Article Cite This: Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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New Insights into Electrochemical Ammonia Oxidation on Pt(100) from First Principles Hemanth Somarajan Pillai and Hongliang Xin*
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Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States ABSTRACT: The electrocatalytic ammonia oxidation reaction (AOR) to molecular dinitrogen (N2) is an essential component within a sustainable nitrogen cycle. The state-of-the-art Pt nanocatalyst, preferably terminated with (100) facets, suffers from a large overpotential (>0.5 V) and rapid deactivation, the origin of which remains largely unexplained due to the intrinsic complexity of solid-electrolyte interfaces. Within the framework of grand-canonical density functional theory (GC-DFT), we show that on Pt(100) the dehydrogenation of *NH2 is the potential determining step and that the *OH species, thermodynamically stable at >0.5 V vs RHE while overlooked in previous studies, plays an important role in kinetics by preferential stabilization of *NH via hydrogen bonding. Attributed to such favorable adsorbate−adsorbate interactions, *NH2 dehydrogenation is thermoneutral at 0.5 V vs RHE forming *NH species that can then dimerize easily at the 4-fold hollow sites, capturing the experimentally observed onset potential. At high operating potentials (>0.63 V vs RHE) where the *NH dehydrogenation to *N becomes thermodynamically feasible, surface deactivation occurs. However, the dimerization of *N with *N or *NH is kinetically facile, which suggests that the adsorbed *N is only the precursor to poisoning species, e.g., *NO, on Pt(100). The mechanistic insights obtained in this study could be exploited in new strategies of designing active, selective, and robust electrocatalysts for ammonia oxidation.
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INTRODUCTION The electrocatalytic ammonia oxidation reaction (AOR) is a crucial step toward sustainable nitrogen transformations1 and has many industrial and engineering applications, such as electrochemical sensing of ammonia,2 wastewater remediation,3 and direct ammonia fuel cells.4,5 For energy generation and storage, ammonia is an excellent hydrogen carrier due to its high volumetric energy density, low carbon footprints, and easy handling and transportation. Although ammonia has serious compatibility issues in polymer electrolyte membrane fuel cells (PEMFCs) due to their low tolerance to ammonia (0.5 V) and rapid deactivation. It is difficult to design catalysts that effectively tackle these issues due to our limited understanding of the reaction mechanism at the solid-electrolyte interface. It is generally accepted that electrocatalytic oxidation of ammonia follows the Gerischer and Mauerer mechanism10 (see Scheme 1). Within this mechanism, the adsorption of ammonia is followed by dehydrogenation steps to create *NHx adsorbates (x = 2, 1, and 0) where OH− ions serve as proton acceptors. The *NHx (x = 2 and 1) species can potentially dimerize forming the N−N bond, which can then be dehydrogenated and desorb as molecular dinitrogen (N2), whereas nitrogen adatoms (*N) do © 2019 American Chemical Society
not dimerize and are instead considered as a surface poison. This mechanism has been under debate, partly because this reaction on metals, e.g., Pt, is highly structure sensitive.11−15 Experimental observations have shown that Pt(100) has a lower onset potential than (111) and (110) surfaces.13 This structure sensitivity was further probed by creating Pt nanoparticles with preferentially oriented (100) facets, which were shown to be more active than polycrystalline Pt.16 Density functional theory (DFT) calculations suggest that the 4-fold hollow sites on the (100) surface allow for better dimerization kinetics in comparison to the (111) surface which results in improved current densities and reduced overpotentials.17 To unravel key intermediates in the AOR, Matsui et al.18 used in situ attenuated total reflection infrared spectroscopy (ATRIR) to monitor the polycrystalline Pt electrode under ammonia electrooxidation conditions. An infrared peak associated with the NH2 wagging of the *N2H4 adsorbate was observed to reach a maximum value at 0.4 V vs RHE and was correlated to the N2 formation rate, suggesting that *NH2 is at least one of the N−N dimerization species. Additionally, Rosca et al. concluded that *NH and *NH2 are the likely adsorbates on Pt(111) and Pt(100), respectively, based on results from CV and in situ infrared spectroscopy.19 Conventional DFT (C-DFT) calculations within the computational hydrogen electrode (CHE) Received: Revised: Accepted: Published: 10819
March 16, 2019 May 31, 2019 June 3, 2019 June 3, 2019 DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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Industrial & Engineering Chemistry Research Scheme 1. Gerischer−Mauerer Mechanism10 for Electrooxidation of NH3 (aqueous) to N2
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model performed by Katsounaros et al.7 on Pt(100) showed that *NH is the key intermediate which can dimerize and form *HNNH. Skachov et al.14 used ab initio molecular dynamics to study AOR on Pt(100) and suggested a potential-dependent reaction pathway including various possible N−N bonding intermediates. At low operating potentials, *NH2 and *N species can create dimers independently, whereas at higher potentials (>0.5 V vs RHE) *N adatoms are the main dimerizing intermediate. Surface poisoning is an additional issue that has to be addressed in order to design better electrocatalysts. Cyclic voltammetry (CV) with the aid of chronoamperometric experiments showed that the reaction starts forming poisonous species above 0.6 V vs RHE.7,16,20 To the best of our knowledge, the identity of this poisoning species has not been confirmed on Pt(100). However, there are experimental results suggesting *N poisoning for other surfaces. Gerischer and Mauerer10 provided ex situ evidence for *N as a poison by running temperatureprogrammed desorption of a Pt electrode between 400 and 600 °C. Gas chromatography analysis showed that N2 was the desorbed product and therefore suggested that nitrogen adatoms were strongly adsorbed on the surface as a poison. Further evidence for nitrogen adatoms as a poison was provided by De Vooys et al.21 by performing surface-enhanced Raman spectroscopy (SERS) on polycrystalline gold (Au) and palladium (Pd) surfaces, where inactive nitrogen adatoms were observed. Activation barriers calculated via DFT on Pt(111) have supported the notion of nitrogen poisoning.14,22 However, DFT calculations on the Pt(100) surface show that the nitrogen dimerization is both kinetically and thermodynamically favorable.7,14,23 To resolve those controversial observations, we employed grand-canonical density functional theory (GC-DFT) to gain mechanistic insights into ammonia electrooxidation at the Pt(100)/electrolyte interface. We show that the electrode potential and adsorbate−adsorbate interactions have a significant effect in influencing stability of surface intermediates and are thus essential for capturing onset potentials from the experimental CV curves. Specifically, we show that the dehydrogenation of *NH2 to *NH is the potential-determining step which becomes thermoneutral at 0.5 V vs RHE due to a favorable interaction between coadsorbed *OH and *NH. The *NH species can then easily dimerize over the 4-fold hollow sites. At high operating potentials (>0.63 V vs RHE) where the *NH dehydrogenation to *N becomes thermodynamically feasible, surface deactivation has been observed. However, the dimerization of *N with *N or *NH is kinetically facile, which suggests that the adsorbed *N is only the precursor to poisoning species, e.g., *NO, on Pt(100).
COMPUTATIONAL METHODS Basics of DFT Calculations. Spin-polarized density functional theory (DFT) calculations were performed using Vienna Ab initio Simulation Package (VASP) with projector augmented wave pseudopotentials. A plane wave energy cutoff of 500 eV was used, and the exchange-correlation was approximated via the generalized gradient approximation (GGA). The RPBE functional was used due to the fact that it has been parametrized specifically for chemisorption systems.24 Pt(100) was chosen as the model system. A supercell of (2 × 2) with 5 layers was used with a lattice constant of 3.977 Å. Increasing the layer thickness from 5 to 7 layers changed the ammonia adsorption energy less than 0.05 eV. A vacuum of 32.5 Å was used, such that there was a vacuum of 65 Å between two images. The reason for such a large vacuum will be mentioned later. Mirror symmetry was enforced in systems by appending the adsorbates on both sides of the slab. For the surface and adsorbate structures, a Monkhorst−Pack mesh of 6 × 6 × 1 was used to sample the brillouin zone, while for molecules only the gamma point was used. Methfessel− Paxton smearing scheme was used with a smearing parameter of 0.1 eV for adsorbate systems, and 0.001 eV for molecules. Electronic energies are extrapolated to kBT = 0 eV. During geometry optimization, the center layer was fixed, while the other 4 layers and adsorbates were allowed to relax until forces are less than 0.03 eV/Å. For dimerization steps, the activation barriers were calculated by creating several images between initial and final states, and the appropriate coordinate (N−N distance) was constrained to optimize reaction pathways. Modeling Solid-Electrolyte Interfaces. Inclusion of solvation is a key aspect of simulating solid-electrolyte interfaces. Solvation can lead to stabilization of reaction intermediates which can affect the energetics of pathways while also potentially act as a mediator to lower activation barriers of key steps.25 Various approaches have been considered to model solvation such as ad-hoc solvation energy correction, implicit solvation, explicit solvation via molecular dynamic simulations, and hybrid solvation. In this study, implicit solvation is employed through the VASPsol26,27 package which uses a continuum dielectric description for the solvent. Work by Zhang et al.28 has shown that implicit solvation captures free solvation energies well for *NH2 and *NH on the nonelectrified Pt(111) surface. The electrolyte is included by self-consistently solving the linearized Poisson−Boltzmann (LPB) equation. At room temperature, the Debye screening length (Å) can be calculated from the 3 electrolyte concentration (M) via κ = I .29 Thus, a Debye screening length of 3 Å was used to model an electrolyte concentration of 1 M. Work by Steinmann et al.29 has shown that for symmetric systems a minimum vacuum distance of 10 × Debye length is required to accurately describe the electrolyte distribution; thus, a minimum distance of 30 Å was required in 10820
DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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Industrial & Engineering Chemistry Research our simulations. The dielectric constant was set to 78.4 to model the aqueous solution at 298.15 K. The surface tension parameter (tau) was set to be 0 in order to get rid of electrostatic potential fluctuations in the vacuum,30 and precision was set to “Accurate”. To model an applied bias at electrochemical interfaces, Neurock et al.31 developed the double reference method in which the slab is charged and a background counter charge exists in order to maintain cell neutrality. Chan et al.32,33 developed an extrapolation scheme whereby cells of increasing size are used, and the energy changes are then extrapolated to an “infinite” cell size to mimic constant potential conditions. Akhade et al.34 proposed a computationally efficient scheme to calculate kinetic barriers as a function of potentials through the use of Marcus theory and the symmetry factor. In this study, we used a constant-potential model developed by Goodpaster et al.35 within the general framework of GC-DFT.36 In GC-DFT, the Kohn−Sham orbitals are directly optimized in order to minimize the grand free energy. However, in the approach used here the number of electrons is adjusted gradually to achieve a specified potential with gradient descent optimization algorithms. Since this approach is still within the GC-DFT framework, we will refer to it as GC-DFT for simplicity; however, it is important to recognize the differences. This approach has been used to study electrocatalytic reactions and shown to reproduce well the experimental results.37,38 All potentials will be with reference to the reversible hydrogen electrode (RHE) unless noted otherwise. The principal idea of the model is that the electrode potential with reference to the standard hydrogen electrode (SHE) can be related to the work function (Φ) of the system and the SHE (4.43 eV)35,39 as shown in eq 1.35,36,40 USHE =
Φ − 4.43 e
Figure 1. xy-plane averaged electrostatic potential and electrolyte density for *NH3 on Pt(100) at 0.6 V vs RHE.
Thermodynamics under Constant Potential. Under constant potential conditions, the electrochemical potential is a fixed variable but the number of electrons in the system can change. Such a system corresponds to a grand-canonical system and the Gibbs free energy is calculated via eq 2.35,36,42 G = E DFT + ZPE − TS − μe × Ne − μcat/ani × |Ne|
(2)
where EDFT is the DFT energy for the system from the constant potential calculations, ZPE is the zero-point energy, and T and S are the temperature and entropy, respectively. ZPE and entropy were calculated by treating all adsorbate degrees of freedom as vibrational within the harmonic oscillator approximation (>50 cm−1). The vibrational frequency calculations were performed on the uncharged system with implicit solvation. μe is the chemical potential of the electron which is also the Fermi level the simulation is set at in order to achieve the required potential. In order to achieve the required potential, Ne is the number of electrons added (positive in sign) or removed (negative in sign) relative to the total number of electrons in the neutral system based on pseudopotential. The last term is the energy associated with the inclusion of cations/anions. μcat/ani is the chemical potential of the cation/anion and is calculated as
(1)
The work function is related to the Fermi level with vacuum potential at zero (Ef = −Φ); therefore, by tuning the Fermi level of the system relative to the vacuum potential, the electrode potential can be controlled within the simulations. Since the Fermi level can be adjusted by changing the number of electrons in the system, controlling the electrode potential comes down to changing the number of electrons. The number of electrons was tuned via Newton’s method, such that the absolute error between the required potential and that calculated via eq 1 was 0.63 V vs RHE) where the *NH dehydrogenation to *N becomes thermodynamically feasible, surface deactivation occurs. However, the dimerization of *N with *N or *NH is kinetically facile, which suggests that *N is only the precursor to poisoning species on Pt(100). The results presented here have important implications in designing electrocatalysts for ammonia oxidation reaction. Designing catalysts with improved *OH adsorption 10825
DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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Industrial & Engineering Chemistry Research can possibly lead to a stabilization of *NH species at reduced onset potentials. This can prevent the formation of *N species, not a poison but a precursor to poisoning, on Pt(100). Better kinetics can be achieved by realizing that *NH dimerization is kinetically hindered at low operating potentials; thus, lowering the activation barrier of this step by engineering the structure and composition of active sites could be utilized to increase current densities.
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amazed by quantum mechanics and its potential for fundamental understanding of surface chemistry and decided to come to United States to learn more about it. He received his Ph.D. in Chemical Engineering under the guidance of Prof. Suljo Linic from the University of Michigan in 2011. His doctoral research focused on understanding adsorbate−adsorbate interactions on metal surfaces. Prior to joining the Virginia Tech in 2014, he was a postdoc with Prof. Jens Nørskov at SLAC/Stanford University for extending the d-band theory. At Virginia Tech, Dr. Xin won the Dean’s award for Outstanding New Assistant Professor (2018) and Engineering Faculty Fellow (2019). He is also the recipient of the prestigious NSF CAREER Award (2019). His expertise is the development/application of electronic structure theory, machine learning, and kinetic modeling tools to problems in fundamental surface science and catalysis. He has authored or coauthored publications in journals including Physical Review Letters, Nature Chemistry, Nature Materials, Science, and so on.
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Hongliang Xin: 0000-0001-9344-1697 Notes
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The authors declare no competing financial interest. Biographies
ACKNOWLEDGMENTS H.P. and H.X. gratefully acknowledge the financial support of the NSF CBET Catalysis program (CBET-1604984) and partial support of the NSF CAREER program (CBET-1845531). The computational resource used in this work is provided by the advanced research computing at Virginia Polytechnic Institute and State University.
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Hemanth Pillai received his undergraduate degree in chemical engineering from Syracuse University in 2013; following this, he joined the department of chemical engineering at Virginia Polytechnic Institute and State University as a Ph.D. student in Prof. Hongliang Xin’s lab. His research is focused on studying reactions at the electrochemical interface via computational methods including density functional theory and machine learning, and is specifically focused on understanding the ammonia electrooxidation reaction in order to find design principles to aid catalyst design.
Hongliang Xin is an Assistant Professor of Chemical Engineering at Virginia Polytechnic Institute and State University. He received a B.S. degree in chemical engineering from Tianjin University in 2002 where he won the prestigious Rongzhijian fellowship and Sinopec fellowship. After that, he went to Tsinghua University and graduated with a M.S. degree in chemical engineering in 2005. At that time, he was truly 10826
DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
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DOI: 10.1021/acs.iecr.9b01471 Ind. Eng. Chem. Res. 2019, 58, 10819−10828
