First-Principles Design of Graphene-Based Active Catalysts for

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First-Principles Design of Graphene-Based Active Catalysts for Oxygen Reduction and Evolution Reactions in the Aprotic Li–O Battery 2

Joonhee Kang, Jong-Sung Yu, and Byungchan Han J. Phys. Chem. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.jpclett.6b01071 • Publication Date (Web): 08 Jul 2016 Downloaded from http://pubs.acs.org on July 10, 2016

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The Journal of Physical Chemistry Letters

Figure 1. Model systems for Gr/Cu and N-Gr/Cu, where Cu(111) surface. Brown, silver, blue mean carbon, nitrogen, and copper, respectively. Figure 1 229x190mm (96 x 96 DPI)

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Figure 2. Free energy diagram of the N-Gr, Gr/Cu, N-Gr/Cu systems. The free energies are shown at different potentials, U = 0 V is the open circuit potential, U0 is equilibrium potential, UDC (emerald line) is the maximum potential where discharge is still downhill in all steps, and UC (pink line) is the minimum potential where charging is all downhill. Insets show the optimized configurations with different substrates. Red and green balls denote the O and Li atoms. Figure 2 559x1000mm (96 x 96 DPI)


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Figure 3. Charge density distribution of the adsorbed Li–O intermediates on the (a) N-Gr, (b) Gr/Cu, and (c) N-Gr/Cu using Bader charge analysis. The green and blue colors denote the negative charge accumulation and depletion, respectively. Figure 3 490x110mm (96 x 96 DPI)



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Figure 4. Calculated density of states for (a) Gr and N-Gr, (b) Gr and Gr/Cu. Fermi level of N-Gr and Gr/Cu move upward from the Gr keeping the shape of conical point. Figure 4 250x210mm (96 x 96 DPI)


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Figure 5. The overpotentials of graphene based catalysts ORR/OER as a function of calculated (a) LiO2, (b) Li2O2 adsorption energies. The green and pink dashed lines are theoretical predictions of ORR and OER overpotentials, based on a simple Sabatier’s principle. Figure 5 271x381mm (96 x 96 DPI)



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First-Principles Design of Graphene-based Active Catalysts for Oxygen Reduction and Evolution Reactions in the Aprotic Li–O2 Battery

Joonhee Kang1, Jong-Sung Yu1,*, and Byungchan Han2,*

1

Department of Energy Systems Engineering, DGIST, Daegu 42988, Republic of Korea

2

Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722,

Republic of Korea

1


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ABSTRACT Using first principles density functional theory (DFT) calculations we demonstrate that catalytic activities towards oxygen reduction and evolution reactions (ORR and OER) in a Li–O2 battery can be substantially improved with graphene-based materials. We accomplish the goal by calculating free energy diagrams for the redox reactions of oxygen to identify a rate-determining step controlling the overpotentials. We unveil the catalytic performance is well described by the adsorption energies of the intermediates LiO2 and Li2O2 and propose that graphene-based materials can be substantially optimized through either by N doping or encapsulating Cu(111) single crystal. Furthermore, our systematic approach with DFT calculations applied to design of optimum catalysts enables to screen promising candidates for the oxygen electrochemistry leading to considerable improvement of efficiency of a range of renewable energy devices.

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TOC

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There is a general understanding that we were faced with serious environmental degradation and energy sustainability issues in our century, which largely originate from harvesting conventional fossil fuels. Extensive efforts with research and development over the last decade deployed several renewable energy conversion and storage devices and some of them to the level of commercialization. For example, a Li–ion battery system plays a central role in powering various locomotive devices such as hybrid or complete electric vehicles. Recently, Li–ion batteries were further developed to enhance energy density and prolong cycle life, and improve safety for longer operation. Nevertheless, it is expected that quite a long time framework is necessary to fully achieve the goals. A Li–O2 rechargeable battery has been focused on as another electric power system, due to its much higher energy density even comparable to gasoline fuels.1-2 Unlike the conventional Li–ion battery where Li ions repeatedly intercalate into electrode, the Li–O2 battery utilizes electrochemical reactions to form or decompose lithium oxide (Li2O) or peroxide (Li2O2) at cathode interfaced with an aprotic electrolyte. The overall reactions are described as Equation (1) and (2).3-5

2 Li  1/ 2O2  Li2O

(1)

2Li  O2  Li2O2

(2)

It is clear that the redox reaction of oxygen gas (oxygen reduction reaction – ORR and oxygen evolution reaction – OER) plays a key role in determining the system efficiency as previously reported.6-8 It is, however, notoriously well known that ORR and OER are very sluggish over ranges of electrochemical devices including Li–O2 battery and fuel cells leaving development of efficient electrocatalysts challenging. Unfortunately, there are a few highly functional 4



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electrocatalysts for the ORR/OER available, which mostly consist of expensive transition metals.913

For a wide commercialization of Li–O2 batteries low material cost, high ORR/OER catalytic

activity, and (electro)chemical stability are critical. It is of interest that cheaper materials based on N-doped carbons (i.e., graphene nanosheets, carbon nanotubes etc.) were reported to often demonstrate better catalytic activity towards the ORR and OER in Li–O2 battery systems than the previous metallic catalysts.4, 14-20 Furthermore, Noh et al.2122

computationally and experimentally demonstrated that non-precious metals (Cu, Fe, Co or the

alloys) encapsulated by N-doped carbon shells could be promising ORR catalysts in proton exchange membrane (PEM) fuel cells showing high activity and durability. The reason was ascribed to rich active sites for ORR and heterogeneous charge distribution at the hybrid interfaces between the core metal and carbon layers. The complexity of ORR and OER involving multielectron transfer, however, renders the clear mechanistic understanding challenging. In this paper, applying first principles density functional theory (DFT) calculations we, for the first time, evaluated catalytic activity of graphene nanosheet (Gr) supported by Cu(111) surface (Gr/Cu) toward ORR/OER for Li–O2 battery system operating with an aprotic electrolyte. Conventionally, control factors of ORR/OER catalytic activity of a graphene-based catalyst have been disputed largely in two aspects: either adsorption energy of O2 and Li23-24 or LiO2 in the catalyst surfaces5, 25. Using DFT computing we characterized atomic interaction energies among the chemical elements and compounds and then, mapped the interaction energies into thermodynamic free energy diagrams to identify mechanisms of the ORR/OER in the catalysts. These two-step approach enables to identify key descriptor and rate-determining step of the catalytic reactions. Furthermore, the method can lead to proposal of promising candidates if combined with high throughput computational screening of materials and guide design concept 5


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optimizing the properties. To achieve the goal we rigorously analyzed effect of a nitrogen (N) doping to graphene on the catalytic activity and rate-determining step to identify a key descriptor on the ORR/OER catalysis as a function of electrode potential. We used first principles DFT calculations as implemented in Vienna ab-initio simulation package (VASP)26 with the spin-polarized Perdew-Burke-Ernzerhof (PBE) generalized-gradient approximation (GGA)27 exchange-correlation functionals and the projector-augmented wave (PAW)28 approach. Van der Waals (vdW) interactions were considered using the DFT-D2 method of Grimme.29 We use the gamma-centered 3 × 3 × 1 k-point scheme to integrate Brillouin zone. The energy cutoff for plane-waves was set to 520 eV and calculations were continued until total energy difference was converged within 10-5 eV. Because of notorious errors by the GGA functional for calculating binding energy of O2 molecule we evaluated the energy using experimentally measured formation energy of a Li2O2 (-5.92 eV per formula unit).30 We did not include the correction of zero-point energy according to previous suggestion.8 We setup a slab model for Gr with a honeycomb symmetry and overlaid on Cu(111) surface (Gr/Cu) to simulate encapsulated catalyst with hybrid materials as shown in Figure 1a. Doping level of N was fixed to approximately 3.1 at.%. The lattice constant of Cu metal was calculated as 3.627 Å being in reasonable agreement with experimental measurement of 3.615 Å .31 The Cu surface was composed of five layers stacked in the direction of (111) plane of face centered cubic (fcc) crystal. Our model system was designed with 4 × 4 supercell of Cu(111) surface (a = b = 10.258 Å) with 12 Å vacuum space to preclude interactions among the images. Our calculations showed that there are lattice mismatch of 4.23 % between Gr and Cu(111) surface, implying that C–C bond are elongated about 0.06 Å from its equilibrium distance in pure graphene as previous work reported.32 During computations Cu atoms at the bottommost two layers were fixed to their 6



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bulk positions but others including all atoms in graphene layer and adsorbates were fully relaxed. The top view of the Gr/Cu with and without doped N were illustrated in Figure 1b and 1c. To setup model systems for LiO2, Li2O2 or (Li2O2)2 adsorption we notified that thermodynamically stable structures of LiO2, Li2O2 or (Li2O2)2 adsorption were already studied in previous reports and they were utilized as our model systems.5, 24 Lu et al.33 reported that reaction intermediates in the ORR are sensitive to the nature of a catalyst in dissociating O2, of which activation energy is lower than binding energies with both Gr and NGr34. Furthermore, computational results5,

23

and experimental observation4,

14

proposed that

nanoclusters of (Li2O2)n are more easily form than other kinds of chemical species in Gr and N-Gr surfaces. Based on these previous studies we assumed following four-electron mechanisms in ORR, via series of three elementary steps: 2O2  4  Li   e   O2  LiO2 * 3  Li   e 

(3)

O2  LiO2 * 3  Li   e   O2  Li2O2 * 2  Li   e 

(4)

(

)

O2 + Li2O2 *+2 Li + + e - « (Li2O2 )2 *

(5)

where the label “*” denotes adsorbate in catalyst surface. We assumed the reverse reactions can well describe OER mechanism in accordance with previous reports.35-36 To identify a rate-determining step we calculated thermodynamic free energy diagrams for the ORR/OER in the Gr, N-Gr, Gr/Cu, N-Gr/Cu, and clean Cu(111) surfaces as a function of electrode potential (U). Thermodynamic potentials for charging and discharging processes were deduced by calculating free energies of all intermediates shown in the elementary reaction steps. We assumed Li+ + e– are in electrochemical equilibrium at U = 0 V with a bulk Li metal. We assumed the 7


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electrochemical potential of an electron shift by –eU when electrode potential U sets in according to Nernst equation ( U  

G ), where G is the change of Gibbs free energy, n is the number of ne

electrons involved with the electrochemical reaction and the e is elementary charge. We defined overpotential for discharging (charging) process as the maximum (minimum) potential to shift the free energies of all intermediates of ORR/OER downhill. UC, UDC, and U0 denote a minimum electrode potential for charging, the maximum potential for discharging reaction and the equilibrium potential in Li–O2 battery, respectively in Figure 2. Our results indicate that free energy diagrams of ORRs in N-Gr, Gr/Cu, and N-Gr/Cu (from the left to the right in Figure 2) are all downhill at U = 0 V. In the discharging process, free energies stay downhill until the electrode potentials (UDC) reach 1.57 V, 1.94 V, and 1.59 V in N-Gr, Gr/Cu, and N-Gr/Cu, respectively. Hence, we identified each overpotential of the catalysts toward ORR in discharging process (ηORR = U0 - UDC) as 0.46 V (N-Gr), 0.11 V (Gr/Cu), and 0.91 V (N-Gr/Cu), respectively, as represented in Table 1. The Gr/Cu showed the lowest overpotential for ORR among the materials. It can be ascribed to the rate-determining steps in the discharging process. At high potential the reaction steps (3) and (4) become uphill in free energies for all of the catalysts implying that both LiO2 and Li2O2 adsorptions should be rate-determining steps in ORR. The OER proceeds in the reverse direction of the free energy diagram for the ORR shown in Figure 2. It shows that free energy diagrams become downhill as the charging potential (UC) decreases to 2.23 V, 2.31 V, and 4.00 V for N-Gr, Gr/Cu, and N-Gr/Cu, respectively. Therefore, we characterized overpotentials for the OER (ηOER) as 0.20 V (N-Gr), 0.26 V (Gr/Cu), and 1.50 V (N-Gr/Cu). The N-Gr catalyst shows the lowest overpotential toward the OER as similarly in the ORR, due to the rate-determining steps: the decomposition of LiO2 and Li2O2. 8



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Our results strongly propose that the overpotentials for ORR and OER are governed by the same rate-determining steps associated with the adsorptions of LiO2 and Li2O2 in the catalyst. Consequently, it is of importance to optimize catalyst structures to control the adsorption energies of LiO2 and Li2O2 in accordance with the Sabatier’s principle.37 To accomplish the goal, we defined the adsorption energy as Equation (6),

Eads    Eintermediates / substrate  Eintermediates  Esubstrate 

(6)

where Eintermediates / substrate , Eintermediates , Esubstrate mean total energies of intermediates plus substrate, intermediates molecule, and the substrate only, respectively. Using DFT calculations we identified thermodynamically stable structures as depicted in Figure 2. It clearly shows that both doping N atom and supporting with Cu(111) surface enhance the adsorption energies of all intermediates in ORR/OER. Bader charge analysis38 illustrates that electrons in N-Gr and N-Gr/Cu transfer from C to N (electronegativity C = 2.55, N = 3.04 according to Pauling scale39) enhancing the binding energies of ORR/OER intermediates, as depicted in Figures 3a and 3c. Additionally, the density of states (DOS) of the electrons in Gr and N-Gr (Figure 4a) denote that the Fermi energy in N-Gr upshifted leading to an n-type doping to a pure Gr. On the contrary, there is smaller amounts of electron transfer (0.0176e) in the Gr/Cu surface from Cu support to Gr (Figure 3b) as similarly to the report by Khomyakov et al.40 The electron DOS in a pure graphene is too small (compared to that in a Cu surface) to change the Fermi level (Figure 4b). Our results also indicate that Cu strongly attracts O2 due to its high enthalpy of oxide formation41 (CuO = –157 and Cu2O = –169 kJmol-1). Thus, Cu support enables for a graphene to adsorb LiO2 and Li2O2 more strongly than without the substrate. Figure 5 illustrates overpotentials in the ORR and OER for the different catalysts as function of 9


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adsorption energies of LiO2 and Li2O2. Surprisingly, overpotentials for ORR and OER in the five catalysts exhibit “V”-shaped curves. The overpotentials in ORR decreases from Gr to Gr/Cu, and it is minimal at the Gr/Cu catalyst as the adsorption energies increase. Further increases in the adsorption energies in Gr/Cu result in upshifting overpotential in ORR compared to the rock bottom (Gr/Cu), leading to the “V”-type correlation. Since the OER is desorption process the bottommost point is located at slightly lower adsorption energy side than that of ORR. The “V” curve clearly indicates that overpotentials towards ORR and OER are governed by the same descriptor of the adsorption energies of LiO2 and Li2O2 in the catalysts. The effect would appear most strongly in the initial stage of nucleation and growth of Li2O2.1 Our results highly recommend that Gr/Cu can be a promising catalyst toward ORR and OER in Li–O2 batteries. Previously4 it was shown that Li2O2 nanoparticles distributed more uniformly with smaller sizes in the N-Gr than in a pure Gr. The larger the size of Li2O2 is generated, the poorer the catalytic performance is induced due to reduced contacting areas with the catalyst and low electric conductivity of Li2O2. The observation suggests that lower (zero-, one-, and two-) dimensional NGr materials can provide abundant nucleation sites and thus, promote the catalytic activity. Onedimensional N-doped carbon nanotube (N-CNT) were, indeed, known to show high electrocatalytic activity in Li–O2 battery.14 Our DFT calculations strongly propose that Gr/Cu or Cu encapsulated by Gr sheet can be better for higher ORR and OER than N-Gr and N-CNT. Underlying origins are the adsorption energies of LiO2 and Li2O2, and the electronic structure optimization for the catalysts in Li–O2 batteries. Even though catalytic activity of Cu encapsulated by graphene layers toward ORR and OER was predicted, electrochemical stability of the catalyst under harsh condition should be confirmed to

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evaluate overall performance. For example, defects in the graphene, such as vacancies or grain boundaries can be nucleation site for oxide formation, which may degrade the catalytic performance eventually. Our paper more focused on how to screen and develop best non precious catalyst for ORR and OER in terms of the activity. Equally of importance task is to confirm electrochemical stability under such harsh conditions using in-situ experimental tools or rigorous computational methods. In summary, we demonstrated that the adsorption energies of LiO2 and Li2O2 play key roles in the catalysis of ORR and OER in Li–O2 batteries. We demonstrated the catalytic activity in the Gr-based materials can be optimized via N atomic doping and employing overlaid Cu(111) surface. It was shown that Cu support not only facilitates chemical interaction between O2 and Li, and Gr surface, but also induces favorable electronic charge distribution at the interface with Gr. Our calculated free energy diagrams for ORR and OER in Li–O2 battery in an aprotic electrolyte as a function of electrode potential led to unveiling the fundamental mechanisms for the rate determining steps and proposed promising candidate catalysts. Our study can help for an optimal design of non-precious catalysts for ORR and OER over a wide range of electrochemical energy devices.

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AUTHOR INFORMATION Corresponding Author * [email protected] * [email protected]

ACKNOWLEDGEMENTS This research was supported by Global Frontier Program through the Global Frontier Hybrid Interface Materials (GFHIM) (2013M3A6B1078882) and through Centre for Multiscale Energy System (NRF-2011-0031571) of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning.

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Figure 1. Model systems for Gr/Cu and N-Gr/Cu, where Cu(111) surface. Brown, silver, blue mean carbon, nitrogen, and copper, respectively.

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Figure 2. Free energy diagram of the N-Gr, Gr/Cu, N-Gr/Cu systems. The free energies are shown at different potentials, U = 0 V is the open circuit potential, U0 is equilibrium potential, UDC (emerald line) is the maximum potential where discharge is still downhill in all steps, and UC (pink line) is the minimum potential where charging is all downhill. Insets show the optimized configurations with different substrates. Red and green balls denote the O and Li atoms.

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Figure 3. Charge density distribution of the adsorbed Li–O intermediates on the (a) N-Gr, (b) Gr/Cu, and (c) N-Gr/Cu using Bader charge analysis. The green and blue colors denote the negative charge accumulation and depletion, respectively.

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Figure 4. Calculated density of states for (a) Gr and N-Gr, (b) Gr and Gr/Cu. Fermi level of N-Gr and Gr/Cu move upward from the Gr keeping the shape of conical point.

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Figure 5. The overpotentials of graphene based catalysts ORR/OER as a function of calculated (a) LiO2, (b) Li2O2 adsorption energies. The green and pink dashed lines are theoretical predictions of ORR and OER overpotentials, based on a simple Sabatier’s principle.

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Table 1. The calculated overpotentials of ORR/OER and adsorption energies of Li–O intermediates with graphene based catalysts and Cu(111). ηORR

ηOER

LiO2 (eV)

Li2O2 (eV)

(Li2O2)2 (eV)

Gr

0.57

0.29

0.86

1.14

1.34

N-Gr

0.46

0.20

1.42

2.20

3.12

Gr/Cu

0.11

0.26

2.15

2.85

3.19

N-Gr/Cu

0.91

1.50

3.84

4.14

5.00

Cu

1.91

1.90

5.17

5.40

8.73

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