Theoretical advances in understanding the active site microenvironment toward the electrocatalytic nitrogen reduction reaction in aqueous media

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Theoretical advances in understanding the active site microenvironment toward the electrocatalytic nitrogen reduction reaction in aqueous media © 2023 the Authors Published version Wu, Tongwei; Melander, Marko M.; Honkala, Karoliina Wu, T., Melander, M. M., & Honkala, K. (2023). Theoretical advances in understanding the active site microenvironment toward the electrocatalytic nitrogen reduction reaction in aqueous media. Current Opinion in Electrochemistry, 42, Article 101383. https://doi.org/10.1016/j.coelec.2023.101383 2023 Review Article Theoretical advances in understanding the active site microenvironment toward the electrocatalytic nitrogen reduction reaction in aqueous media Tongwei Wu 1,2 , Marko M. Melander 3 and Karoliina Honkala 3 Abstract The electrocatalytic nitrogen reduction reaction (eNRR) in aqueous media has received substantial attention because it enables the direct conversion of N 2 to NH 3 under benign conditions. There are, however, many factors limiting the overall eNRR efficiency, including the competing hydrogen evolution reaction (HER) and sluggish reaction kinetics due to a strong N^N bond. These challenges call for more systematic theoretical insight into the eNRR reaction mechanism to guide the rational optimization of experimental designs. In this review, we present the latest computational advances in eNRR in an aqueous medium, including the key aspects of both catalyst design and proton accessibility. Specifically, we discuss the importance of constant potential and explicit solvent simulations, the role of the electrochemical interface, and the impact of the active center microenvironment on eNRR activity and selectivity. Finally, the current challenges and the future prospects for eNRR are addressed. Addresses 1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China 2 School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054 PR China 3 Department of Chemistry, Nanoscience Center, University of Jyväskylä, Jyväskylä, FI-40014, Finland Corresponding author: Honkala, Karoliina. ([email protected]) Current Opinion in Electrochemistry 2023, 42:101383 This review comes from a themed issue on Fundamental and Theoretical Electrochemistry (2024) Edited by Kai S. Exner For a complete overview see the Issue and the Editorial Available online 29 August 2023 https://doi.org/10.1016/j.coelec.2023.101383 2451-9103/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). Keywords Electrocatalysis, Ambient NH 3 synthesis, Microenvironment, Theoretical calculations. Introduction Ammonia (NH 3 ) is one of the most fundamental raw materials in modern industry and agricultural production [1e6]. Additionally, it is regarded as an excellent hydrogen carrier due to its small carbon footprint and ease of storage and transportation [3]. The industrial HabereBosch process is a mature technology to synthesize NH 3 , but it requires high temperatures and pressures. Such harsh conditions consume 2e3% of the world’s energy supply annually and require hydrogen as a proton feedstock [6]. Traditionally, hydrogen is produced via steam reforming, which emits large amounts of carbon dioxide [6e8]. Therefore, it is particularly important to develop methods to produce ammonia under mild conditions without utilizing fossil hydrogen as a proton source [8,9]. The electrocatalytic N 2 reduction reaction (eNRR) has recently gained increasing attention because it can achieve NH 3 production under mild conditions utilizing renewable electricity and water as a hydrogen source [10e18]. Currently, eNRR efficiency in aqueous media is below what is needed at the industrial scale or associated with naturally occurring nitrogenase enzymes [18,21e23]. The Li-mediated process in organic solvents has been experimentally verified and extensively reviewed recently [19,20]. We focus on how the aqueous eNRR activity and selectivity could be improved through advanced electrode design principles. This is motived by a recent review [25] highlighting the fact that despite many false-positive eNRR catalysts and general disbelief in the viability of aqueous eNRR, advanced electrode design approaches deserve further investigation before conclusively dismissing aqueous eNRR [14,24,25]. At the atomic level, efficient eNRR requires a catalyst to bind a N 2 molecule and carry out the multiple protoncoupled electron transfer (PCET) steps to form NH 3 [18]. A natural nitrogenase enzyme can catalyze a key step of the nitrogen cycle, converting atmospheric nitrogen into bioavailable NH 3 through a FeMo cofactor under mild conditions [21,22]. This cofactor possesses Available online at www.sciencedirect.com ScienceDirect Current Opinion in Electrochemistry www.sciencedirect.com Current Opinion in Electrochemistry 2023, 42:101383 an impressive activity associated with its structure, which consists of Fe atoms coupled with a Mo atom via sulfur atoms. It has been demonstrated that the sulfur atoms in the FeMo cofactor are labile that, in turn, has led to a suggestion of a sulfur atom replacement with N being integral into N 2 activation and reduction [22,23]. Inspired by this, it is strongly emphasized that the catalyst is one of the most important components to achieve eNRR at mild conditions while the migration of sulfur sites during catalysis indicates that the active sites are dynamic in nature. Alternatively, there is a network of hydrogen bonds established between amino acid residues and water molecules around N 2 at the active site in nitrogenase enzymes [22]; this implies that the availability of protons and electrons to effectively controls the eNRR activity and selectivity. To summarize, the nearly ideal performance of nitrogenases can be attributed to the combination of a NRR active catalytic site, the dynamics of a reaction center, and the limited number of protons near catalytic centers. Inspired by these findings, we specifically review the latest computational advances in the eNRR from an atomistic viewpoint for both catalyst design and proton accessibility regulation aspects, as well as provide a perspective how the surface microenvironments of electrocatalysts could improve eNRR. Atomic and electronic descriptors Electrocatalysts and the electrochemical interfaces play an integral role in eNRR’s ability to tune selectivity, reduce energy consumption, and maximize conversion efficiency. The majority of relevant studies have focused on modulating the composition, morphology, size, crystal structure, crystallinity, and internal electronic structure of catalysts [11,17,26,27]. In recent years, researchers have begun to explore surface activation to modify catalyst properties, such as hydrophobicity, adsorption ability, electronegativity, localization, and atomic/electronic structure [18,28]. While significant improvements have been made, satisfactory activity and selectivity have not been achieved, and rational catalyst design has remained difficult as it is partially unclear how factors such as active center reactivity, dynamics, and proton availability contribute to the activity and Faradaic efficiency of eNRR [12,18]. Therefore, to develop highly efficient and selective eNRR electrocatalysts, the systematic atomic-level understanding of eNRR chemistry is an important prerequisite. In general, the eNRR is proposed to proceed either via a dissociative or associative mechanism [21,29,30], as shown in Figure 1a. The industrial HabereBosch process follows the dissociative pathway and requires harsh reaction conditions [31,32]. However, for eNRR, the Figure 1 (a) Schematic illustrations for eNRR pathway [30]. Reproduced from Ref. [30] with permission from Wiley-VCH. (b,c) The scaling relationship between adsorption energies and the E ad[N*] descriptor on various metal surfaces [33]. Reproduced from Ref. [33] with permission from the Royal Society of Chemistry. (d,e) The limiting potentials of eNRR and HER with E ad[N*] descriptor on various metal surfaces [34]. Reproduced from Ref. [34] with permission from Wiley-VCH. 2Fundamental and Theoretical Electrochemistry (2024) Current Opinion in Electrochemistry 2023, 42:101383 www.sciencedirect.com associative pathway, including multiple reaction species, is widely accepted as a possible mechanism under mild reaction conditions [30]. The role of the binding environment of key reaction intermediates on catalyst surfaces has been extensively studied at the atomic level. For instance, Nørskov et al. have calculated the binding energies for all the reaction intermediates along the associative eNRR pathway including NH x and N 2 H x (x = 0, 1, 2) molecules on the variety of metal surfaces [33,34]. They found that the binding energies of NH x and N 2 H x scale well with N binding energy (E ad[N*] ), which can be used as a descriptor, see Figure 1b and 1c. This linear scaling between reaction intermediates precludes the tuning of binding energies independently from each other, which is represented in the form of volcano plot [33e35]. Importantly, it is noted that the linear scaling relationships of *N 2 H and *NH 2 fundamentally limit eNRR activity on solid metal surfaces due to strong binding on the catalytically active sites [34]. Furthermore, the comparison of hydrogen evolution reaction (HER) and eNRR limiting potentials as a function of E ad[N*] descriptor shows that the HER limiting potential is less negative than that for eNRR, as shown in Figure 1d and e. This is consistent with the fact that low eNRR activity and selectivity generally originate from the severely competing HER process [18] and implies that the E ad[N*] descriptor is an appropriate descriptor for monitoring eNRR activity, but it is certainly not the only one. Indeed, other adsorption energies, such as *NH and N 2 H*, have also been used as the activity descriptors [36e39]. In addition, conventional energetic descriptors, also multiple electronic descriptors, have been proposed, including the polarization response of an active site, an electric dipole of the adsorbed N 2 , and the number of d orbital electrons and empty orbitals [40e43]. For instance, it has been demonstrated that the dipole moment of the adsorbed N 2 molecule can also be used as an effective theoretical indicator for the catalytic performance of active sites for eNRR, as shown in Figure 2a and b. Based on the dipole indicator, several single transition metal (TM) atoms were screened via density functional theory (DFT) calculations in twodimensional phthalocyanine (2D Pc) organic frameworks, and 2D Mo-Pc was proposed as a promising SAC for eNRR with extremely low onset potential of 0.25 V [44]. The origin of such high catalytic activity was associated with the large dipole moment introduced into the N^N bond via strong MoeN interactions, thus promoting the activation of the N^N triple bond through the occupation of p * antibonding orbitals [44]. N 2 activation can take place either by donating electrons from N 2 to the empty orbitals of an active site or by accepting electrons from the partially occupied orbitals of an active site to the antibonding orbitals of N 2 [45,46]. Recently, sandwich structures with a single TM atom between hexagonal boron nitride (h-BN) and graphene sheets (namely, BN/TM/G) were designed for eNRR through DFT calculations [47], which suggested that TM SACs can donate charge to h-BN and then create an optimized polarization field on the surface, as shown in Figure 2c. This interfacial polarization field enables electron donation from the TM atom to a neighboring B atom, which then acts as the active site to catalyze eNRR (Figure 2c) [47]. Accounting for charge transfer at the reaction center to activate N 2 , the relationship between the intrinsic electronic properties and catalytic activity was established through a highthroughput DFT screening of 23 TM-SACs. The identified electronic descriptor of empty d orbitals explains the low overpotential and good eNRR activity, as shown in Figure 2d and e [48]. This analysis suggests that catalytic activity correlates with the local microenvironment of active centers and that eNRR can be enhanced by adjusting the orbital occupation at the active site. While major efforts have been undertaken to improve the eNRR catalysts, the rational design of active and efficient electrocatalysts is still far from satisfactory. Three reasons can be identified for this. Firstly, the poor activity and high eNRR overpotentials originate from the scaling relations (volcano relationships) between adsorption energies of nitrogen-containing intermediates (NH x and N 2 H x ) on catalyst surfaces and prevent electrocatalysts to approach the region of optimal eNRR activity. To achieve high eNRR activity at low overpotentials, the simultaneous competitive HER process should be avoided [8,18]. Secondly, the kinetics of the first PCETstep to form N 2 H* limits activity [18]. Thirdly, computational models often exclude components defining the properties of electrochemical interfaces, such as pH, electrolyte, solvent, and electrode potential effects, leading to electrostatic and other noncovalent interactions, and dynamic processes [49,50]. Hence, to move forward, we need to simultaneously establish strategies to break eNRR scaling relationships, limit HER, accelerate the PCET kinetics, and control the electrochemical interface. This calls for the use of advanced computational techniques and tighter integration with experiments [50]. New strategies for eNRR: the electrode potential, solvent, and electrolyte effects Besides structural modifications, the explicit inclusion of the electrode potential via grand-canonical ensemble (GCE) DFT [51] may enable breaking the scaling relations. Unlike canonical DFT with the computational hydrogen electrode (CHE) model, GCE-DFT can be used to model thermodynamics, kinetics, and charge transfer as a function of the electrode potential [52e55]. Recent GCE-DFT calculations lead to different scaling relations than canonical DFT calculations because non-PCET steps also depend on the Advances in electrocatalytic nitrogen reduction Wu et al. 3 www.sciencedirect.com Current Opinion in Electrochemistry 2023, 42:101383 electrode potential [56]. In another study, GCE-DFT calculations [57] ascribed the experimentally verified [58] activity of the Chevrel phase Fe 2 Mo 6 S 8 surface, containing a similar FeeSeMo coordination environment as a nitrogenase enzyme’s FeMo-cofactor, to the simultaneous N 2 adsorption and dissociation of a subsurface FeeS bond that leads to the formation of free atom-like d-states, see Figure 3a. These d-states selectively stabilize the *NNH intermediate relative to *N 2 or *NH 3 because they significantly overlap with the pstates of *NNH but minimally with the p-states of *N 2 or *NH 3 , see Figure 3a. The computational study shows the need to explicitly include the electrode potential but also demonstrates that the dynamic nature of proposed eNRR active site resembles the one suggested for the nitrogenase FeMo-cofactor where two FeeS bonds break to produce the active Fe site. The FeeS subsurface bond remains broken until *NH 3 desorbs, and when a proton adsorbs at Fe active site to enable HER, the broken FeeS bonds will return to their initial state. The dynamic restructuring of the atomic environment around the Fe active site allows breaking the scaling relations as the catalyst selectively stabilizes the nitrogen intermediates to decrease D G of the first hydrogenation step, while binding *NH 3 weakly to release NH 3 gas, see Figure 3b and c. In general, considering the explicit electrode potential and solvent effects is crucial for modeling eNRR. On a single Fe atom catalyst embedded in N-doped graphene (FeeN 4 ), eNRR depends sensitively on both the electrode potential and solvent [59]. The comparison between gaseous and aqueous systems shows that the water environment can facilitate the adsorption and activation of N 2 on the Fe site due to stronger N 2 adsorption and electron injection to the empty p  orbital, respectively, as shown Figure 3d[59]. Furthermore, the electrode potential modifies kinetics for the hydrogenation step as the distal N of *N2 gains more electrons and makes it more capable of accepting Figure 2 (a) Variation of Gibbs free energies and dipole moments of the N 2 molecule adsorbed on 2D TM-Pc with different transition metals [44]. (b) The optimized N 2 adsorption configurations on the 2D Mo-Pc, and the project density of states (PDOS) for the N-2p and Mo-4d states in the end-on adsorption geometry together with the 2p states of an isolated N 2 molecule [44]. Reproduced from Ref. [44] with permission from the Royal Society of Chemistry. (c) Geometrical structures (top panel), electrostatic potential along the plane across the N 2 and B sites (middle panel), and electron density differences (bottom panel) of N 2 adsorbed on BN/V/G [47]. Reproduced from Ref. [47] with permission from the American Chemical Society. (d) The PDOS plot is projected onto the d orbital of different metal atoms [48]. (e) The relationship between the percentage of empty d orbital and limiting potential [48]. Reproduced from Ref. [48] with permission from Springer Nature. 4Fundamental and Theoretical Electrochemistry (2024) Current Opinion in Electrochemistry 2023, 42:101383 www.sciencedirect.com protons at negative electrode potential without a significant impact on N 2 adsorption, see Figure 3e and f. Finally, the estimated electrode potential for eNRR agrees well with the experimental results supporting the necessity to consider electrode potential and explicit solvation effects in calculations. We have also recently addressed the eNRR on a graphene-embedded RueN 4 model, using GCE-DFT to study the reaction mechanism, thermodynamics, and kinetics as an explicit function of the electrode potential [60]. Our results show that the eNRR/HER competition cannot be understood without using GCE-DFT to address the potential dependency of the reaction. The charge transfer from an initial state to a final state for Volmer and N 2 hydrogeneration reactions at the RueN 4 exhibits unusual nonlinear behavior, in contrast with metallic Au and Pt systems making the activation energy more sensitive to the electrode potential than the reaction energy. The study further highlights the importance of the coadsorption of key reaction intermediates (N 2 * and H*), and the pivotal role of the non-innocent ligands may play in eNRR selectivity, as shown in Figure 4aand b. This allowed us to disentangle different factors that contribute to eNRR activity on RueN 4 , but unfortunately, HER is more favorable than the first *N 2 protonation step hampering the catalytic activity of the RueN 4 catalyst in experiments [60,61]. In similar vein, eNRR on a FeeN 4 electrocatalyst was studied by combining GCE-DFT calculations and the microkinetic analysis to identify the potential-dependent crossover between the H- and N 2 -binding affinities. At low potentials, *N 2 is favored over *H, but at more negative potentials, the trend is reversed as shown in Figure 4c and d [62]. This observation is consistent with experiments displaying the activity drop for eNRR as a function of potential. The crossover from *N 2 to *H is due to the larger charge transfer in the formation of H* compared to N 2 *, which shows that potential-dependent charge transfer affects the relative affinity of surface intermediate, thus crucially impacting NRR activity. While the above examples demonstrate that GCE-DFT and explicit solvent models are needed to simulate eNRR, experiments have shown that eNRR is also very sensitive to electrolyte, hydrophobicity, and pH effects [18,63e68]. For instance, a hydrophobic electrochemical interface constructed using fluorinated solvents limits Figure 3 (a) Atomic structure and PDOS plots for the bare Fe 2 Mo 6 S 8 surface, the Fe 2 Mo 6 S 8 surface with *N 2 , *NNH and *NH 3 , and *NNH [57]. The associative alternating (opaque) and associative distal (transparent) eNRR pathways at Fe (b) and Mo (c) surface sites in H 2 O solvent at different electrode potentials [57]. Reproduced from Ref. [57] with permission from the American Chemical Society. (d) Free energy profile of the preferred reaction pathway in clean (no solvation) and liquid (explicit solvation) systems on a FeN 4 catalyst [59]. Calculated free energy profiles for N 2 adsorption (e) and the first N 2 protonation step (f) at different electrode potentials [59]. Reproduced from Ref. [59] with permission from the American Chemical Society. Advances in electrocatalytic nitrogen reduction Wu et al. 5 www.sciencedirect.com Current Opinion in Electrochemistry 2023, 42:101383 the supply of protons, which, in turn, leads to a higher N 2 coverage on the catalytic sites, improved N 2 solubility compared to water, and consequently enhanced the Faradaic efficiency toward NH 3 [67]. More generally, microkinetic [12] and transport [68] studies have established some efficient ways for modulating the N 2 and H þ mass transport in the electric double layer to facilitate eNRR. A practical way to achieve this is through modulating catalyst pore sizes as larger pores are suggested to enhance N 2 mass transport and improve the Faradaic efficiency toward NH 3 [29]. Another strategy to control mass transfer is to use proton-filtering covalent organic frameworks (COFs) to enhance N 2 flux to a catalyst [69]. Molecular dynamics simulations have showed that electrostatic interfaces between the COF charge centers and H þ suppress HER while dispersion interactions between COF and N 2 concentrate nitrogen at the active catalyst sites [69]. While the importance of controlling the electrochemical interface microenvironment to improve eNRR activity and selectivity has been widely recognized [18,28], computational models are still in their infancy. Atomic and bottom-up multiscale models to understand, for example, cation effects, solvent dynamics and reorganization, and mass transfer in eNRR are scarce. These factors have, however, been investigated for other electrocatalytic reactions, such as CO 2 RR and ORR, which can serve as inspiration for eNRR modeling [53,70e73]. Yet, as it stands, atomistic understanding of the electrochemical interface during eNRR is highly limited and should be considered as the next frontier in the theoretical treatments of electrocatalytic ammonia production. Overall, these considerations indicate that aqueous eNRR may still be a viable approach to produce ammonia, but novel catalytic design concepts are needed. Figure 4 (a) The grand free energies of the competing eNRR (blue) and HER (green) pathways [51]. (b) The coadsorption model of *N 2 and *H including electrode potential U and explicit H 2 O solvent [60]. Reproduced from Ref. [60] with permission from the American Chemical Society. (c) Change of D G (*N 2 /*NNH), D G(*N 2 ), and D G(*H) by the electrode potential (U), where the red vertical dashed line represents the crossover potential U at D G(*H) = D G(*N 2 )) [62]. (d)NH 3 production rates and coverages obtained with the GCE-DFT-based microkinetic analysis [52]. Reproduced from Ref. [62] with permission from Nature. 6Fundamental and Theoretical Electrochemistry (2024) Current Opinion in Electrochemistry 2023, 42:101383 www.sciencedirect.com Concluding remarks In summary, the electrocatalytic N 2 reduction reaction accounts for a promising strategy for ammonia synthesis. However, the competing HER process, nitrogen scaling relations, and slow kinetics fundamentally limit the activity and selectivity of eNRR electrocatalysts far below the requirements of industrially viable electrocatalytic NH 3 synthesis. To improve the robustness of computational studies toward designing improved aqueous eNRR catalysts, future models should consider the following aspects: (1) Computational studies typically model eNRR catalysts and mechanisms at a solidevacuum interface, using conventional constant charge approaches, while real electrochemical reactions correspond to constant potential conditions and feature mobile electrolyte solutions. This calls for a wider adoption of constant potential methods and development of ways to treat the electrolyte efficiently. (2) While assuming mostly a static active site, real catalytic active centers are dynamic and respond to changes in a reaction environment and conditions. These dynamic effects also control the proton availability. Thus, the dynamic catalytic mechanisms for eNRR calculations must be carefully considered. (3) Aside from the solid catalysts, electrochemical interfaces possess a wealth of physicochemical interactions for eNRR, which significantly affect catalytic activity and proton availability. Thus, simulating only catalysts and binding sites is insufficient, and we need to pay more attention on investigating how the entire electrochemical interface impacts eNRR Author contributions T.W. wrote the first version of the manuscript. M.M.M. and K.H. revised the work. Declaration of competing interest The authors declare the following financial interests/ personal relationships which may be considered as potential competing interests: Tongwei Wu reports financial support was provided by National Natural Science Foundation of China. Tongwei Wu reports financial support was provided by Sichuan Natural Science Foundation. Tongwei Wu reports financial support was provided by China National Postdoctoral Program for Innovative Talents. Tongwei Wu reports financial support was provided by China Postdoctoral Science Foundation. Marko Melander reports financial support was provided by Research Council of Finland. Data availability This is review based on published works. Acknowledgements This work was supported by the National Natural Science Foundation of China (Nos. 52202214 and 52001059) and Sichuan Natural Science Foundation (No. 2023NSFSC0954). T.W. also acknowledges the support by the China National Postdoctoral Program for Innovative Talents (No. BX2021053) and China Postdoctoral Science Foundation (No.2021M700680). MMM was supported by the Research Council of Finland (grant #338228). KH gratefully acknowledges support by the Research Council of Finland (grant numbers 317739, 329977, and 351583) and the Jane and Aatos Erkko Foundation (funding to the LACOR project). 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