some reference values have not been identified yet.
681 The current lack of standardization in reporting PPI, ECSA, experimental conditions, and key
682 performance metrics hinders meaningful comparisons across ERN studies. This variability limits
683 a broader understanding of the ERN and obscures the identification of optimal electrode materials
684 and operating parameters. Reviewing Cu<sub>foam</sub>/M and Ni<sub>foam</sub>/M studies reveals nuances based on
685 various aspects, prompting reconsideration of the composition of the solution to be treated and
the need to set reference concentrations for both reactant and supporting electrolyte, as well as using a constant amount of circulated charge to compare different electrolysis properly. Morphological and structural analysis of the electrode before and after ERN electrolysis is notably absent in the literature. Similarly, accelerated life tests for these 3D foam electrodes are rarely considered.

From this perspective, several key research directions are essential and require future development. Reactor configurations designed to optimize 3D foam electrodes and strategies to suppress the competitive HER could significantly improve ERN efficiency. Exploring electrocatalytic materials that selectively inhibit HER without impacting ERN could be a promising research direction, particularly relevant in galvanostatic electrolysis. Additionally, investigations in electrode stability under realistic water conditions by accelerated tests, considering potential issues like inorganic scaling, are crucial for practical applications.

The benchmarking of 3D electrodes for ERN represents a significant challenge, as highlighted by the nuances and gaps identified in this review. It is evident that the current state of research lacks characterization protocols and standardized reporting practices, which hinders effective comparison of electrode performance across studies. By addressing these challenges, we should be able to accelerate the development of highly efficient, selective, and scalable ERN systems, propelling this technology, still under development, from a low technology readiness level (TRL) towards a widespread water treatment technology. While significant advances have been made in understanding the fundamental principles of ERN and exploring novel electrodes configurations, there remains a need for practical implementation and field testing under real-world conditions.

## Acknowledgements

This work has been supported by ECOS Nord-Peru CONCYTEC program (Project P20P01 and PROCIENCIA project No. 005-2020) and PhD scholarship from PROCIENCIA (GRANT No. 237-2015-FONDECYT). This material is based upon research supported by the Transatlantic Research Partnership of the Embassy of France in the United States and the FACE
712 Foundation. This project has received funding from the Herman Frasch Fund for Chemical
713 Research, Bank of America, N.A., Trustee with grant number G10224-300. Y. Adjez would like
714 to acknowledge Sorbonne Université (France) for granting him a PhD contract. The authors
715 acknowledge the support of the Centre National de la Recherche Scientifique (CNRS), since this
716 work has been partially funded by the CNRS Energy unit (Cellule Energie) through the project
717 PEPS.

718
719 **5. References**

720 [1] A.J. dos Santos, H.L. Barazorda-Ccahuana, G. Caballero-Manrique, Y. Chérémond, P.J.

721 Espinoza-Montero, J.R. González-Rodríguez, U.J. Jáuregui-Haza, M.R.V. Lanza, A.

722 Nájera, C. Oporto, A. Pérez Parada, T. Pérez, V.D. Quezada, V. Rojas, V. Sosa, A.

723 Thiam, R.A. Torres-Palma, R. Vargas, S. Garcia-Segura, Accelerating innovative water

724 treatment in Latin America, Nat. Sustain. (2023). https://doi.org/10.1038/s41893-022-

725 01042-z.

726 [2] I. Jeon, E.C. Ryberg, P.J.J. Alvarez, J.H. Kim, Technology assessment of solar

727 disinfection for drinking water treatment, Nat. Sustain. 5 (2022).

728 https://doi.org/10.1038/s41893-022-00915-7.

729 [3] N. Singh, B.R. Goldsmith, Role of Electrocatalysis in the Remediation of Water

730 Pollutants, ACS Catal. 10 (2020) 3365–3371. https://doi.org/10.1021/acscatal.9b04167.

731 [4] I. Katsounaros, On the assessment of electrocatalysts for nitrate reduction, Curr. Opin.

732 Electrochem. 28 (2021) 100721. https://doi.org/10.1016/j.coelec.2021.100721.

733 [5] D. Cecconet, F. Sabba, M. Devecseri, A. Callegari, A.G. Capodaglio, In situ

734 groundwater remediation with bioelectrochemical systems: A critical review and future

735 perspectives, Environ. Int. 137 (2020) 105550.

736 https://doi.org/10.1016/j.envint.2020.105550.

737 [6] M.J. Pennino, J.E. Compton, S.G. Leibowitz, Trends in Drinking Water Nitrate

738 Violations Across the United States, Environ. Sci. Technol. 51 (2017) 13450–13460.

739 https://doi.org/10.1021/acs.est.7b04269.

740 [7] M. Allaire, H. Wu, U. Lall, National trends in drinking water quality violations, Proc.

741 Natl. Acad. Sci. U. S. A. 115 (2018) 2078–2083.

742 https://doi.org/10.1073/pnas.1719805115.

743 [8] M. Allaire, H. Wu, U. Lall, National trends in drinking water quality violations, Proc.
744 Natl. Acad. Sci. U. S. A. 115 (2018) 2078–2083.

745 https://doi.org/10.1073/pnas.1719805115.

746 [9] R. Epsztein, O. Nir, O. Lahav, M. Green, Selective nitrate removal from groundwater
747 using a hybrid nanofiltration-reverse osmosis filtration scheme, Chem. Eng. J. 279
748 (2015) 372–378. https://doi.org/10.1016/j.cej.2015.05.010.

749 [10] A.M. Bergquist, J.K. Choe, T.J. Strathmann, C.J. Werth, Evaluation of a hybrid ion
750 exchange-catalyst treatment technology for nitrate removal from drinking water, Water
751 Res. 96 (2016) 177–187. https://doi.org/10.1016/j.watres.2016.03.054.

752 [11] S. Singh, A.G. Anil, V. Kumar, D. Kapoor, S. Subramanian, J. Singh, P.C. Ramamurthy,
753 Nitrates in the environment: A critical review of their distribution, sensing techniques,
754 ecological effects and remediation, Chemosphere. 287 (2022) 131996.
755 https://doi.org/10.1016/j.chemosphere.2021.131996.

756 [12] B.K. Pramanik, L. Shu, V. Jegatheesan, A review of the management and treatment of
757 brine solutions, Environ. Sci. Water Res. Technol. 3 (2017) 625–658.
758 https://doi.org/10.1039/c6ew00339g.

759 [13] J. Sanchis-Carbonell, I. Carrero-Ferrer, A. Sáez-Fernández, M. Pedro-Monzón, P.
760 Campíns-Falcó, V. Montiel, Towards a zero liquid discharge process from brine
treatment: Water recovery, nitrate electrochemical elimination and potential valorization
761 of hydrogen and salts, Sci. Total Environ. 926 (2024).
762 https://doi.org/10.1016/j.scitotenv.2024.172060.

764 [14] S. Garcia-Segura, M. Lanzarini-Lopes, K. Hristovski, P. Westerhoff, Electrocatalytic
765 reduction of nitrate: Fundamentals to full-scale water treatment applications, Appl. Catal.
766 B Environ. 236 (2018) 546–568. https://doi.org/10.1016/j.apcatb.2018.05.041.

767 [15] K. Flores, G.A. Cerrón-Calle, C. Valdes, A. Atrashkevich, A. Castillo, H. Morales, J.G.
768 Parsons, S. Garcia-Segura, J.L. Gardea-Torresdey, Outlining Key Perspectives for the
769 Advancement of Electrocatalytic Remediation of Nitrate from Polluted Waters, ACS
770 ES&T Eng. 2 (2022) 746–768. https://doi.org/10.1021/acsestengg.2c00052.

771 [16] M. Marcos-Hernández, G. Antonio Cerrón-Calle, Y. Ge, S. Garcia-Segura, C.M. Sánchez-Sánchez, A.S. Fajardo, D. Villagrán, Effect of surface functionalization of Fe3O4 nano-enabled electrodes on the electrochemical reduction of nitrate, Sep. Purif. Technol. 282 (2022) 119771. https://doi.org/10.1016/j.seppur.2021.119771.

775 [17] G.A. Cerrón-Calle, A.S. Fajardo, J. Liu, C.M. Sánchez-Sánchez, S. Garcia-Segura, Enabling circular economy by N-recovery: Electrocatalytic reduction of nitrate with cobalt hydroxide nanocomposites on copper foam treating low conductivity groundwater effluents, Sci. Total Environ. 887 (2023) 163938. https://doi.org/10.1016/j.scitotenv.2023.163938.

780 [18] D. Anastasiadou, Y. van Beek, W. Chen, T. Wissink, A. Parastaev, E.J.M. Hensen, M. Costa Figueiredo, Morphology Changes of Cu2O Catalysts During Nitrate Electroreduction to Ammonia**, ChemCatChem. 15 (2023) 1–9. https://doi.org/10.1002/cctc.202201503.

784 [19] A.S. Fajardo, P. Westerhoff, S. Garcia-Segura, C.M. Sánchez-Sánchez, Selectivity modulation during electrochemical reduction of nitrate by electrolyte engineering, Sep. Purif. Technol. 321 (2023) 124233. https://doi.org/10.1016/j.seppur.2023.124233.

787 [20] J.M. McEnaney, S.J. Blair, A.C. Nielander, J.A. Schwalbe, D.M. Koshy, M. Cargnello, T.F. Jaramillo, Electrolyte engineering for efficient electrochemical nitrate reduction to ammonia on a titanium electrode, ACS Sustain. Chem. Eng. 8 (2020) 2672–2681. https://doi.org/10.1021/acssuschemeng.9b05983.

791 [21] I. Sanjuán, L. García-Cruz, J. Solla-Gullón, E. Expósito, V. Montiel, Bi–Sn nanoparticles for electrochemical denitrification: activity and selectivity towards N2 formation, Electrochim. Acta. 340 (2020). https://doi.org/10.1016/j.electacta.2020.135914.
795 [22] D.R. MacFarlane, P. V. Cherepanov, J. Choi, B.H.R. Suryanto, R.Y. Hodgetts, J.M.

796 Bakker, F.M. Ferrero Vallana, A.N. Simonov, A Roadmap to the Ammonia Economy,

797 Joule. 4 (2020) 1186–1205. https://doi.org/10.1016/j.joule.2020.04.004.

798 [23] P.H. van Langevelde, I. Katsounaros, M.T.M. Koper, Electrocatalytic Nitrate Reduction

799 for Sustainable Ammonia Production, Joule. 5 (2021) 290–294.

800 https://doi.org/10.1016/j.joule.2020.12.025.

801 [24] H. Yin, X. Xing, W. Zhang, J. Li, W. Xiong, H. Li, A simple hydrothermal synthesis of

802 an oxygen vacancy-rich MnMoO4 rod-like material and its highly efficient

803 electrocatalytic nitrogen reduction, Dalt. Trans. 52 (2023) 16670–16679.

804 https://doi.org/10.1039/d3dt03018k.

805 [25] X. Huang, X. Xing, W. Xiong, H. Li, Flower-Like Ni-Mn Bimetallic Oxide-Based

806 Nanosheets for Enhanced Electrocatalytic Nitrogen Reduction to Ammonia, Energy and

807 Fuels. 37 (2023) 19147–19155. https://doi.org/10.1021/acs.energyfuels.3c03517.

808 [26] G.A. Cerrón-Calle, T.P. Senftle, S. Garcia-Segura, Strategic tailored design of

809 electrocatalysts for environmental remediation based on density functional theory (DFT)

810 and microkinetic modeling, Curr. Opin. Electrochem. 35 (2022) 101062.

811 https://doi.org/10.1016/j.coelec.2022.101062.

812 [27] Y. Wang, W. Zhou, R. Jia, Y. Yu, B. Zhang, Unveiling the Activity Origin of a Copper-

813 based Electrocatalyst for Selective Nitrate Reduction to Ammonia, Angew. Chemie - Int.

814 Ed. 59 (2020) 5350–5354. https://doi.org/10.1002/anie.201915992.

815 [28] D. Hao, Z. gang Chen, M. Figiela, I. Stepniak, W. Wei, B.J. Ni, Emerging alternative for

816 artificial ammonia synthesis through catalytic nitrate reduction, J. Mater. Sci. Technol.

817 77 (2021) 163–168. https://doi.org/10.1016/j.jmst.2020.10.056.

818 [29] L. Yang, J. Li, F. Du, J. Gao, H. Liu, S. Huang, H. Zhang, C. Li, C. Guo, Interface

819 engineering cerium-doped copper nanocrystal for efficient electrochemical nitrate-to-
820 ammonia production, Electrochim. Acta. 411 (2022) 140095.

821 https://doi.org/10.1016/j.electacta.2022.140095.

822 [30] Z. Niu, S. Fan, X. Li, J. Duan, A. Chen, Interfacial engineering of CoMn2O4/NC
823 induced electronic delocalization boosts electrocatalytic nitrogen oxyanions reduction to
824 ammonia, Appl. Catal. B Environ. 322 (2023) 122090.
825 https://doi.org/10.1016/j.apcatb.2022.122090.

826 [31] A. Atrashkevich, A.S. Fajardo, P. Westerhoff, W.S. Walker, C.M. Sánchez-Sánchez, S.
827 Garcia-Segura, Overcoming barriers for nitrate electrochemical reduction: By-passing
828 water hardness, Water Res. 225 (2022) 119118.
829 https://doi.org/10.1016/j.watres.2022.119118.

830 [32] J. Ma, W. Wei, G. Qin, T. Xiao, W. Tang, S. Zhao, L. Jiang, S. Liu, Electrochemical
831 reduction of nitrate in a catalytic carbon membrane nano-reactor, Water Res. 208 (2022)
832 117862. https://doi.org/10.1016/j.watres.2021.117862.

833 [33] J. Li, G. Zhan, J. Yang, F. Quan, C. Mao, Y. Liu, B. Wang, F. Lei, L. Li, A.W.M. Chan,
834 L. Xu, Y. Shi, Y. Du, W. Hao, P.K. Wong, J. Wang, S.X. Dou, L. Zhang, J.C. Yu,
835 Efficient Ammonia Electrosynthesis from Nitrate on Strained Ruthenium Nanoclusters,
836 J. Am. Chem. Soc. 142 (2020) 7036-7046. https://doi.org/10.1021/jacs.0c00418.

837 [34] A. Iarchuk, A. Dutta, P. Broekmann, Novel Ni foam catalysts for sustainable nitrate to
838 ammonia electroreduction, J. Hazard. Mater. 439 (2022) 129504.
839 https://doi.org/10.1016/j.jhazmat.2022.129504.

840 [35] A. Kulshreshtha, S.K. Dhakad, Preparation of metal foam by different methods: A
841 review, Mater. Today Proc. 26 (2019) 1784-1790.
842 https://doi.org/10.1016/j.matpr.2020.02.375.

843 [36] M. Grdeń, M. Alsabet, G. Jerkiewicz, Surface science and electrochemical analysis of
844 nickel foams, ACS Appl. Mater. Interfaces. 4 (2012) 3012-3021.
845 https://doi.org/10.1021/am300380m.

846 [37] A.S. Fajardo, P. Westerhoff, C.M. Sanchez-Sanchez, S. Garcia-Segura, Earth-abundant elements a sustainable solution for electrocatalytic reduction of nitrate, Appl. Catal. B Environ. 281 (2021) 119465. https://doi.org/10.1016/j.apcatb.2020.119465.

849 [38] G.A. Cerrón-Calle, A.S. Fajardo, C.M. Sánchez-Sánchez, S. Garcia-Segura, Highly reactive Cu-Pt bimetallic 3D-electrocatalyst for selective nitrate reduction to ammonia, Appl. Catal. B Environ. 302 (2022) 120844.
852 https://doi.org/10.1016/j.apcatb.2021.120844.

853 [39] G.A. Cerrón-Calle, A. Wines, S. Garcia-Segura, Atomic hydrogen provision by cobalt sites in a bimetallic Ni/Co(OH)x and trimetallic Ni/Cu2O/Co(OH)x configurations for superior ammonia production, Appl. Catal. B Environ. 328 (2023) 122540.
856 https://doi.org/10.1016/j.apcatb.2023.122540.

857 [40] C.C.L. McCrory, S. Jung, J.C. Peters, T.F. Jaramillo, Benchmarking heterogeneous electrocatalysts for the oxygen evolution reaction, J. Am. Chem. Soc. 135 (2013) 16977–16987. https://doi.org/10.1021/ja407115p.

860 [41] Y. Guan, W. Suo, Z. Zhang, Y. Wang, S. Sun, G. Liu, Insights on the Catalytic Active Site for CO2 Reduction on Copper-based Catalyst: A DFT study, Mol. Catal. 511 (2021) 111725. https://doi.org/10.1016/j.mcat.2021.111725.

863 [42] N. Atrak, E. Tayyebi, E. Skúlason, Effect of co-adsorbed water on electrochemical CO2 reduction reaction on transition metal oxide catalysts, Appl. Surf. Sci. 570 (2021) 151031. https://doi.org/10.1016/j.apsusc.2021.151031.

866 [43] D. Jang, J. Maeng, J. Kim, H. Han, G.H. Park, J. Ha, D. Shin, Y.J. Hwang, W.B. Kim, Boosting electrocatalytic nitrate reduction reaction for ammonia synthesis by plasma-induced oxygen vacancies over MnCuOx, Appl. Surf. Sci. 610 (2023) 155521.
869 https://doi.org/10.1016/j.apsusc.2022.155521.
870 [44] W. Zheng, Y. Chen, X. Peng, K. Zhong, Y. Lin, Z. Huang, The phase evolution and
871 physical properties of binary copper oxide thin films prepared by reactive magnetron
872 sputtering, Materials (Basel). 10 (2018) 1–13. https://doi.org/10.3390/ma11071253.

873 [45] M.I. Pintor-Monroy, D. Barrera, B.L. Murillo-Borjas, F.J. Ochoa-Estrella, J.W.P. Hsu, M.A. Quevedo-Lopez, Tunable Electrical and Optical Properties of Nickel Oxide (NiO x ) Thin Films for Fully Transparent NiO x -Ga 2 O 3 p-n Junction Diodes, ACS Appl. Mater. Interfaces. 10 (2018) 38159–38165. https://doi.org/10.1021/acsami.8b08095.

877 [46] N.D. Koshel', V. V. Malyshev, Measurement of the resistivity of the electrode
878 NiOOH/Ni(OH)2 solid phase active substance during the discharge process, Surf. Eng.
879 Appl. Electrochem. 46 (2010) 348–351. https://doi.org/10.3103/S1068375510040095.

880 [47] H. Bartzsch, D. Glöß, B. Böcher, P. Frach, K. Goedicke, Properties of SiO2 and Al2O3 films for electrical insulation applications deposited by reactive pulse magnetron sputtering, Surf. Coatings Technol. 174–175 (2003) 774–778.
881
882
883 https://doi.org/10.1016/S0257-8972(03)00384-0.

884 [48] J. Liu, J. Dang, M. Wang, X. Wang, X. Duan, S. Yuan, T. Liu, Q. Wang, Metal–Organic-Framework-Derived Cobalt nanoparticles encapsulated in Nitrogen-Doped carbon nanotubes on Ni foam integrated Electrode: Highly electroactive and durable catalysts for overall water splitting, J. Colloid Interface Sci. 606 (2022) 38–46.
885
886
887
888 https://doi.org/10.1016/j.jcis.2021.07.152.

889 [49] W. Wu, J. Liu, N. Johannes, Electrodeposition of Ir–Co thin films on copper foam as
890 high-performance electrocatalysts for efficient water splitting in alkaline medium, Int. J.
891 Hydrogen Energy. 46 (2021) 609–621. https://doi.org/10.1016/j.ijhydene.2020.09.268.

892 [50] N. Girichandran, S. Saedy, R. Kortlever, Electrochemical CO2 reduction on a copper
893 foam electrode at elevated pressures, Chem. Eng. J. 487 (2024) 150478.
894 https://doi.org/10.1016/j.cej.2024.150478.
895

[51] W. Zheng, M. Liu, L.Y.S. Lee, Best Practices in Using Foam-Type Electrodes for

896

Electro catalytic Performance Benchmark, ACS Energy Lett. 5 (2020) 3260–3264.

897

https://doi.org/10.1021/acsenergylett.0c01958.

898

[52] J. Van Drunen, B. Kinkead, M.C.P. Wang, E. Sourty, B.D. Gates, G. Jerkiewicz,

899

Comprehensive structural, surface-chemical and electrochemical characterization of

900

nickel-based metallic foams, ACS Appl. Mater. Interfaces. 5 (2013) 6712–6722.

901

https://doi.org/10.1021/am401606n.

902

[53] Y.J. Shih, Z.L. Wu, Y.H. Huang, C.P. Huang, Electrochemical nitrate reduction as

903

affected by the crystal morphology and facet of copper nanoparticles supported on nickel

904

foam electrodes (Cu/Ni), Chem. Eng. J. 383 (2020) 123157.

905

https://doi.org/10.1016/j.cej.2019.123157.

906

[54] F. Jamshidi, W. Kunz, P. Altschuh, T. Lu, M. Laqua, A. August, F. Löffler, M. Selzer,

907

B. Nestler, A 3D computational method for determination of pores per inch (PPI) of

908

porous structures, Mater. Today Commun. 34 (2023) 105413.

909

https://doi.org/10.1016/j.mtcomm.2023.105413.

910

[55] S. Das, N.G. Deen, J.A.M. Kuipers, Multiscale modeling of fixed-bed reactors with

911

porous (open-cell foam) non-spherical particles: Hydrodynamics, Chem. Eng. J. 334

912

(2018) 741–759. https://doi.org/10.1016/j.cej.2017.10.047.

913

[56] I. Mohammed, T. Bauer, M. Schubert, R. Lange, Hydrodynamic multiplicity in a tubular

914

reactor with solid foam packings, Chem. Eng. J. 231 (2013) 334–344.

915

https://doi.org/10.1016/j.cej.2013.07.024.

916

[57] L.F. Arenas, C. Ponce de León, F.C. Walsh, 3D-printed porous electrodes for advanced

917

electrochemical flow reactors: A Ni/stainless steel electrode and its mass transport

918

characteristics, Electrochem. Commun. 77 (2017) 133–137.

919

https://doi.org/10.1016/j.elecom.2017.03.009.
920 [58] A.S. Suleiman, N. Dukhan, Long-domain simulation of flow in open-cell mesoporous
921 metal foam and direct comparison to experiment, Microporous Mesoporous Mater. 196
922 (2014) 104–114. https://doi.org/10.1016/j.micromeso.2014.05.003.

923 [59] F.J. Recio, P. Herrasti, L. Vazquez, C. Ponce De León, F.C. Walsh, Mass transfer to a
924 nanostructured nickel electrodeposit of high surface area in a rectangular flow channel,
925 Electrochim. Acta. 90 (2013) 507–513. https://doi.org/10.1016/j.electacta.2012.11.135.

926 [60] D.F.S. Morais, J.C.B. Lopes, M.M. Dias, V.J.P. Vilar, F.C. Moreira, e–NETmix: A
927 pioneering electrochemical flow reactor with enhanced mass transfer, Chem. Eng. J. 481
928 (2024). https://doi.org/10.1016/j.cej.2023.148244.

929 [61] Z. Qiao, Z. Wang, C. Zhang, S. Yuan, Y. Zhu, J. Wang, PVAm–PIP/PS composite
930 membrane with high performance for CO2/N2 separation, AIChE J. 59 (2012) 215–228.
931 https://doi.org/10.1002/aic.

932 [62] W.S. Walker, E. Bezerra Cavalcanti, A. Atrashkevich, A.S. Fajardo, E. Brillas, S.
933 Garcia-Segura, Mass transfer and residence time distribution in an electrochemical cell
934 with an air-diffusion electrode: Effect of air pressure and mesh promoters, Electrochim.
935 Acta. 378 (2021) 138131. https://doi.org/10.1016/j.electacta.2021.138131.

936 [63] M.J. Kim, Y. Seo, M.A. Cruz, B.J. Wiley, Metal Nanowire Felt as a Flow-Through
937 Electrode for High-Productivity Electrochemistry, ACS Nano. 13 (2019) 6998–7009.
938 https://doi.org/10.1021/acsnano.9b02058.

939 [64] Y. Chen, G. Zhang, Q. Ji, H. Lan, H. Liu, J. Qu, Visualization of Electrochemically
940 Accessible Sites in Flow-through Mode for Maximizing Available Active Area toward
941 Superior Electrocatalytic Ammonia Oxidation, Environ. Sci. Technol. 56 (2022) 9722–
942 9731. https://doi.org/10.1021/acs.est.2c01707.

943 [65] M.C.O. Monteiro, A. Mirabal, L. Jacobse, K. Doblhoff-Dier, S.C. Barton, M.T.M.
944 Koper, Time-Resolved Local pH Measurements during CO2Reduction Using Scanning
945 Electrochemical Microscopy: Buffering and Tip Effects, JACS Au. 1 (2021) 1915–1924.

946 https://doi.org/10.1021/jacsau.1c00289.

947 [66] M.C.O. Monteiro, M.T.M. Koper, Measuring local pH in electrochemistry, Curr. Opin.

948 Electrochem. 25 (2021) 100649. https://doi.org/10.1016/j.coelec.2020.100649.

949 [67] J.H. Han, Exploring the Interface of Porous Cathode/Bipolar Membrane for Mitigation

950 of Inorganic Precipitates in Direct Seawater Electrolysis, ChemSusChem. 15 (2022).

951 https://doi.org/10.1002/cssc.202200372.

952 [68] R. Mao, H. Zhu, K.F. Wang, X. Zhao, Selective conversion of nitrate to nitrogen gas by

953 enhanced electrochemical process assisted by reductive Fe(II)-Fe(III) hydroxides at

954 cathode surface, Appl. Catal. B Environ. 298 (2021) 120552.

955 https://doi.org/10.1016/j.apcatb.2021.120552.

956 [69] M.D. Patil, S.D. Dhas, A.A. Mane, A. V. Moholkar, Clinker-like V2O5 nanostructures

957 anchored on 3D Ni-foam for supercapacitor application, Mater. Sci. Semicond. Process.

958 133 (2021) 105978. https://doi.org/10.1016/j.mssp.2021.105978.

959 [70] M. Thommes, K. Kaneko, A. V. Neimark, J.P. Olivier, F. Rodriguez-Reinoso, J.

960 Rouquerol, K.S.W. Sing, Physisorption of gases, with special reference to the evaluation

961 of surface area and pore size distribution (IUPAC Technical Report), Pure Appl. Chem.

962 87 (2015) 1051–1069. https://doi.org/10.1515/pac-2014-1117.

963 [71] E. Gregorová, T. Uhlířová, W. Pabst, P. Diblíková, I. Sedlářová, Microstructure

964 characterization of mullite foam by image analysis, mercury porosimetry and X-ray

965 computed microtomography, Ceram. Int. 44 (2018) 12315–12328.

966 https://doi.org/10.1016/j.ceramint.2018.04.019.

967 [72] S. Trasatti, O.A. Petrii, International Union of Pure and Applied Chemistry Physical

968 Chemistry Division Commission on Electrochemistry: Real Surface Area Measurements

969 in Electrochemistry, Pure Appl. Chem. 63 (1991) 711–734.
970 https://doi.org/10.1351/pac199163050711.

971 [73] A. Lasia, A. Rami, Kinetics of hydrogen evolution on nickel electrodes, J. Electroanal.

972 Chem. 294 (1990) 123–141. https://doi.org/10.1016/0022-0728(90)87140-F.

973 [74] T.R.L.C. Paixão, Measuring Electrochemical Surface Area of Nanomaterials versus the

974 Randles–Ševčík Equation, ChemElectroChem. 7 (2020) 3414–3415.

975 https://doi.org/10.1002/celc.202000633.

976 [75] P. Connor, J. Schuch, B. Kaiser, W. Jaegermann, The Determination of Electrochemical

977 Active Surface Area and Specific Capacity Revisited for the System MnOx as an

978 Oxygen Evolution Catalyst, Zeitschrift Fur Phys. Chemie. 234 (2020) 979–994.

979 https://doi.org/10.1515/zpch-2019-1514.

980 [76] C. Hurth, C. Li, A.J. Bard, Direct probing of electrical double layers by scanning

981 electrochemical potential microscopy, J. Phys. Chem. C. 111 (2007) 4620–4627.

982 https://doi.org/10.1021/jp0661084.

983 [77] A.J. Bard, L.R. Faulkner, Fundamentals and Applications Plasmonics : Fundamentals

984 and Applications, 2nd ed., John Wiley & Sons, Inc., 2001.

985 [78] O. Gharbi, M.T.T. Tran, M.E. Orazem, B. Tribollet, M. Turmine, V. Vivier, Impedance

986 Response of a Thin Film on an Electrode: Deciphering the Influence of the Double Layer

987 Capacitance, ChemPhysChem. 22 (2021) 1371–1378.

988 https://doi.org/10.1002/cphc.202100177.

989 [79] D.M. Morales, M. Risch, Seven steps to reliable cyclic voltammetry measurements for

990 the determination of double layer capacitance, JPhys Energy. 3 (2021) 034013.

991 https://doi.org/10.1088/2515-7655/abee33.

992 [80] S. Akkari, V. Vivier, C.M. Sánchez-Sánchez, Urea electro-oxidation byproducts impact

993 on NiO/NiOOH anode performance studied by operando electrochemical impedance

994 spectroscopy, Electrochim. Acta. 474 (2024) 143526.
995 https://doi.org/10.1016/j.electacta.2023.143526.

996 [81] G.J. Brug, A.L.G. van den Eeden, M. Sluyters-Rehbach, J.H. Sluyters, The analysis of

997 electrode impedances complicated by the presence of a constant phase element, J.

998 Electroanal. Chem. 176 (1984) 275-295. https://doi.org/10.1016/S0022-0728(84)80324-1.

999 1.

1000 [82] O. Gharbi, M.T.T. Tran, B. Tribollet, M. Turmine, V. Vivier, Revisiting cyclic

1001 voltammetry and electrochemical impedance spectroscopy analysis for capacitance

1002 measurements, Electrochim. Acta. 343 (2020) 136109.

1003 https://doi.org/10.1016/j.electacta.2020.136109.

1004 [83] S.P. Zankowski, P.M. Vereecken, Electrochemical Determination of Porosity and

1005 Surface Area of Thin Films of Interconnected Nickel Nanowires, J. Electrochem. Soc.

1006 166 (2019) D227-D235. https://doi.org/10.1149/2.0311906jes.

1007 [84] U.S.G. Survey, Rare Earth Elements — Critical Resources for High Technology, (2002)

1008 1-11.

1009 [85] X. Lu, H. Song, J. Cai, S. Lu, Recent development of electrochemical nitrate reduction

1010 to ammonia: A mini review, Electrochem. Commun. 129 (2021) 107094.

1011 https://doi.org/10.1016/j.elecom.2021.107094.

1012 [86] J. Gao, B. Jiang, C. Ni, Y. Qi, X. Bi, Enhanced reduction of nitrate by noble metal-free

1013 electrocatalysis on P doped three-dimensional Co3O4 cathode: Mechanism exploration

1014 from both experimental and DFT studies, Chem. Eng. J. 382 (2020) 123034.

1015 https://doi.org/10.1016/j.cej.2019.123034.

1016 [87] Y. Wang, C. Wang, M. Li, Y. Yu, B. Zhang, Nitrate electroreduction: Mechanism

1017 insight,: In situ characterization, performance evaluation, and challenges, Chem. Soc.

1018 Rev. 50 (2021) 6720-6733. https://doi.org/10.1039/d1cs00116g.

1019 [88] X. bo Li, G. ri Xu, Hydrothermal vs electrodeposition: How does deposition method
1020 affect the electrochemical capacitor performance of manganese dioxide?, Ceram. Int. 43
1021 (2017) 8963-8969. https://doi.org/10.1016/j.ceramint.2017.04.036.

1022 [89] D. Taherinia, M. Moazzeni, S. Moravej, Comparison of hydrothermal and
1023 electrodeposition methods for the synthesis of CoSe2/CeO2 nanocomposites as
electrocatalysts toward oxygen evolution reaction, Int. J. Hydrogen Energy. 47 (2022)
1024 17650-17661. https://doi.org/10.1016/j.ijhydene.2022.03.257.
1025

1026 [90] G.A. Cerrón-Calle, A. Maya, D. Leon, M. Roldan, A.S. Fajardo, C.M. Sánchez-Sánchez, S. Garcia-Segura, Unlocking Sustainable Nitrate Reduction: Earth-Abundant Bimetallic Electrodes Under Galvanostatic Evaluation, Electrochim. Acta. (2024) 144263.
1027
1028
1029 https://doi.org/10.1016/j.electacta.2024.144263.

1030 [91] Y. Xu, Y. Sheng, M. Wang, T. Ren, K. Shi, Z. Wang, X. Li, L. Wang, H. Wang, Interface coupling induced built-in electric fields boost electrochemical nitrate reduction to ammonia over CuO@MnO2 core-shell hierarchical nanoarrays, J. Mater. Chem. A. 10 (2022) 16883-16890. https://doi.org/10.1039/d2ta02006h.

1034 [92] C. Zhou, J. Bai, Y. Zhang, J. Li, Z. Li, P. Jiang, F. Fang, M. Zhou, X. Mei, B. Zhou, Novel 3D Pd-Cu(OH)2/CF cathode for rapid reduction of nitrate-N and simultaneous total nitrogen removal from wastewater, J. Hazard. Mater. 401 (2021) 123232.
1035
1036
1037 https://doi.org/10.1016/j.jhazmat.2020.123232.

1038 [93] X. Wang, M. Zhu, G. Zeng, X. Liu, C. Fang, C. Li, A three-dimensional Cu nanobelt cathode for highly efficient electrocatalytic nitrate reduction, Nanoscale. 12 (2020) 9385-9391. https://doi.org/10.1039/c9nr10743f.

1041 [94] Y. Zhang, Y. Zhao, Z. Chen, L. Wang, L. Zhou, P. Wu, F. Wang, P. Ou, Fe/Cu Composite Electrode Prepared by Electrodeposition and Its Excellent Behavior in Nitrate Electrochemical Removal, J. Electrochem. Soc. 165 (2018) E420-E428.
1042
1043
1044 https://doi.org/10.1149/2.0081810jes.
1045 [95] W. Fu, Y. Du, J. Jing, C. Fu, M. Zhou, Highly selective nitrate reduction to ammonia on
1046 CoO/Cu foam via constructing interfacial electric field to tune adsorption of reactants,
1047 Appl. Catal. B Environ. 324 (2023) 122201.
1048 https://doi.org/10.1016/j.apcatb.2022.122201.

1049 [96] F. Yao, M. Jia, Q. Yang, F. Chen, Y. Zhong, S. Chen, L. He, Z. Pi, K. Hou, D. Wang, X.
1050 Li, Highly selective electrochemical nitrate reduction using copper phosphide self-
1051 supported copper foam electrode: Performance, mechanism, and application, Water Res.
1052 193 (2021). https://doi.org/10.1016/j.watres.2021.116881.

1053 [97] X. Tan, X. Wang, T. Zhou, T. Chen, Y. Liu, C. Ma, H. Guo, B. Li, Preparation of three
dimensional bimetallic Cu–Ni/NiF electrodes for efficient electrochemical removal of
nitrate nitrogen, Chemosphere. 295 (2022) 133929.
1054
1055
1056 https://doi.org/10.1016/j.chemosphere.2022.133929.

1057 [98] S. Zhang, M. Li, J. Li, Q. Song, X. Liu, High-ammonia selective metal – organic
framework – derived Co-doped Fe / Fe 2 O 3 catalysts for electrochemical nitrate
reduction, 119 (2022) 2115504119.
1058
1059
1060 https://doi.org/https://doi.org/10.1073/pnas.2115504119.

1061 [99] J. Wang, J. Feng, T.O. Soyol-Erdene, Z. Wei, W. Tang, Electrodeposited NiCoP on
1062 nickel foam as a self-supported cathode for highly selective electrochemical reduction of
nitrate to ammonia, Sep. Purif. Technol. 320 (2023) 124155.
1063
1064 https://doi.org/10.1016/j.seppur.2023.124155.

1065 [100] Y. Gao, R. Wang, Y. Li, E. Han, M. Song, Z. Yang, F. Guo, Y. He, X. Yang, Regulating
dynamic equilibrium of active hydrogen for super-efficient nitrate electroreduction to
ammonia, Chem. Eng. J. 474 (2023) 145546. https://doi.org/10.1016/j.cej.2023.145546.

1068 [101] W. Tao, P. Wang, B. Hu, X. Wang, G. Zhou, Accelerating the reaction kinetics from
nitrate to ammonia by anion substitution in NiCo-based catalysts, J. Environ. Chem.
Eng. 11 (2023) 109117. https://doi.org/10.1016/j.jece.2022.109117.
1071 [102] Z.H. Xue, H.C. Shen, P. Chen, G.X. Pan, W.W. Zhang, W.M. Zhang, S.N. Zhang, X.H. Li, C.T. Yavuz, Boronization of Nickel Foam for Sustainable Electrochemical Reduction of Nitrate to Ammonia, ACS Energy Lett. (2023) 3843-3851.
1072
1073
1074 https://doi.org/10.1021/acsenergylett.3c01139.

1075 [103] Y. Wang, S. Shu, M. Peng, L. Hu, X. Lv, Y. Shen, H. Gong, G. Jiang, Dual-site electrocatalytic nitrate reduction to ammonia on oxygen vacancy-enriched and Pd-decorated MnO2nanosheets, Nanoscale. 13 (2021) 17504-17511.
1076
1077
1078 https://doi.org/10.1039/d1nr04962c.

1079 [104] F. Li, Y.C. Li, Z. Wang, J. Li, D.-H. Nam, Y. Lum, M. Luo, X. Wang, A. Ozden, S.-F. Hung, B. Chen, Y. Wang, J. Wicks, Y. Xu, Y. Li, C.M. Gabardo, C.-T. Dinh, Y. Wang, T.-T. Zhuang, D. Sinton, E.H. Sargent, Cooperative CO2-to-ethanol conversion via enriched intermediates at molecule-metal catalyst interfaces, Nat. Catal. 3 (2020) 75-82.
1080
1081
1082
1083 https://doi.org/10.1038/s41929-019-0383-7.

1084 [105] E. Vichou, A. Solé-Daura, C. Mellot-Draznieks, Y. Li, M. Gomez-Mingot, M. Fontecave, C.M. Sánchez-Sánchez, Electrocatalytic Conversion of CO2 to Formate at Low Overpotential by Electrolyte Engineering in Model Molecular Catalysis,
1085
1086
1087 ChemSusChem. 15 (2022) 1-10. https://doi.org/10.1002/cssc.202201566.

1088