Strategies toward Selective Electrochemical Ammonia Synthesis

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Strategies Toward Selective Electrochemical Ammonia Synthesis Aayush R. Singh, Brian A. Rohr, Michael J. Statt, Jay A. Schwalbe, Matteo Cargnello, and Jens K. Nørskov ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.9b02245 • Publication Date (Web): 29 Jul 2019 Downloaded from pubs.acs.org on July 29, 2019

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ACS Catalysis

Strategies Toward Selective Electrochemical Ammonia Synthesis Aayush R. Singh,† Brian A. Rohr,† Michael J. Statt,† Jay A. Schwalbe,† Matteo Cargnello,† and Jens K. Nørskov∗,†,‡,¶ †SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, United States ‡SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, United States ¶Department of Physics, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark E-mail: [email protected]

Abstract The active and selective electroreduction of atmospheric nitrogen (N2 ) to ammonia (NH3 ) using energy from solar or wind sources at the point of use would enable a sustainable alternative to the Haber-Bosch process for fertilizer production. While the process is thermodynamically possible, experimental attempts thus far have required large overpotentials and have produced primarily hydrogen (H2 ). In this perspective, we show how insights from electronic structure calculations of the energetics of the process, combined with mean-field microkinetic modeling, can be used to (1) understand the activity and selectivity challenges in electrochemical NH3 synthesis, and (2) propose alternative strategies toward an economically viable process. In particular, we develop the theoretical understanding for two promising actionable avenues that are gaining

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interest in the experimental literature, (1) circumventing the scaling relations between adsorbed surface intermediates and (2) using non-aqueous electrolytes to suppress the competing hydrogen evolution reaction.

Keywords ammonia synthesis, electrocatalysis, selectivity, non-aqueous, density functional theory, microkinetic modeling, scaling relations

1. Introduction Ammonia (NH3 ), an important component used in large-scale fertilizer production, is currently produced industrially from atmospheric nitrogen (N2 ) and fossil-derived hydrogen (H2 ) via the Haber-Bosch process. 1 Three important drawbacks of this process are (1) the harsh conditions (high temperature and pressure) needed to break the highly inert N-N triple bond and maintain a thermodynamic driving force, 1–5 (2) the dependence on fossil fuel resources for energy and hydrogen (1% of the global energy supply and 3-5% of the global natural gas supply), 6 and (3) the need for advanced infrastructure for fertilizer distribution, not available in some parts of the world. 7 The development of a process to electrochemically reduce N2 to NH3 under ambient conditions using energy from solar or wind sources could help to alleviate these drawbacks and enable a sustainable process for making fertilizer directly at the point of use. If such a process could be discovered, it would also open up the large scale production of ammonia as an energy carrier. 8,9

While N2 reduction reaction (NRR) is favorable from a thermodynamic perspective, experimental attempts thus far using solid electrodes in aqueous electrolytes have been plagued by sluggish kinetics (often large required overpotentials) and, more importantly, poor faradaic efficiencies of less than 1% towards NH3 , with most protons and electrons instead going to2


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wards evolving H2 . Better selectivity towards NH3 can be obtained using molecular catalysts and enzymes, but these catalysts are fragile and deactivate after a small number of turnovers. 10–12 A strategy we will discuss extensively in this work that has achieved some success in both the homogeneous and heterogeneous catalytic literature is the use of non-aqueous electrolytes to hinder access to protons. 13

The results of representative NRR experimental studies 14–33 over heterogeneous electrocatalysts under ambient conditions that provide information about applied voltage, NRR current density, and NRR faradaic efficiency (listed in Table S1 of Section I of the Supporting Information) are summarized in Fig. 1, where the x-axis represents the current density to NH3 and the y-axis represents the energy cost for the production of an NH3 molecule. The energy cost, G, is estimated using Eq. 1, where n = 3 is the number of electrons transferred to make one NH3 molecule electrochemically, F is Faraday’s constant, Vcell is the total applied cell voltage, and η is the faradaic efficiency (See Section I of the Supporting Information).

G=

nFVcell η

(1)

For studies in which the anode voltage is ambiguous, a reasonable overpotential of 0.4 V is assumed. 34,35 It is important to note that small variations in Vcell are insignificant compared to the order-of-magnitude variations observed in η. Since small amounts of ammonia can be particularly difficult to measure, 33,36 the experimental points are classified by their level of control experiments, with

15

N2 (N15) isotopic labeling controls being the most de-

sirable. We include results that do not report any control experiments, but recommend that they be repeated with proper controls in the future. Fig. 1 also separates the literature results into aqueous and non-aqueous systems, a distinction that is particularly important in this work. A dashed line corresponding to the approximate energy costs of the industrial Haber-Bosch process (of the order 10 eV/molecule) is included, 6 and a green star is shown 3


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to represent where the field of electrochemical NH3 synthesis would like to go in the future, towards lower energy cost that can compete with Haber-Bosch and higher current density to minimize capital costs. The NRR current density also serves as an alternative measure of the reliability for the experiments, because higher concentrations of NH3 can be reached with higher production rates. We note that a produced NH3 concentration well in excess of 1 ppm to drown out known contamination sources, 37,38 in addition to appropriate control experiments, would likely be a superior metric for reliability, but most of the experimental papers up to this point have not specified this.

From Fig. 1, it is clear that there have been very few literature results to date that achieve both low energy cost and a high experimental reliability (large current densities and proper control experiments). In fact, there appears to be a trade off between the two metrics for some of the most promising results. Results obtained by Zhou et al. 22 and Suryanto et al. 23 in non-aqueous ionic liquid systems demonstrate high faradaic efficiency and therefore the lowest energy per NH3 , but these systems produce small total current densities. Work by Furuya and Yoshiba 16 shows a large NRR current density, but at a very low faradaic efficiency (large energy cost per NH3 ). We believe some of the most promising results so far to be those achieved by Hao et al., 30 Song et al., 14 and Tsuneto et al. 21 (recently reproduced by Lazouski et al. 32 and Andersen et al. 33 ), who are able to produce substantial amounts of NH3 , supported by adequate control experiments, while suppressing hydrogen evolution.

In this perspective, we discuss the role that theoretical analysis can play in designing an economically viable electrochemical ammonia synthesis process. In particular, we outline the theoretical basis for two potential avenues toward active and selective N2 reduction: (1) a catalyst-based strategy to circumvent the scaling relations between adsorbed surface intermediates and (2) the use of non-aqueous electrolytes to suppress hydrogen evolution, an idea that we think has been successfully implemented in some of the most promising

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[16]

[15]

104 Energy Cost [eV/NH3]

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[17]

[24]

[27]

103

[28]

102

[20]

[16] [16]

[26]

Li-PEBCD [19] Li3N [33] [25] [21] [18] VN [29]

Ti3C2Tx [31] Fe/FTO [22]

[16]

Li3N [32] CNS [14]

[23]

Bi-K [30]

Fe/SS [22]

101

Haber-Bosch 10

3

N15 Labeling

10 2 10 1 100 Current Density to NH3 [mA/cm2] Ar Blank

No Blank

Aqueous

101 Non-Aqueous

Figure 1: Experimental results from the literature classified on a logarithmic scale by their energy cost (y-axis, eV per NH3 molecule) and their current density to NH3 (x-axis, mA/cm2 ). The degree of filling of the symbols represents the level of control experiments, with filled symbols corresponding to isotopic labeling, half-filled symbols representing argon controls, and empty-symbols indicating no control experiments. Circles and triangles represent aqueous and non-aqueous systems, respectively. A dashed line indicating the approximate energy cost of the current industrial process is shown, 6 as is a green star representing desired future directions. All experimental results are labeled by reference number, while results that provide isotopic labeling controls are additionally labeled by the catalyst used. electrochemical NH3 synthesis results to date. 21–23,32

2. Circumventing the Scaling Relations Theoretical studies thus far have primarily characterized the activity and selectivity of various catalysts for electrochemical ammonia synthesis using limiting potential analysis (LPA).

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Examples of such studies on transition metal catalysts include those performed by Van der Ham et al., 39 Skúlason et al., 40 and Montoya et al. 41 There have also been theoretical studies on transition metal nitrides by Abghoui et al., 42–44 on transition oxides by Höskuldsson et al., 45 and on various 2D materials. 46–50 The first step of the LPA method is to perform Density Functional Theory (DFT) calculations that can be used to compute the adsorption energy of each intermediate involved in a reaction mechanism. The most favorable pathway for electrochemical ammonia synthesis is usually the associative mechanism, described by Eqs. 2-7.

∗ + N2 (g) + H+ + e− ↔ ∗N2 H

(2)

∗N2 H + H+ + e− ↔ ∗N2 H2

(3)

∗N2 H2 + H+ + e− ↔ ∗N + NH3 (g)

(4)

∗N + H+ + e− ↔ ∗NH

(5)

∗NH + H+ + e− ↔ ∗NH2

(6)

∗NH2 + H+ + e− ↔ ∗ + NH3 (g)

(7)

Since DFT is not well-suited for calculating the energy of a proton-electron pair, the

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computational hydrogen electrode (CHE) 51 is used to reference the energy of a protonelectron pair to H2 gas under an applied potential of 0 V vs. RHE. The energy of each step in the above mechanism therefore depends linearly on potential under the CHE scheme, and so the limiting potential, defined as the reducing potential at which all steps in the pathway are thermodynamically downhill, can be computed as the negative of the energy difference corresponding to the most uphill step. Variations in the limiting potential provide a good measure of trends in the catalytic activity of the oxygen reduction reaction (ORR) 51 and are therefore expected to do so for NRR as well. The DFT-calculated limiting potential for NRR (UL,N ) is plotted as a function of the binding free energy of *N (∆GN ) for various closepacked transition metal surfaces in Fig. 2A. A similar analysis can be performed to obtain the limiting potential for hydrogen evolution (UL,H ) as a function of ∆GN , using the HER mechanism shown in Eqs. 8-9, also plotted for the close-packed transition metal surfaces in Fig. 2A. The limiting potentials UL,N and UL,H are also shown for the transition metal nitride rock salt (100) surface vacancies in Fig. 2B. The limiting potential results shown in Fig. 2 are in qualitative agreement with past studies of transition metal and transition metal nitride surfaces for NRR and HER. 40–44 The DFT calculation methodology and tabulated energetics used in this analysis and throughout this work are provided in Sections II and III of the Supporting Information.

∗ + H+ + e− ↔ ∗H

(8)

∗H + H+ + e− ↔ ∗ + H2 (g)

(9)

It is important to note that because the binding energies of surface intermediates scale (approximately) linearly with each other, 40,41 the choice of ∆GN as the descriptor for NRR

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Limiting Potential (UL) [V vs. RHE]

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1.0 0.0

(A) Transition Metal (TM) Terraces Pt Ru Rh Ir Fe Co Ni WRe Mo Fe Mo Co Re W Ni Ru

0.8 0.5 0.6 1.0 0.4 1.5

Rh Ir

0.22.0 0.02.5 0.03

Pd

0.2 1

0

(B) TM Nitride Surface Vacancies Nb

Cu Zr

Mo Cr V

Ti Nb

Mo Cr

V

Ti Zr

Pd Pt

UL, HER UL, NRR 2

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10.4

Cu

Nitrogen Binding Energy ( GN) [eV]

2

3

0.6 2

1

0.80

1


1.0 2

Figure 2: NRR (black) and HER (red) limiting potential (UL ) as a function of Nitrogen binding energy (∆GN ) plotted for (A) close-packed transition metal terraces and (B) transition metal nitride rock salt (100) surface vacancies. The solid lines refer to the limiting potential volcano obtained using scaling relationships between adsorbed surface intermediates while each individual point is computed using the explicit DFT calculations for that surface. The results shown here qualitatively reproduce past studies of NRR and HER on transition metal and transition metal nitride surfaces. 40–44 and HER activity is an arbitrary one. We take advantage of the typical cancellation in DFT error that arises from the use of scaling relationships. As shown previously by Medford et al., 52 the error perpendicular to a scaling line is smaller than the error associated with an individual point, allowing us to differentiate between activities and selectivities for classes of materials more easily than between two materials of the same class. The following two assumptions are generally made to simplify the analysis: (1) a sufficiently reducing potential is applied such that electrochemical steps (such as the Heyrovsky step for H2 production, involving a proton-electron transfer) dominate over possible thermochemical steps (such as the Tafel step for surface recombination of *H adsorbates) 41,53 and (2) the effect of adsorbateadsorbate interactions is neglected, which is particularly reasonable because *H is often the most abundant adsorbate. 13

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The volcano curves shown in Fig. 2 demonstrate why NRR under aqueous conditions is so difficult on catalyst surfaces that have been explored thus far. For both the transition metal and transition metal nitride surfaces considered here, the limiting potential required for HER is consistently less negative than the limiting potential required for NRR. Since the rate of these electrochemical processes depends exponentially on the potential applied, even small differences in the limiting potential translate to orders of magnitude lost in selectivity. This result is consistent with numerous aqueous experimental 15–20,25–28 and computational 13,39–41 studies to date. The picture for the metals is slightly more promising than for the nitrides, however, because in the metal case the peaks of the NRR and HER volcano curves are separated considerably in ∆GN . The main implication of this is that a catalyst near the peak of the NRR volcano is a relatively poor (overly strong binding) HER catalyst. On the other hand, the NRR and HER peaks on the nitrides align with each other, leaving little hope for high NRR selectivity for these classes of materials.

In addition to demonstrating the challenges of electrochemical N2 reduction, Fig. 2 is also useful in designing improved catalysts. Fundamentally, LPA suggests that a more active and selective catalyst for NRR must break or circumvent at least one existing linear scaling relationship between surface-bound intermediates or transition-states. NRR activity for the transition metal catalysts (Fig. 2A), for example, is limited specifically by scaling between the binding free energy changes of *N2 H formation (∆GN2 H , Eq. 2) and *NH protonation (∆GNH2 − ∆GNH , Eq. 6). The transition metal nitrides (Fig. 2B) are also limited on the weak binding side by *N2 H formation, but are instead limited on the strong binding side by *N protonation (∆GNH − ∆GN , Eq. 5). Taking advantage of scaling between the binding free energies of *H and adsorbates binding through an N atom, the NRR vs. HER selectivity challenge can also be framed in terms of scaling. On the left leg, selectivity for NRR is limited by scaling between the free energy changes of the Heyrovsky step (−∆GH , Eq. 9) and *N (in the nitride case) or *NH (in the metal case) protonation. On the right

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leg, NRR selectivity is restricted by scaling between the binding free energies of the Volmer step (∆GH , Eq. 8) and *N2 H formation (∆GN2 H , Eq. 2).

Of the scaling relationships mentioned above, one limitation that we believe can be circumvented is the mechanism of N2 activation. The above model assumes an associative mechanism with a proton-electron transfer to N2 , forming *N2 H. An alternative pathway is the dissociative mechanism, where N2 adsorbs dissociatively before being hydrogenated, as shown in Eq. 10.

2 ∗ + N2 (g) ↔ 2∗N

(10)

When following the dissociative path described by Eq. 10, NRR would skip the associative electrochemical activation steps corresponding to Eqs. 2-4 and would instead proceed twice through the protonation steps described by Eqs. 5-7. Since Eq. 10 does not depend directly on applied potential, the concept of a limiting potential for NRR is no longer an acceptable metric for activity. We will instead use the largest activation barrier to denote activity of a given catalyst. One of the most important implications of this change is that our volcano curves for NRR now depend on the applied potential only in regions where electrochemical steps are limiting. For simplicity, we assume here that the charge transfer coefficient β = 0.5 and that the intrinsic barriers (at ∆G = 0) for the electrochemical steps are equal to 0.7 eV, indicating that they would occur at approximately 1 turnover per site per second at room temperature. This assumption for the intrinsic barriers is also implicitly made in LPA, when it is said that appreciable rates can be obtained once all steps are thermodynamically downhill. The choice of 0.7 eV as the intrinsic barrier for this analysis is also in agreement with electrochemical barriers for the N-NH proton-electron transfer step described in Eq. 5, calculated in the presence of an explicit water layer based on methods developed by Chan and Nørskov (See Section IV of the Supporting Information). 54,55 The assumption of a constant intrinsic barrier for all reaction steps relies on (1) the relative insensitivity of proton-electron 10


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transfer barriers to binding configuration (since the transition-state involves a proton from solution attacking a surface-bound intermediate rather than two surface-bound species) and (2) the fact that most steps in our reaction network involve the transfer of a proton to the same surface-bound atom (nitrogen). The explicit calculation of all proton-electron transfer barriers in future studies would further improve the accuracy of our approach. The choice of β = 0.5 is also consistent with our DFT calculations and with previous studies of the charge transfer coefficient for simple proton-electron transfer reactions. 56 For the thermochemical barrier (Eq. 10), we invoke previous theoretical studies of the Haber-Bosch process to obtain the N2 dissociation transition-state scaling relationship for transition metal surfaces. 57,58 We also consider the ideal scaling relationship, where the transition-state energy is equal to the energy of the dissociated state, as well as an intermediate scaling relationship. It has been suggested previously that active sites that explicitly force the on-top binding of *N can exhibit a scaling relationship approximately equal to the intermediate scaling considered here, 59 without significantly affecting other scaling relationships. The negative of the maximum activation barrier (-max(GA )) for NRR is plotted as a function of ∆GN for transition metal catalysts in Fig. 3 at an applied potential of -0.5 V vs. RHE, using (A) standard, (B) intermediate (same slope and lower intercept), and (C) ideal N2 dissociation scaling relationships. Hydrogen evolution at the same applied potential, which is independent of the N2 dissociation scaling relationship, is also included.

From Fig. 3A, we can see that for the standard transition metal catalysts, the simple analysis suggests that the purely electrochemical pathway dominates at all applied potentials. This goes some way toward validating our use of the LPA method for the transition metals. When a more favorable N-N transition-state scaling relationship is considered, however, the left leg of the NRR volcano curve is allowed to continue on further to more positive values of ∆GN because N2 activation is relatively easier. The higher observed peak leads to greater predicted activity because the maximum barrier is smaller and also greater selectivity

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(A)

EN

N

= 0.83 E2N + 2.17

-max(GA) [eV]

0.4

Full NRR Associative NRR HER U = -0.5 V vs. RHE

0.6 0.8 1.0 1.2 1.4 2

1

0

1

2

Nitrogen Binding Energy ( GN) [eV] (B)

EN

N

= 0.83 E2N + 0.85

(C)

0.4

0.4

0.6

0.6

-max(GA) [eV]

-max(GA) [eV]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.8 1.0 1.2 1.4

EN

N

= 1.0 E2N + 0.0

0.8 1.0 1.2 1.4

2

1

0

1

2

2


1

0

1

2


Figure 3: The negative of the maximum activation barrier (-max(GA )) at -0.5 V vs. RHE on close-packed transition metal surfaces for NRR considering both dissociative and associative N2 activation (solid black), NRR with only associative N2 activation (dashed cyan), and HER (solid red), using (A) standard, (B) intermediate (same slope and lower intercept compared to standard), and (C) ideal N2 dissociation scaling relationships. The N2 dissociation scaling used is listed above each plot, while the other scaling relationships used are the same for all three panels. because the NRR peak is closer to the HER peak. This is an example of how a new class of materials that circumvents a particular scaling relationship can drastically improve activity and selectivity for NRR. It is important to note that it is likely difficult to synthesize materials that exhibit improved transition-state scaling, and this represents an ongoing opportunity for experimental groups. We target the N-N transition-state scaling relationship in this work because of its sensitivity to catalyst structure and the binding coordination of adsorbed intermediates, 59 but in principle other scaling relationships in the reaction network (between intermediates or transition-states of the various reaction steps) could also be altered to improve NRR activity and/or selectivity. 12


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3. Beyond the Catalyst: Reducing Access to Protons In the previous section, we outline a catalyst-based strategy for active and selective N2 electroreduction to NH3 . In this section, we examine an orthogonal strategy that could substantially increase NRR selectivity while maintaining reasonable activity: the use of a non-aqueous electrolyte. Electrochemical fuel production on solid electrodes has historically been performed in aqueous environments because high rates of proton transfer are generally desired. Recent theoretical developments suggest, however, that restricting access to protons and electrons could yield a disproportionately higher selectivitity to NH3 at the expense of only a slight decrease in rate. 13 This tradeoff provides an intriguing path forward for N2 reduction, as we will show.

Since we expect variations in the proton donor concentration to affect the kinetics of N2 electroreduction beyond the thermodynamics, here we present a comprehensive kinetic model that predicts activity and selectivity for a wide range of proton availabilities (solvent choices) and applied potentials. For this section, we make only a small modification to the purely electrochemical reaction mechanisms for NRR and HER described by equations Eqs. 2-9: we choose to separate Eq. 2 into two steps, described by Eqs. 11-12.

∗ + N2 (g) ↔ ∗N2

(11)

∗N2 + H+ + e− ↔ ∗N2 H

(12)

Since every step considered is first order in the coverage of an adsorbed species, the microkinetic model is linear and can be solved analytically at steady state, such that the coverage of each surface bound species is time invariant (see Section V of the Supporting Information for the analytical solution). As above, we assume that β = 0.5 and that the 13


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intrinsic barriers (at ∆G = 0) for the electrochemical steps are 0.7 eV. To consider the effect of proton donor concentration, we adjust the prefactor for each electrochemical step such that the rate constant for a proton-electron transfer in a particular electrolyte, k+ , is given by Eq. 13, where kb is Boltzmann’s constant, T is the system temperature, h is Planck’s constant, c+ is the proton donor concentration in the electrolyte of interest, c0 is the proton donor concentration in a fully aqueous system, and ∆Ga,0 is the proton transfer free energy barrier in a fully aqueous system. k+ =

kb T h

c+ c0

∆Ga,0 exp − kb T

(13)

It is important to note that this particular effect of proton donor concentration on the prefactor cannot be overcome with voltage, as it represents the difficulty or entropic penalty of bringing proton donors to the surface via diffusion. We also note that for the purpose of this study, proton transfer in a fully aqueous system is assumed to proceed through the basic mechanism with water as the proton donor, which is known to be the case for aqueous systems at pH values above 5. 60 A key implication of the fact that water, rather than hydronium, is the assumed proton donor is that the forward rate constant for proton transfer can not be decreased by an increase in pH. However, the rate constant can be decreased by using a non-aqueous electrolyte to dilute the proton donor (water) concentration. Fig. 4 shows the activity and selectivity to NRR as a series of 2-D volcano plots (heatmaps), where the descriptors are chosen to be combinations of applied potential, ∆GN , and proton donor concentration relative to a fully aqueous system,

c+ . c0

Points corresponding to four hypothetical

sets of experimental conditions are shown to illustrate the importance of all three variables considered in the analysis.

The results in Fig. 4 show that selectivity is poor at high proton concentrations (such as in an aqueous system) on any catalyst. The most important implication of the microkinetic 14


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(A) 15

10

log10(rN [s 1]) 5

0

(B) 5

10

1.5 2.0

3.0

(i) 4

3

2

(iii) 1

0

1

15

10

log10(rN [s 1]) 5

1.0

0

(i)

2.5 3.0

10

8

6

log10 cc+0 (

4

3

6

Voltage [V vs. RHE]

2.0

(iv) 4

2

1

0

2

(iii) 1

0

1

2

Nitrogen Binding Energy ( GN) [eV] 5

log10 rNr+N rH (

4

1.0

1.5

2

(i)

2.5

10

(ii)

)

(ii)

(D) 5

3

2.0

3.0

2

(

4

1.5

Nitrogen Binding Energy ( GN) [eV] (C)

5

log10 rNr+N rH

1.0

(ii)

2.5

6

Voltage [V vs. RHE]

Voltage [V vs. RHE]

1.0

Voltage [V vs. RHE]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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)

2

1

0

(ii)

1.5 2.0

(i)

2.5 3.0

0

3

10

8

)

6

log10

(

c+ c0

(iv) 4

2

0

)

N , (B and D) heatmaps Figure 4: 2-D NRR activity, rN , (A and C) and selectivity, rNr+r H as a function of applied voltage, U, and nitrogen binding energy, ∆GN , (A and B) and as a function of applied voltage, U, and proton donor concentration relative to an aqueous system, cc+0 , (C and D). In panels A and B, cc+0 = 10−5 is held constant, while in panels C and D, ∆GN = -1.3 eV is held constant. Points are included corresponding to various possible experimental conditions: (i) ∆GN = -1.3 eV, U = -2.4 V vs. RHE, cc+0 = 10−5 , (ii) ∆GN = -1.3 eV, U = -1.3 V vs. RHE, cc+0 = 10−5 , (iii) ∆GN = 1.3 eV, U = -2.4 V vs. RHE, cc+0 = 10−5 , (iv) ∆GN = -1.3 eV, U = -2.4 V vs. RHE, cc+0 = 10−1 .

analysis is that the effect of proton concentration is largest at substantial reducing potentials on catalysts with a very strong binding energy. Mechanistically, this occurs because the mechanisms for NRR and HER become almost fully irreversible in this limit. The strong reducing potential tilts all electrochemical proton-electron transfer steps forward, making backward rates negligible, and the strong binding energy leads to an irreversible chemical 15


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N2 activation step as well. The physical picture in this limit is that all steps in the NRR and HER mechanisms move forward rapidly, as soon as N2 or protons arrive to the surface. Selectivity is therefore governed by the relative rates of N2 adsorption (zeroth order in proton concentration) and proton adsorption (first order in proton concentration). NRR selectivity increases as the rate of proton adsorption is reduced by a decrease in the proton concentration, as described in Eq. 13, while the N2 adsorption rate is relatively unaffected by changes in proton availability. In this regime, a non-aqueous electrolyte with proton activity that is a few orders of magnitude less than water can result in good selectivities at reasonable rates. This analysis is in qualitative agreement with a range of recent non-aqueous experimental results from the literature that appear in the more favorable (lower) half of Fig. 1. 21–23,32

The points labeled (i), (ii), (iii), and (iv) in Fig. 4 emphasize the required experimental conditions that lead to adequate activity and selectivity for NRR. Point (ii) uses a strong binding catalyst and a non-aqueous electrolyte, but the applied voltage is not reducing enough for the NRR mechanism to be in the irreversible limit. Point (iii) uses a non-aqueous electrolyte and a sufficiently negative applied voltage, but a weak binding catalyst that cannot activate N2 . Point (iv) uses a strong binding catalyst and a strong reducing potential, but has a proton donor concentration that is too high for NRR to compete with HER. Only point (i) represents the necessary experimental conditions for active and selectivity NRR: a strong binding catalyst, a large reducing applied potential, and a non-aqueous electrolyte with reduced proton donor activity. One result that we believe is particularly well modeled by this analysis is the work by Tsuneto et al., 21 (more recently verified by Lazouski et al. 32 and Andersen et al. 33 ) where lithium nitride plated on various transition metal electrodes is likely a strong binding catalyst, very reducing potentials are applied (cell potentials greater than 4 V), and a non-aqueous electrolyte (dilute ethanol in THF) is used.

The analysis here also serves to unify continuous N2 electroreduction in non-aqueous elec-

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trolytes with cyclic processes that physically or temporally separate the activation of N2 from the addition of protons. 61 This physical or temporal isolation is effectively a more extreme version of reducing the proton concentration. The main advantage of complete separation is that the selectivity can be brought up to nearly 100%, but the primary disadvantage is that the device becomes more complicated from an engineering standpoint as the process is not a direct, continuous path to ammonia production.

It is important to note, however, that while this analysis represents a first attempt at describing the impact of non-aqueous electrolytes on faradaic efficiency and current density for NRR, there remain phenomena that the analysis does not capture. Song et al., 14 for example, have achieved NRR faradaic efficiency and current density in an aqueous system comparable to some of the best non-aqueous results to date by taking advantage of the enhanced electric field at carbon nano-spikes (CNS). Hao et al. 30 find even greater activity and selectivity by promoting NRR with respect to HER using bismuth nanocrystals and potassium cations in water. Further theoretical studies alongside quantitative experimental verification are required to describe both of these impressive aqueous results. Additionally, the current analysis treats proton donor concentration as the only descriptor for proton activity, neglecting likely relevant parameters such as donor identity, the acid dissociation constant, and electrolyte conductivity, all of which could affect activation barriers and reaction rates beyond our simplified analysis. Another limitation of our analysis is that we do not consider how the use of various non-aqueous solvents impacts the energetics of adsorbed intermediates and solvated ions. We hope that this work will inspire in-depth theoretical studies of electrolyte effects in electrochemical N2 reduction that build upon this simple first attempt.

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4. Conclusions and Outlook Our view is that the most promising future improvements in electrochemical ammonia synthesis will fall into two categories: (1) identifying materials that exhibit more favorable scaling relationships between surface intermediates, and (2) reducing access to protons and electrons through solvent engineering. In this perspective, we describe how DFT calculations and microkinetic modeling can play an important role in understanding both approaches described above and identifying new systems worthy of experimental study. For new catalysts to give substantially improved activity and selectivity, they must follow a different set of scaling relationships.

Acknowledgement This work was supported by a research grant (9455) from VILLUM FONDEN. The authors acknowledge support from the U.S. Department of Energy Office of Basic Energy Sciences to the SUNCAT Center for Interface Science and Catalysis. B.A.R. was supported by the NSF GFRP, grant number DGE-1656518.

Supporting Information Available The Supporting Information contains tabulated data, computational and analytical methodology, and additional results.

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Strategies toward Selective Electrochemical Ammonia Synthesis

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