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Kinetics, Catalysis, and Reaction Engineering

Activity Trends for Catalytic CO and NO Co-Oxidation at Low Temperature Diesel Emission Conditions Yuying Song, and Lars C. Grabow Ind. Eng. Chem. Res., Just Accepted Manuscript • Publication Date (Web): 28 Aug 2018 Downloaded from http://pubs.acs.org on August 28, 2018

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Industrial & Engineering Chemistry Research

Activity Trends for Catalytic CO and NO CoOxidation at Low Temperature Diesel Emission Conditions Yuying Song, Lars C. Grabow* Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX 77204, USA *[email protected]

ACS Paragon Plus Environment

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Industrial & Engineering Chemistry Research 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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ABSTRACT

The diesel oxidation catalyst (DOC) is an essential component of modern vehicle emissions control systems. The pervasive challenge for low temperature oxidation of engine exhaust gas is the mutual inhibition between the various pollutants, causing a marked increase in light-off temperature. Using a combination of density functional theory and descriptor-based microkinetic modeling we have screened catalysts for low temperature co-oxidation of CO and NO with specific emphasis on minimizing inhibition effects. Compared to standard Pt-Pd alloys, we find that coinage metal alloys, i.e., Cu, Ag, Au with at least one oxophilic constituent should possess more robust low temperature activity with minimal inhibition. We attribute this remarkable performance to high surface concentrations of oxygen due to the oxophilic component and less competitive adsorption between CO and NO to the exposed coinage metal sites. We believe that these fundamental insights provide valuable design principles for improved low temperature oxidation catalysts.

KEYWORDS: diesel oxidation catalyst, computational screening, density functional theory, NO oxidation, CO oxidation


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1. INTRODUCTION Diesel engine emissions are main contributors to environmental pollution and reduction in air quality. The diesel tailpipe exhaust contains CO, hydrocarbons (HC), NOx and particulate matter (PM)1, all of which have an adverse effect on the environment. Newly developed low temperature combustion (LTC) engines are more fuel efficient and can effectively reduce the emission of NOx and PM.2 The production of CO and HC, however, increases, and the higher LTC engine efficiency translates into a lower operating temperature for the diesel oxidation catalyst (DOC), causing a reduction of oxidation rates and insufficient DOC performance.3,4 The combination of lower temperature and higher pollutant concentration poses a major challenge for the currently commercialized Pt-Pd alloy oxidation catalysts.5 To still achieve Environmental Protection Agency emission regulations, additional fuel is often burned in the LTC engine to raise the inlet gas temperature of the DOC, which in turn compromises fuel efficiency. To this end, the development of a DOC with improved lower temperature activity is paramount to take full advantage of the fuel economy benefits offered by modern LTC engines. Besides, Pt-Pd alloys are insufficient to oxidize CO and NO simultaneously, since CO binds to active sites more strongly and inhibits the adsorption of NO.6–8 Therefore, developing a catalyst with reduced inhibition effect can improve the DOC efficiency. To identify metal alloys with promising catalytic properties from thousands of possible binary, or even ternary, combinations, we follow a computational screening approach to bypass costly experimental high-throughput testing.9 This approach is rooted in the Sabatier principle and relies on the existence of a small set of catalytic activity descriptors, which may be obtained from density functional theory (DFT) simulations.10,11 The approach has been successfully used


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in the past, e.g., for the discovery of novel metal alloy catalysts for the methanation reaction (CO + 3H2 → CH4+H2O).10 Herein, we augment available literature results to develop a computational screening framework for metal alloy catalysts with CO and NO co-oxidation activity at reduced temperature and minimal inhibition effects. As prototype reaction in heterogeneous catalysis, the CO oxidation reaction has been widely studied,12–16 and activity trend predictions are published for different temperatures,17 various surface facets,18 and even coverage effects have been addressed.19 Detailed mechanistic investigations of NO oxidation have suggested that both, Langmuir-Hinshelwood and Eley-Rideal mechanisms can describe the reaction kinetics,20–24 but better agreement with observed reaction behavior is obtained for Langmuir-Hinshelwood kinetics.22,25 Carefully conducted DFT studies by Getman et al. may offer an explanation for such disparate mechanistic conclusions, showing that lateral interactions at increased surface coverage do not only destabilize surface intermediate or transition states, but they may also lead to changes in the preferred reaction mechanism.26 Similarly, Frey et al. used grand canonical Monte Carlo simulations to study the influence of lateral interactions in O2 dissociation during NO oxidation on transition metals.27 When accounting for these interactions, the rates differ substantially from those values calculated in the absence of interactions, and most metals become active. Grabow et al. have found a similar effect for the CO oxidation reaction.19 While adsorbate-adsorbate interactions are known to affect the absolute reaction rate, literature studies suggest that the activity trend across transition metals is by and large unaffected.19,28 Since our focus here is on catalytic trends, we will therefore not include the effect of lateral adsorbateadsorbate interactions.


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While the NO oxidation reaction has attracted widespread attention, we are not aware of a published descriptor-based activity trend study as they exist for CO oxidation. To fill this gap and to design an improved CO and NO co-oxidation catalyst suitable for LTC exhaust aftertreatment, we first developed the required scaling relationships for NO oxidation on terrace and step sites of eleven late transition metals using DFT calculations. Next, we combined our new results with available literature data for CO oxidation and built a micro-kinetic model for the CO and NO co-oxidation at 425 K to analyze activity and selectivity trends across metal alloy catalysts. Our model correctly captures mutual inhibition effects for Pt and Pd during simultaneous CO and NO oxidation as well as reported particle size effects. Computational screening of several binary alloys indicates that alloys of coinage metals can potentially reduce the inhibition effect and exhibit superior low temperature oxidation performance as long as one catalyst component has oxophilic character.

2. THEORETICAL METHODS 2.1 DFT Calculations All periodic density functional theory (DFT) calculations were performed using the Vienna ab initio Simulation Package (VASP) as calculator in the Atomic Simulation Environment (ASE).29– 31

The interactions with the atomic cores were described by the projector-augmented-wave

(PAW) method,32,33 and exchange and correlation were treated in the revised Perdew-BurkeErnzerhof generalized gradient approximation (GGA-RPBE).34,35 Wave functions were expanded into plane wave basis set with an energy cutoff of 400 eV. Partial occupancies of bands were set using Gaussian smearing with a Fermi temperature of kbT = 0.1 eV and electronic energies were subsequently extrapolated to 0 K. All slab calculations included a dipole correction in the


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direction normal to the surface,36 and relaxed geometries were optimized until the force was less than 0.05 eV/Å. The climbing image nudged elastic band (NEB) method with a minimum of 5 intermediate images was used to locate transition states and estimate activation energy barriers.37 All reported transition states were confirmed by frequency analyses in the harmonic oscillator approximation and a Cartesian displacement of 0.01 Å, showing only a single imaginary vibrational mode along the reaction coordinate. These parameters were chosen to maintain consistency of our results with prior literature data.18,38 To establish NO oxidation trends, we studied eleven late transition metals. Most of these metals (Pt, Pd, Rh, Au, Ag, Cu, Ir, Ni) adopt the fcc crystal structure, Ru and Co are hcp crystals, and Fe has bcc structure. The lattice constants for all metals, summarized in Table S 1, were optimized for their preferred bulk structure, and for Ru and Co also in the fcc structure. For Ni, Co and Fe spin polarization was included. Adsorption calculations and NEB calculations were performed on both terrace surfaces and step sites of these metals. The terrace surface was modeled as the thermodynamically most stable, close packed surface: the (111) surface for fcc metals, the (0001) surface for hcp metals, and the (110) surface for bcc metals. For stepped surfaces, the fcc(211) surface was used for fcc and hcp metals. In the case of the bcc metal Fe, the stepped (210) surface was used.39 Terrace surfaces were modeled as a (3×3) surface unit cell and stepped surfaces used a (2×1) unit cell, as shown in Figure S 1. All slabs have an equivalent of four atomic layers, with the top two layers fully relaxed and the bottom two layers fixed. The vacuum distance between slabs in the z direction is 20 Å. A 6×6×1 Monkhorst-Pack k-point grid was used for terrace surfaces, and a 4×4×1 grid for stepped surfaces.40 Convergence with respect to the k-point setup was confirmed to be within 0.02 eV for oxygen adsorption on Pt(211). The reported adsorption energies were calculated with respect to clean slabs and the gas phase


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energies of H2(g), H2O(g), CH4(g), and NO(g). The DFT calculated O2(g) and NO2(g) energies were avoided, because DFT fails to correctly predict their enegies.41,42 Instead, the O2(g) and NO2(g) energies were corrected to match the heat of reaction for () + () → () and () + () → () to tabulated values.

2.2 Microkinetic model The steady-state microkinetic model for CO and NO co-oxidation was implemented in the python package CatMap.43 Both CO and NO oxidation reactions were assumed to follow a Langmuir-Hinshelwood mechanism,18,22 given by the following elementary steps:

CO(g) + * ↔ CO*

R1

NO(g) + * ↔ NO*

R2

O2(g) + * ↔ O2*

R3

O2* + * ↔ 2O*

R4

CO* + O* ↔ CO2(g) + 2*

R5

NO* + O* ↔ NO2(g) + 2*

R6

We assumed that both terrace and step sites exist in the system and all reactions may occur on both types of sites. Unless stated otherwise, we adopted a ratio of 95:5 for terrace:step sites, which roughly approximates a cubo-octahedral particle of 5-6 nm in diameter. Molecular adsorption and desorption steps were assumed to be non-activated and adsorbed species can diffuse freely between the two different sites. The CO, NO, and O adsorption, and NO oxidation transition state energies are adopted from calculations in this work. A constant correction of 0.2 eV was added to the CO adsorption energy


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on all metals to mitigate the effect of CO overbinding.44 Frequencies of these species were calculated on Pt surfaces only and were assumed to be identical on other metals. The energetic values and frequencies for O2 adsorption, and the transition states for CO oxidation and O2 dissociation were adopted from the literature.17,18,38,45 Entropy and enthalpy corrections were included in the model in the harmonic approximation for adsorbates and using the Shomate equation for gas phase molecules. A detailed table of reaction intermediates and their corresponding formation energies and frequencies are provided in Table S 2.

3. RESULTS AND DISCUSSION 3.1 Adsorption We calculated adsorption energies for O, NO and NO2 on all symmetrically nonequivalent adsorption sites on the terrace and step surfaces of 11 transition metal surfaces. These results are summarized in Table 1 and Table 2. On terrace surfaces, O preferentially binds to the 3-fold hollow fcc or hcp sites. NO does not show any general site preference. It can bind to top, fcc or hcp sites depending on the metal, but it consistently binds to the surface through its N atom. NO2 has two possible stable adsorbed configurations. Figure 1a shows the T,T-N,O-nitrito structure, in which NO2 binds via its N-O bond, bridging two surface atoms.46 Alternatively, NO2 can bind to two surface through its two oxygen atoms forming an inverted V structure normal to the surface. This configuration is displayed in Figure 1b and is referred to as the T,T-O,O’-nitrito structure.46


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(a) T,T-N,O-nitrito

(b) T,T-O,O’-nitrito

Figure 1. Side and top views of NO2 adsorption configurations on (a) Pt(111), and on (b) Au(111).

Table 1. Adsorption energiesa and most favorable adsorption positions on terrace surfaces. O Surfaces

NO

NO2

Position

EO*/e V

Position

ENO*/e V

Position

ENO2*/e V

Configurationb

Au(111)

fcc

2.53

top

-0.17

t-b-t c

0.96

b

Ag(111)

fcc

2.12

fcc

-0.12

t-b-t

0.51

b

Cu(111)

fcc

0.97

fcc/hcp

-0.86

t-b-t

0.25

b

Pd(111)

fcc

1.31

fcc

-1.93

t-b-t

0.45

a/b

Pt(111)

fcc

1.45

fcc

-1.53

t-b-t

0.51

a/b

Rh(111)

fcc

0.61

hcp

-2.15

t-b-t

-0.05

a

Ni(111)

fcc

0.31

fcc/hcp

-2.12

t-b-t

0.01

a/b

Ir(111)

fcc

0.89

top

-1.72

t-b-t

0.11

a


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Co(0001)

hcp

0.04

hcp

-2.07

t-b-t

-0.08

b

Ru(0001)

hcp

-0.06

top

-2.22

t-b-t

-0.26

a/b

Fe(110)

hollow/lb d

-0.66

hollow

-2.49

t-lb-t

-0.55

a

a

Energies are electronic energies without zero-point energy (ZPE) or entropy corrections.

b

Configuration a denotes T,T-N,O-nitrito, configuration b denotes T,T-O,O’-nitrito.

c

t-b-t denotes the top-bridge-top site.

d

lb denotes the long-bridge site.

Table 2. Adsorption energiesa and most favorable positionsb on stepped surfaces. O

NO

NO2

Surface Position

EO*/eV

Position

ENO*/eV

Position

ENO2*/eV

Configurationc

Au(211)

EB

2.58

EB

-0.38

EB

0.70

b

Ag(211)

hcp2

2.09

EB

-0.25

EB

0.30

b

Cu(211)

hcp2

0.89

EB

-0.97

EB

-0.07

b

Pd(211)

fcc1

1.36

EB/hcp2

-1.95

EB

0.04

a

Pt(211)

EB

1.16

EB

-2.02

EB

-0.21

a

Rh(211)

EB

0.45

EB

-2.37

EB

-0.39

a

Ni(211)

hcp2

0.15

EB

-2.26

EB

-0.40

a/b

Ir(211)

EB

0.11

top1

-2.50

EB

-0.67

a/b

Co(211)

hcp2

-0.25

EB/hcp2

-2.24

EB

-0.55

b

Ru(211)

hcp2

-0.46

EB/top1

-2.63

EB

-0.97

b

Fe(210)

hcp2

-0.85

hcp2

-2.52

EB

-0.80

b

a

Energies are electronic energies without zero-point energy (ZPE) or entropy corrections.

b

EB (edge bridge), hcp2, fcc1, and top1 denote adsorption sites on the stepped surface as defined in Figure S 1 in the SI. c

Configuration a denotes T,T-N,O-nitrito, configuration b denotes T,T-O,O’-nitrito.


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Stepped surfaces are less symmetric than terrace surfaces and more distinguishable adsorption sites are available, but in general, the undercoordinated sites along the step (EB, top1, fcc1 and hcp2 site as shown in Figure S 1) are more active. The lower coordination number causes a dband center shift closer to the Fermi level,47–49 which is consistent with the stronger binding exhibited by stepped surfaces (Table 2) compared to flat terraces (Table 1). As on terrace surfaces, NO2 exhibits two possible adsorption configurations on step sites, but it always assumes the same configuration on a given metal regardless of the surface structure. One exception is iron, where the T,T-N,O-nitrito configuration is more stable on the flat (110) surface, while the T,T-O,O’-nitrito configuration is preferred on the (210) step surface.

3.2 NO Oxidation Reaction We consider the surface catalyzed NO* + O* → NO2* + * step for the formation of NO2. The most stable O and NO co-adsorption configurations were used as initial states, and the most stable NO2 adsorption configurations were used as final states. The reaction pathways between these states were examined with NEB calculations to locate transition states and calculate reaction barriers, which are tabulated in Table 3. On terrace surfaces, the reaction pathways can be divided into two groups based on the two different stable NO2 configurations. For each group, the transition state configuration is similar (except for Ir and Co). When NO2 has the T,T-N,O-nitrito configuration, the transition state has a configuration in which O binds to a bridge site and NO binds to the closest top site; when NO2 has the T,T-O,O’-nitrito configuration, the transition states have O and NO binding to the nearest 3-fold hollow sites. We were unable to identify a similar transition state grouping for stepped surfaces, probably because of their higher geometric complexity.


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Upon inspection of Table 3, we observe that NO oxidation on the noble metals Ag and Au has a relatively small reaction barrier, whereas Fe has the largest reaction barrier. For most metals, the activation energies on steps and terraces are similar. Therefore, we consider both as possible active sites for NO oxidation. We also note a couple of observations regarding the reactions on Ru and Fe. For the Ru(211) surface the activation barrier is 0.36 eV lower when the second most stable T,T-N,O-nitrito configuration is used as final state. This NO2 configurations is 0.11 eV less stable than the T,TO,O’-nitrito configuration, but both adsorb at the same EB step site. This suggests that the T,TN,O-nitrito configuration of NO2 is more easily formed on Ru(211), and an isomerization step to form the more stable T,T-O,O’-nitrito configuration may be required. For the Fe(110) surface, O and NO approach each other from their initial adsorption sites and reach the final NO2 state without forming a defined transition state. In other words, NO2 dissociation occurs spontaneously to O and NO on Fe(110) and we use the reaction energy as activation energy for NO oxidation on Fe(110).

Table 3. Activation energies and reaction energies for O* + NO* → NO2* + * reaction on different metal surfaces. These energies are given relative to infinitely separated initial states.a Terrace Surfaces Ea/eV ∆E/eV Step Surfaces Ea/eV ∆E/eV Au(111)

0.04

-1.40

Au(211)

0.14

-1.50

Ag(111)

0.04

-1.50

Ag(211)

0.20

-1.53

Cu(111)

0.77

0.14

Cu(211)

0.94

0.01

Pd(111)

1.55

1.07

Pd(211)

1.57

0.64

Pt(111)

1.35

0.59

Pt(211)

1.68

0.65


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a

Rh(111)

1.70

1.49

Rh(211)

1.99

1.53

Ni(111)

1.96

1.82

Ni(211)

1.98

1.71

Ir(111)

1.40

0.94

Ir(211)

2.51

1.73

Co(0001)

2.43

1.96

Co(211)

2.20

1.94

Ru(0001)

2.18

2.02

Ru(211)

2.40

2.12

Fe(110)

2.60

2.60

Fe(210)

2.79

2.58

Energies are electronic energies without ZPE or entropy corrections.

3.3 Scaling Relations The NO oxidation activation energy calculations in Table 3 may be related to the adsorption energy results from Table 1 and Table 2 in terms of a transition state scaling (TSS) relation as depicted in Figure 2. As descriptor on the ordinate we chose the adsorption energy of the dissociated initial state at infinite separation given by the sum of the binding energies ∗ and

∗ from Table 1 and Table 2. The transition state energy on the abscissa is defined as the sum of the activation energy from Table 3 and the initial state energy at infinite separation.


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O* + NO*

2.5

NO2(g) + 2*

Au

Au

Ag 2.0

Ag

1.5

ETS/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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Pt Pd Pt

1.0

Co

0.5

Ir

0.0

Ru Co

Fe Fe

-0.5

Rh

Pd Cu Ir

Cu

Rh

Ni Ni

terrace surface stepped surface

Ru -1.0 -4

-3

-2

-1

0

1

2

3

EO*+ENO*/eV

Figure 2. The fitted transition state scaling relation for NO oxidation on terrace and stepped surfaces. For terrace surfaces: = 0.507( + ) + 1.104, = 0.957 ; for stepped surfaces: = 0.530( + ) + 1.125, = 0.973.

The TSS relation in Figure 2 allows us to make some general observations regarding the catalytic reactivity trend on transition metals for NO oxidation. For noble metals such as Au and Ag, NO and O bind weakly to the surface, resulting in a high transition state energy for the formation of NO2 despite the low activation barrier. For reactive metals, the transition state energy is low because NO and O in the initial state bind to the surface too strongly. This may have an adverse effect on the NO oxidation rate because of active site blocking or by rendering the NO2 formation step endothermic. It can also be observed that the TSS relation for terrace and


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step surfaces are similar, which indicates a weak structure sensitivity for the NO oxidation reaction and active sites may be found on both facets. Based on the linear TSS relation in Figure 2, we conclude that the NO oxidation transition state energy can be expressed as a function of and only. Previous studies in the literature have identified linear scaling relations between transition states and the descriptors and for CO oxidation and O2 dissociation reactions.17,18 In this work, we consider two kinds of surfaces resulting in six independent variables - , and on terrace and on stepped surfaces. Ideally, we want no more than two independent descriptors to allow for easy trend analysis and plotting. While structure-sensitive scaling relationships have recently been reported,50–52 we simply tested several combinations and empirically found that the binding energies of CO and O on stepped surfaces ( _" and _" ) work well for our purpose. The binding energies of CO and O on terrace surfaces can be easily scaled with _" or _" , implying that the same is true for the CO-O and O-O transition state energies on terrace surfaces. Scaling relations are known to work well for species that bind through chemically similar atoms.53 Although NO binds through its N atom and it is not obvious that _" and _" are sufficient to find a linear scaling relationship for , we were able to show empirically in Figure S 2 that can be expressed linearly by _" and _" . The R2 values are 0.890 for on terrace surfaces and 0.982 for on stepped surfaces. Thus, only two variables, _" and

_" , are needed as reactivity descriptors to predict the binding energies of the other intermediates and the reaction barriers. For simplicity, we use and to refer to _" and

_" for the remainder of this work.

3.4 Microkinetic Models


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With the DFT derived scaling relations and the descriptors and , we can build microkinetic models for CO and NO co-oxidation reactions, and analyze the reactivity trends by generating volcano plots for given conditions. Figure 3a shows the initial production rate volcano plots for CO2 and NO2 under traditional diesel engine emission condition where T = 600 K, P = 1 atm, with 5000 ppm CO, 100 ppm NO and 10% oxygen. The balance is inert nitrogen. Pt and Pd metals are re-identified as the best monometallic catalysts for CO and NO oxidations at high temperature, which agrees with previous experimental observations and micro-kinetic results.5,17,18 Their high oxidation activity is part of the reason why Pt/Pd alloys are commercially used as diesel oxidation catalysts. However, at LTC engine working temperature (T = 425 K), the optimal activity region moves in the direction of the coinage metals (top-right region) as shown in Figure 3b. Monometallic Ag is found to be the metal sitting closest to the volcano top, suggesting that Ag is the best monometallic catalyst for both NO and CO oxidations at lower temperatures. Other metals in the vicinity are Au, Cu, Rh, Pd and Pt. The volcano plot for CO oxidation at two different temperatures is consistent with a previously published micro-kinetic model at different reaction conditions.18 The shift of the volcano top can be explained by a change of surface coverage with temperature. As indicated in Figure 4, the maximum activity is found in the regions where no single intermediate blocks the surface.


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(a)

(b)

Figure 3. CO2 and NO2 production rate contour plots as a function of EO and ECO. The feed composition is 5000 ppm CO, 100 ppm NO and 10% O2. An error bar of 0.2 eV is included for each metal, which is approximately the DFT calculation accuracy using the RPBE functional.35 (a) High temperature condition (T = 600 K, P = 1 atm). (b) LTC engine exhaust temperature (T = 425 K, P = 1 atm). The black/white dots represent binary alloys in the descriptor space.


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(a)

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(b)

Figure 4. Steady-state coverage plots as a function of EO and ECO. The feed composition is 5000 ppm CO, 100 ppm NO and 10% O2. The O* coverage is shown in red, NO* coverage is shown in blue, and CO* coverage is shown in green. The grey area represents the low coverage region. (a) High temperature condition (T = 600 K, P = 1 atm). (b) LTC engine exhaust temperature (T = 425 K, P = 1 atm).

The CO2 and NO2 production rate plots at 425 K have two adjacent maxima. To explain this phenomenon, we analyzed the reaction rates of individual elementary steps and found that the two maxima can be explained by bifunctional catalytic behavior since two types of surfaces exist in the system. The larger volcano top at the center corresponds to the maximum O2 dissociation rate on terrace surfaces, while the secondary volcano top to the top-right corresponds to the maximum O2 dissociation rate on stepped surfaces. Furthermore, O2 dissociation is so active on the stepped surfaces that some produced O* atoms diffuse to terrace surfaces and facilitate the oxidation reactions on the terrace following a bifunctional mechanism. The two individual maxima overlap and result in the total production rate plot with two maxima. The relative extent of the two maxima can be tuned by varying the ratio of terrace to step sites, as indicated in


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Figure 5 for CO2 production. The top-right volcano top is becoming increasingly vital as the proportion of step sites increases, which can be correlated to particle size effects on activity. With decreasing particle size, i.e. increasing number of under-coordinated step sites, coinage metals become more active. This is in good agreement with previous studies showing that nanosized Au particles are more catalytic active compared to larger Au particles.15,16

(a)

(b)

(c)

(d)

Figure 5. Initial CO2 production rate plots for different terrace to step site ratios. (a) 99.5:0.5, (b) 98:2, (c) 80:20, (d) 50:50. Reaction conditions are T = 425 K, P = 1 atm, with 5000 ppm CO, 100 ppm NO and 10% O2.

One origin of inhibition between CO and NO oxidation becomes obvious when we consider a finite level of conversion. The production rate plots for 10% conversion at T = 425 K are shown


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in Figure 6 and exhibit a large region spanning Au, Pt, Pd and Rh, where NO is formed instead of being oxidized to NO2. The individual elementary reaction rates reveal that in this region any produced NO2 immediately reacts with CO to form NO and CO2. Thus CO oxidation in this region is promoted by the overall reaction CO + NO2 → CO2 + NO and the volcano top for CO2 production shifts to the area with optimal conditions for this reaction. This phenomenon can only be observed when considering a finite conversion of NO to NO2 (10% in our case) when solving the microkinetic model. The implication is that in this area NO and CO can be oxidized only sequentially, and CO oxidation must be complete before NO oxidation may occur.

Figure 6. CO2, NO2 and NO production rate plots as a function of EO and ECO at T = 425 K, P = 1 atm. The gas phase contains 5000 ppm CO, 100 ppm NO and 10% O2. CO and NO are assumed to have 10% conversion.

In addition to rationalizing the sequential oxidation behavior at finite conversion, we investigate the direct kinetic coupling between NO and CO oxidation, which may cause cooxidation rates to differ significantly from the individual oxidation rates. The extent of promotion of CO oxidation by NO2 depends on the rate of NO oxidation; thus, we consider the interplay between CO and NO oxidation at 0% conversion, where only the forward reactions


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may take place. This analysis decouples the intrinsic kinetic effects on the initial oxidation rates from any secondary reaction pathways. To define the catalytic consequences of CO and NO co-oxidation more accurately, we introduce a relative change in turnover frequency (TOF)

#$ =

% &' − 1, % &$

i = *+ , ,.

Here, % &$ represents the TOF when CO or NO is individually oxidized and % &' represents the oxidation rate of i when CO and NO are co-oxidized. If #$ equals 0, CO and NO oxidation proceed independently. A negative value of #$ indicates inhibition, whereas a positive value corresponds to promotion. The blue area in Figure 7 represents the region where inhibition is predicted (#$ < 0), while the red area indicates a promotional effect of co-oxidation (#$ > 0). In the green areas both oxidation reactions proceed independently of each other ( #$ = 0 ). The explanation for the inhibition in the blue area in the bottom-right side is intuitive. The addition of NO/CO not only leads to competitive adsorption between both reactants, but also decreases the number of empty sites required for O2 activation; thus, lowering the CO/NO oxidation rate, and vice versa. This agrees well with experimental observations showing that the onset temperature of CO oxidation on Pt is higher in the presence of NO.8 Less intuitive is the inhibition effect predicted for the topright area where sufficient empty sites for NO and CO adsorption are present. Here, the cooxidation rates are lowered because NO and CO compete for the limited number of O*. In the red area, however, CO oxidation and NO oxidation show synergy and the co-oxidation rates are higher than the individual oxidation rates. The explanation for this behavior is that the most


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abundant surface intermediate in this region is O* and the addition of NO or CO helps in regenerating empty sites. The overall outcome is an increase in the reaction rate for NO and CO oxidation.

(a)

(b)

Figure 7. Relative inhibition plots. (a) Relative change in CO oxidation TOF during CO and NO co-oxidation compared with CO oxidation alone. (b) Relative change in NO oxidation TOF during CO and NO co-oxidation compared with NO oxidation alone. The mutual inhibition and the sequential nature of CO and NO oxidation over commercially used Pt-Pd DOC implies that the catalyst bed must to be sufficiently long to ensure adequate NO conversion. Though the sequential nature of CO and NO oxidation is difficult to avoid over most metals, Figure 3b and Figure 7 suggest that a more active DOC formulation for LTC engine exhaust applications may exist, which offer the added advantage of preventing mutual inhibition. If such a catalyst formulation can be identified it would offer the advantage of lowering required metal loading for the DOC. Unfortunately, no monometallic catalyst falls directly into the target area and we resort to computational screening of bimetallic alloys to fill in the blank descriptor space.

3.5 Alloy Screening


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Based on a previous theoretical study on bimetallic surfaces, a linear interpolation scheme is a reasonable first approximation to predict oxygen binding energies.54 Following this argument, there is a good probability that alloys of coinage metals from the top-right region with another metal from the bottom-left region may fall into the target area. Thus, we selected a series of alloys that satisfy this constraint and present them in Figure 8. Most metal alloys have a 3:1 atomic ratio with Ag, Au, or Cu as the host metal, but some alloys with 1:1 ratio have also been tested.

Figure 8. Parity plot of CO binding energies calculated from DFT and values estimated using linear interpolation. For A3B type alloys, A is the host (A = Pt, Pd, Ag, Au, or Cu). Only the AB step of A3B type alloys are labeled in the figure. The complete data of CO binding energies on each alloy surface and the corresponding adsorption sites are listed in the Table S 3.

To computational screen the alloy candidates, we calculated the values of the two descriptors, EO and ECO, on their step models. For alloys with 3:1 metal ratio, we distinguish between two


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possible step types: the AA-step and the AB-step, as shown in Figure S 3. The adsorption is not strictly limited to the step edge sites of these models and near step sites are also considered. For A3B alloys where A is one of the less reactive metals Ag, Au or Cu, the adsorption on the AB type step is generally more stable than on an AA type step. In contrast to our expectation based on the work from Greeley and Nørskov for O binding on flat terrace surfaces,54 the binding energies of O and CO on stepped alloy surfaces show significant deviations to the linear interpolation between adsorption energies of monometallic surfaces with respect to the composition ratio of alloy, as indicated in Figure 8. For most alloys, CO binds even more strongly on stepped alloy surfaces than on the corresponding pure metal surfaces. This observation can be easily rationalized considering that CO needs only a single metal binding site and there are documented cases where the d-band center of a reactive metal moves closer to the Fermi level, when it is dispersed within a less reactive metal.55 In the volcano plot of Figure 3b we have summarized the screening outcome and included alloy materials as dots, but labeled only those that have interesting properties. A complete table with the calculated descriptor values and preferred adsorption sites is provided as Table S 3. The CO binding preference to the most active metal is reflected in Figure 3b by the alloy clusters near Pt, Pd, Rh or Cu, while only very few alloys fall into the region of high oxidation activity. Among the screened alloys Ag3Pd is the only one that has both AA and AB type steps with properties that place it closely to the volcano top region. Another alloy of interest is the Au3Cu alloy. Its AB type step is predicted to lie most closely to the volcano top, and even the less active AA type step is predicted to have better low temperature performance than Pt-Pd alloys. Besides, as discussed above, the AA type step gains activity for very small catalyst particles, and smaller particles are also more likely to exhibit a larger fraction of AB type sites. A recent experimental


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study using SiO2 supported Au-Cu alloy catalysts by Bauer et al. confirms the good low temperature activity of this alloy for CO oxidation.56 Moreover, the authors observed NO inhibition effects on CO oxidation on this catalyst, which is consistent with our placement of the Au3Cu alloy in the blue shaded inhibition area of Figure 7. Considering that low temperature CO oxidation activity is not a challenge per se, it may be more beneficial to focus on the inhibition behavior as the target property for a good LTC diesel oxidation catalyst design. For example, Binder et al. synthesized a novel mixed Cu, Co, Ce oxide catalyst, and successfully demonstrated the absence of any propene inhibition on CO oxidation over this material.57 While we cannot exactly place this ternary oxide into our screening plots in Figure 3 and Figure 7, we argued earlier that high oxygen surface coverage is a common characteristic of the alloys that show no inhibition, i.e., those in the red area of Figure 7. The AB step of the binary Cu3Co alloy would fall into this region, but this agreement may very well be coincidental. Other potential alloys with robust oxidation activity and minimal inhibition between CO and NO oxidation are those composed of Pt, Pd, Cu, Ag or Au, where at least one component is oxophilic. Notably, some of these binary alloys are predicted to have higher activity than the commercially used Pt-Pd alloy. We propose these coinage metal alloys as promising candidates for further experimental tests to validate the predictive capability of our model and potentially discover the next generation diesel oxidation catalyst. It must be acknowledged, however, that the presented screening method can merely provide guidance in the search of good catalysts with high initial activity and minimal inhibition, but for practical applications one must also consider additional properties, such as other feed components (e.g. hydrocarbons, water), cost, support effects, metal/oxide phase transition behavior, sintering stability, coking, deactivation, etc.


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4. CONCLUSIONS We used DFT to study the NO oxidation reaction on flat and stepped surfaces of eleven late transition metals. The activation barriers on both surfaces follow nearly identical scaling relations, suggesting that the relevant N-O bond forming/breaking step is not structure sensitive. To further assess overall NO oxidation activity trends we integrated our results with existing scaling relations for CO oxidation and empirically found that the binding energies of O* and CO* at step sites are suitable descriptors for all relevant surface species and activation barriers on both step and terrace sites. A descriptor-based microkinetic model of simultaneous CO and NO oxidation over bifunctional metal catalysts with active sites on terraces and steps asserts that Pt and Pd are the best metal catalysts for traditional diesel engine emission conditions at T = 600 K. For lower temperatures representative of LTC engine emissions (T = 425 K), the region of maximum oxidation activity moves towards coinage metals, consistent with the archetypical low temperature CO oxidation activity of Au nanoparticles. By varying the ratio of step to terrace sites, our model also reproduces known particle size effects. One of the many challenges for low temperature emissions catalysis is the presence of multiple contaminants that can mutually inhibit their oxidation. From a detailed reaction rate analysis we have identified three main causes for inhibition: (i) on most active metals the oxidation of NO may only occur after all CO has been consumed, because NO2 is a better oxidant than O2; (ii) competitive adsorption between CO and NO hinders O2 activation and adsorption for alloys with weak oxygen affinity and high CO/NO affinity; and (iii) competition for surface O* species on less reactive alloys with mostly empty sites. On the flip side our model also predicts regions in


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the descriptor space with high oxygen coverages, which encompasses several alloys of Pt, Pd, Cu, Ag or Au. These catalysts may even show a promotion in oxidation activity when the second reactant is introduced. We note that many materials in the inhibition-free target area are highly oxophillic and the formation of surface and/or bulk oxides is likely to occur. Although we neglect the possibility of oxide formation in our screening study, our results are by and large consistent with experimental reports on closely related binary and ternary oxide catalysts. While counterexamples likely exist, we take the overall good agreement with various experimental findings as indicator for the applicability of our work for the future design of low temperature diesel oxidation catalysts.

ASSOCIATED CONTENT The Supplemental Information is available free of charge. Bulk lattice constants from DFT calculations, surface models for pure metals and A3B type alloys, microkinetic model input energetics (PDF)

AUTHOR INFORMATION Corresponding Author * Lars C. Grabow: [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.


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Funding Sources This work is funded by the US Department of Energy (DE-EE0008233) and the National Science Foundation (CBET-1258688). ACKNOWLEDGMENTS We thank Drs. William S. Epling and Michael P. Harold for fruitful discussions. Computational resources were provided by the Extreme Science and Engineering Discovery Environment (XSEDE) supported by National Science Foundation (ACI-1548562) and the National Energy Research Scientific Computing (NERSC) Center, a DOE Office of Science User Facility supported by the Office of Science, U.S. Department of Energy, under contract number DE-AC02-05CH11231. Additional support for this work was provided by resources of the uHPC cluster managed by the University of Houston and acquired through NSF Award 1531814. We also thank the Center of Advanced Computing and Data Systems (CACDS) and Research Computing Center (RCC) at the University of Houston for access to the Opuntia/Sabine clusters. AUTHOR BIOGRAPHIES Yuying Song received her B.S. degree in Chemical Engineering from the East China University of Science and Technology in 2013 and subsequently joined the Department of Chemical and Biomolecular Engineering at the University of Houston to pursue a Ph.D. degree under the mentorship of Prof. Lars Grabow. Her research focuses on fundamental investigations of heterogeneous catalysis


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and the design of improved catalytic materials using a combination of computational and experimental methods. She works primarily on applications relevant to emissions control catalysis for low temperature combustion diesel engines, as well as the synthesis of clean and sustainable fuels from biomass feedstock. Lars C. Grabow is an Associate Professor of Chemical and Biomolecular Engineering with a courtesy appointment in Chemistry at the University of Houston. He received his Ph.D. in Chemical Engineering under the guidance of Manos Mavrikakis from the University of Wisconsin in 2008 and continued his postdoctoral research between 2008 and 2011 in the group of Jens Nørskov at the Technical University of Denmark and Stanford University. His expertise is the application of electronic structure calculations (density functional theory) and kinetic modeling to problems in heterogeneous catalysis, energy storage and surface science. Dr. Grabow won the U.S. Department of Energy Early Career Award (2014), the NSF CAREER Award (2015), and the Excellence in Research Award at the Assistant Professor level from the University of Houston (2017). He serves as the 2018 Chair of the AIChE Catalysis and Reaction Engineering division and Vice-Chair of the Southwest Catalysis Society. He is a member of the International Advisory Board of ChemCatChem and a former member of the Early Career Advisory Board of ACS Catalysis from 2017 – 2018.

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Table of Contents Graphic


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Activity Trends for Catalytic CO and NO Co ... - ACS Publications

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