Modeling reaction pathways - Brookhaven National … · Modeling reaction pathways ... CHEMKIN3, Cantera2, or Matlab An example 1 2( ) 2( ) 2 ( )2 ... 2Cantera (MatlabChemKinetics

Catalysis Center for Energy Innovation

Modeling reaction pathways

CCEI is an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Basic Sciences

University of DelawareDepartment of Chemical and Biomolecular Engineering Center for Catalytic Science and Technology (CCST)Catalysis Center for Energy Innovation (CCEI),

an Energy Frontier Research Center

November 4, 2014


Types of catalytic kinetic models and microkinetic modeling

Overview of parameter estimation methods and scales

Accuracy

Lateral interactions

Semi‐empirical methods

Thermodynamic consistency

MKM uses

Analysis

Catalyst discovery

Outline




Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Surface Reaction Rate Calculation Paradigm

• Hierarchy of calculation of surface reaction rates

Empirical rate-law; typically fitted to experimental data

Langmuir-Hinshelwood rate-law; developed using rate determining step (RDS) and MF theory

Microkinetic analysis;No assumptions on RDS (Dumesic, 1993)

Complexity/Reliability/Accuracy


Microkinetic Modeling• All relevant elementary reactions

– Written by hand or computer generated1

• No simplifying assumptions• Rate determining step (RDS), partial equilibrium (PE), quasi‐steady state (QSS), and most abundant reaction intermediate (MARI) are all computed

• Reactor + Catalyst model needed– Use computer software, such as surface

CHEMKIN3, Cantera2, or Matlab

An example1

2( ) 2( ) 2 ( )2

2( )

2( )

2

2 2 ( )

Re2* 2 *

2* 2 *

* * * ** * * **

g g g

g

g

g

H O H O

Elementary actionsH H

O O

H O HOHO H H OH O H O

1Ring: Rangarajan et al., Computers & Chemical Engineering 45, 114 (2012).2Cantera (Matlab Chem Kinetics Package): Goodwin et al., Cantera: An Object-oriented Software Toolkit for Chemical Kinetics, Thermodynamics, and Transport Processes. 2014.3Chemkin (Fortran Chem Kinetics Package): Coltrin; Kee and Rupley, Int. J. Chem. Kinet. 23, 1111 (1991).Coltrin; Kee and Rupley Surface CHEMKIN (Version 4. 0): A Fortran package for analyzing heterogeneous chemical kinetics at a solid-surface---gas-phase interface; SAND-90-8003B; 1991.Reactor Design (commercial kinetics software)




Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Parameter Estimation of Microkinetic Models

Parameters fitted to data Inability to simultaneously predict multiple experimental sets

Parameters estimated with empirical methods (Bond-Order Conservation) Efficient, reasonable accuracy (2-4 kcal/mol) Limited to small adsorbates

Density functional theory (DFT)-based semi-empirical methods Group additivity, Brønsted-Evans-Polanyi (BEP) relations

Parameters obtained from DFT Fairly accurate (<5 kcal/mol)

Hierarchical methods Empirical or semi-empirical to screen; DFT to refine (zero coverage limit) Include coverage effects


Hierarchy Enables Rapid Screening ofChemistry, Fuels, and Catalysts

Quantum:ab initio, DFT, TST,

CPMD, QM/MM MD

Continuum: MF-ODEs

Discrete: KMC

Ideal: PFR, CSTR, etc.

Computational Fluid Dynamics

(CFD)

Mesoscopic:PDEs

Discrete:CG-KMC

Pseudo-homogeneous:Transport correlations

Quantum-based correlations:

BEPs, GA, LSRs

Catalyst scale:Reaction rate

Reactor scale:Performance

Electronic scale:Parameter estimation

Accuracy, cost




Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Accuracy of MKM

• Myth: A microkinetic (detailed) model [even with DFT input] can quantitatively describe experimental data


C2H6O2 Steam Reforming

Good agreement with data*

No parameter fitting performedThermal dehydrogenation steps are kinetically most importantOH*‐mediated steps inactive on Pt (due to low [OH*])

Christiansen and Vlachos, App. Cat. A: General 431–432, 18 (2012).

1.8 1.9 2 2.1 2.2 2.310-2

10-1

100

101

Ea: 18 kcal mol-1

Ea: 15 kcal mol-1

1000 T -1 / K-1

H2 R

ate

/ min

-1

1.8 1.9 2 2.1 2.2 2.310-2

10-1

100

101

Ea: 17 kcal mol-1

Ea: 15 kcal mol-1

1000 T -1 / K-1

CO

Rat

e / m

in-1

1.8 1.9 2 2.1 2.2 2.310-4

10-2

100

Ea: 26 kcal mol-1

Ea: 20 kcal mol-1

1000 T -1 / K-1

CO

2 Rat

e / m

in-1

1.8 1.9 2 2.1 2.2 2.310-2

10-1

100

101

Ea: 13 kcal mol-1

Ea: 16 kcal mol-1

1000 T -1 / K-1

H2 R

ate

/ min

-1

Model (♦) Experiments (■)

* Kandoi et al., J. Phys. Chem. C 115 (4) 961 (2011)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Nor

mal

ized

Sen

sitiv

ity C

oeffi

cien

t

T = 483KExcess Water

T = 483KStoichiometric

T = 543KExcess Water

T = 543KStoichiometric

Feed Conditions

1st (C-H) Thermal Dehydrogenation2nd (C-H) Thermal Dehydrogenation1st (O-H) Thermal Dehydrogenation1st (C-H) Oxidative Dehydrogenation1st (O-H) Oxidative Dehydrogenation


You Need to Ensure That MKM Captures Correctly

Temperature effectsReaction orders of reactantsReaction orders of products (Effect of co‐feeding products)Ideally the RDS is tested spectroscopicallyIdeally the MASI is tested via IR

Salciccioli; Stamatakis; Caratzoulas and Vlachos, Chem. Eng. Sci. 66, 4319 (2011).


• Types of catalytic kinetic models and microkinetic modeling

• Overview of parameter estimation methods and scales

• Accuracy

• Lateral interactions

• Semi‐empirical methods

• Thermodynamic consistency

• MKM uses

– Analysis

– Catalyst discovery

Outline


Lateral Interactions:Estimation via Hierarchical Estimation Methodology

Myth: After parameterization of a microkinetic model via DFT (or semi‐empirical methods), the model is correct and no further refinement is needed


Lateral Interactions Are Typically Critical

Adsorbate heterogeneity arises due to coverage effectsCombinatorial problem in a priori estimating kinetic parameters due to coverage effects

x (nm)

y (nm)

Spatial Occupation Probability Map for CO*

0 0.5 1 1.5

0

0.5

1

1.5

0

0.2

0.4

0.6

0.8

1

Review of Catalyst and Kinetic Modeling: Salciccioli et al., Chem. Eng. Sci. 66, 4319 (2011)


Hierarchical Multiscale Modeling Principle: Dealing with Size (No. of Reactions and Coverage)

Start with a sufficiently simple, physical model at each scaleAutomatic mechanism generationFirst‐principles based semi‐empirical parameter estimation

Link all models Identify important scale and parameter(s)

Sensitivity and flux analyses

Use higher level theory for this scale and parameter(s)Kinetically relevant steps, inclusion of coverage effects, internal diffusion, etc.

Iterate

First‐principles accuracy at orders of magnitude reduced cost


NH3 Decomposition on Ru: 2NH3 =N2+3H2

• NH3 as a storage medium• ‘Pure’ H2 – No COx

• A microkinetic model is build using BOC and TST

• Our microkinetic model captures the trend

• High N* coverages

196 m x 84 m x 1078 m

Mhadeshwar et al., Cat. Letters 96, 13‐22 (2004)

0

0.2

0.4

0.6

0.8

1

650 850 1050 1250T [K]

N*

Empty sites (*)

0

20

40

60

80

100

Expts. [Ganley et al.]

PFR model

Exptl: Ganleyet al., AIChE J. 2003

*NH*NH 33 *H*NH**NH 23

*H*NH**NH 2 *H*N**NH *2N*2N 2 *2H*2H 2


DFT Estimates Lateral Interactions

• DACAPO (solid-state electronic structure package by Hammer and coworkers*)

• 3-Layer slab of Ru(0001)

• 2 2 unit cell

• All layers are relaxed

• Plane wave cutoff = 350eV

• 18 k-points for surface Brillouin zone

• Generalized gradient approximation (PW-91)

* Hammer et al., DACAPO version 2.7 (CAMP, Technical University, Denmark)

90

100

110

120

130

0 0.25 0.5 0.75 1(N*+H*) coverage [ML]

DFT Calculations (this work)Linear fit Q

N (at H*=0)=128.2-33.3N*

N on Ru(0001) 3 layer slab

Linear fit QN (at N*=0.25)=120.1-6.2H*

N-N interactions

N-H interactions



Exps: Ganley et al., AIChE J. (2004)

DFT‐Retrained Microkinetic Model

• H-H and N-H interactions are small

• N-N interactions completely change the chemistry

• Extensive validation against UHV and high P data

0

20

40

60

80

100

650 850 1050 1250

Expts. [Ganley et al.]

PFR modelwithout interactions

T [K]

PFR modelwith interactions

0

0.2

0.4

0.6

0.8

1

650 850 1050 1250T [K]

*

N* without interactions* without interactions

H*

N*

NH3*





Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Myth: First‐principles (DFT) MKM can be developed for any mechanism and feedstockEstimation with first‐principles semi‐empirical methods (FPSEM) is possibleRefinement of important parameters is feasible

Semi‐Empirical Estimation Methods


Modeling Reactions of Large Molecules is Challenging

Combinatorial explosion in number of calculations for first‐principles (DFT) calculations

Semi‐empirical methods can potentially identify relevant species and reactions instantaneously

Major advances in systematic development of semi‐empirical methods and understanding of errors

10

102

103

104

105

106

IntermediatesReactions

Num

ber o

f Cal

cula

tions

HO

HOOH

HO

OH

OHHO

OHHO

HO

HO

OH

OHHO

HO

HO

HO

HO

Sugars


DFT‐Based Estimation Methods

Surface thermochemistry via group additivityBrønsted‐Evans Polanyi relations for reaction barriers of homologous seriesTransfer thermo from one material to another (linear scaling relations)Perform high‐throughput microkinetic modeling (MKM) for materials predictionIdentify key steps and refine them via higher level theory

Linear Scaling Relations• Libraries of atomic binding energies

• Binding energies of AHx species vs. heteroatom A valency

• Metal transferability

Group Additivity • Single metal thermochemistry

• Thermodynamic consistency

• Screen adsorbates

Brønsted Evans Polanyi (BEP)• Activation energies• Reaction rate constants

• Screen reactions

Microkinetic Model• Compute rates, conversion, selectivity, abundant species

• Identify key adsorbates and reactions

• Refine thermochemistry and barriers via DFT

• Include adsorbate-adsorbate interactions

Review: Salciccioli et al., Chem. Eng. Sci. 66, 4319 (2011);Salciccioli et al., J. Phys. Chem. C 114, 20155 (2010); J. Phys. Chem. C 116, 1873 (2012); Sutton and Vlachos, ACS Catal. 2, 1624 (2012); J. Catal. 297, 202 (2013)


• Binding for oxygenates traced to (CHyOx) groups• Use alcohols and dehydrogenated alcohol intermediates to develop groups

• Include contributions for ΔHf,298, S(T) and Cp(T)

GroupΔHf,298 Value

[kcal/mol][C(C,H2)-O(M,H)] -50.2[C(C,H,M)-O(H)] -46.8[C(C,M2)-O(H)] -39.3[C(C,H2)-O(M)] -26.1[C(C,H,M)-O(M)] -26.5[C(C,M,M)-O(M)] -33.4[C(C,M)=O] -33.4[C(C2,H)-O(M,H)] -51.1[C(C2,M)-O(H)] -42.9[C(C2,H)-O(M)] -25.1[C(C2,M)-O(M)] -23.0

C

HH

OH

Pt Pt Pt

CH

H

H

Group Additivity for Adsorbed Species

Salciccioli, Y. Chen, and Vlachos, J. Phys. Chem. C 114(47), 20155 (2010).

O

C CH2

A BO

HC CO


Group Additivity for Adsorbed SpeciesSecond‐order effects included:

4‐member ring strainWeak interactionsHydrogen bonding

Calculations of ΔHf,298 of C2HxO2 and C3HxO3 species show good quantitative agreement

This method can be used for initial screening of larger hydrocarbons and oxygenates

-160

-120

-80

-40

-160-120-80-40

C2HxO2

C3HxO3

Gro

up a

dditi

vity

[kca

l/mol

]DFT calculated [kcal/mol]

Values are taken with respect to the most highly hydrogenated species in the gas phase (C2H6O2, C3H8O2) and H adsorbed on a separate slab

Salciccioli, Y. Chen, and Vlachos, J. Phys. Chem. C 114(47), 20155 (2010).Salciccioli et al., J. Phys. Chem. C 114, 20155 (2010); J. Phys. Chem. C 116, 1873 (2012)


Molecular binding energy is linearly dependent on atomic binding energy1These correlations relate molecular binding energy to atomic binding energy on different surfacesMultidentate species can be accounted for by summing the contributions of each bond to the surface2

Transferability Between Metals

[1] Abild‐Pedersen et al. PRL 99, (2007)[2] Salciccioli, Y. Chen, and Vlachos, J. Phys. Chem. C 114(47), 20155 (2010)

M Pt A,i,M A,i,Pt S ROHA,i

Q Q Q Q x E E

CH

H OH CH

H OH CH

OHCH

OH≈


This scheme was validated by testing the binding energy of all C2HxO2 intermediates on Ni(111) and Ni‐Pt‐Pt(111)

These correlations allow for metal transferability of the C2H6O2decomposition mechanisms

-120

-80

-40

0-120-80-400 P

redi

cted

Q [k

cal/m

ol]

Q DFT [kcal/mol]

Ni(111)-120

-80

-40

0 -120-80-400Pred

icte

d Q

[kca

l/mol

]

Q DFT [kcal/mol]

Ni-Pt-Pt(111) Pt(111)Qo,Qc

QC2HxO2

Salciccioli, Y. Chen, and Vlachos, J. Phys. Chem. C 114(47), 20155 (2010)

Transferability Between Metals


Linear Free‐Energy Relations (LFR)

Two types of relations are foundTransition State Scaling (TSS)Brønsted‐Evans‐Polanyi (BEP)

There is reductionistic trend of combining multiple reaction types into a homologous seriesMinimal calculationsAccuracy may be sacrificed

Connection between the two LFR types and distinct homologous series


Distinct Homologous Series Developed

Performed DFT calculations for methane, methanol, ethane, and ethanol

45 stable intermediates and 124 transition states

Considered C‐H (a and b positions), O‐H, C‐C, C‐O, and C‐OH reactionsStatistical tests are used

-1

0

1

2

3

4

5

-3 -2 -1 0 1 2 3

Combined C-HO-HCombined C-CC-OHC-O

Act

ivat

ion

Ene

rgy

(eV

)

Heat of Reaction (eV)

Sutton and Vlachos, ACS Catal. 2 1624 (2012); J. Catal. 297, 202 (2013)


Profound computational savings(Important) information content remains the same

1

102

104

106

CPU

hrs

DFT

Scre

en

conf

orm

ers v

ia G

A

DFT

GA

/BEP

->D

FT

GA/BEP

Glycerol thermal decomposition

Chen et al., J. Phys. Chem. 115(38), 18707 (2011)

Computational Savings from Hierarchy


Hierarchical Multiscale Modeling Principle: Dealing with Size (No. of Reactions and Coverage)

Start with a sufficiently simple, physical model at each scaleAutomatic mechanism generationFirst‐principles based semi‐empirical parameter estimation

Link all models Identify important scale and parameter(s)

Sensitivity and flux analyses

Use higher level theory for this scale and parameter(s)Kinetically relevant steps, inclusion of coverage effects, internal diffusion, etc.

Iterate

First‐principles accuracy at orders of magnitude reduced cost


Speed of Convergence of FPSEM Models

0

0.2

0.4

0.6

0.8

1

300 320 340 360 380 400

DFT02345

Frac

tiona

l Con

vers

ion

Temperature (°C)

It takes only a few iterations to converge the semi‐empirical model to be nearly indistinguishable with the DFT‐based MKMInteraction parameters in the first two iterations are most critical for qualitative agreement

3 2 4 2CH CH OH CH + CO + H

3 2 3 2CH CH OH CH CHO + H

3 2 4 22CH CH OH 3CH + CO

Ethanol MKM/Pt

160 reversible reactions67 gas and surface species

0

0.1

0.2

0.3

0.4

0.5

0.6

300 320 340 360 380 400

Frac

tiona

l C S

elec

tivity

, CH 4

Temperature (°C)

Sutton and Vlachos, Chem. Eng. Sci., In press




Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Myth: Your model is thermodynamically consistent A mechanism based solely on a single DFT mechanism is thermo consistentChallenges

DFT is not accurate for gas‐phase thermo overall thermo is not accurate; thus, we often mix high level ab initio data or NIST thermo dataKinetic parameters are adjusted to describe dataEither adjustment leads to thermo incosistencies

Thermodynamic Consistency of MKM


Most Literature Mechanisms Are Thermodynamically Inconsistent

0

20

40

60

80

100

473 573 673 773 873

CO

con

vers

ion

[%]

Temperature [K]

Coupling mechanismWGS

Equilibrium

Experiments(Xue et al.)

A/V=450 cm-1

Deutschmann et al.mechanism

WGS: CO+H2O=CO2+H2

10-12

10-8

10-4

100

104

300 700 1100 1500 1900

Keq

surfa

ce/K

eqga

s

Temperature [K]

Hickman and Schmidt mechanism

Optimized mechanism

CO oxidation: CO2= CO+O


Reaction

TSTPre‐exponentials of a reversible reaction

Enthalpic consistency for single reaction

Carry out a thermodynamic loop on a state variable X:

Thermodynamic Loop

f ,bS / RBf ,b

k TA eh

fbfb A/AlnR/)SS(R/S

HEE bf

f

b

A

AA B A B

A* B*

Gas

SurfaceSurface

Surface

A Bads surf adsX X X gasX

A * A*A* B*B* B *A B

‘Exact’


For every surface reaction, one can write a corresponding gas reaction and a thermo loopThe number of surface species usually determines the number of independent variablesCannot simply change barriers or pre‐exponentials to fit data without paying attention to thermo consistency

Linear Independence and Parameter Tuning

• Mhadeshwar et al., Thermodynamic consistency in microkinetic development of surface reaction mechanisms, Journal of Physical Chemistry B 107, 12721-12733 (2003).

• Salciccioli et al., A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior, Chem. Eng. Sci. 66, 4319–4355 (2011).




Accuracy




MKM uses

Analysis

Catalyst discovery

Outline


Reconcile apparently contradictory experimental data at different conditions (TPD, steady state, various operating conditions)Mechanistic understandingPerform reactor optimization

Model‐based design of experiments to assess modelRational catalyst design

CompositionSizeShape

What Can We Use MKMs for?Tr

aditi

onal

Mod

ern


0

0.2

0.4

0.6

0.8

1

200 240 280 320 360

C2H5pi-C2H4sigma-C2H4CCH3Vacancies

Cov

erag

e

Temperature [K]

-1 0 1

H2=50 torr

150665

Normalized Sensitivity Coefficient

j

iij A

RSlnln

Hydrogenation rxns are rate controlling (π-C2H4**→C2H5**, C2H5**→C2H6)

Ethylene Hydrogenation: Analysis


Ethylene Hydrogenation ReactionH2 + 2* ↔ 2H*C2H4 + 2* ↔ π-C2H4**C2H6 + 3* ↔ C2H5** + H*π-C2H4** + 2* ↔ σ-C2H4****π-C2H4** + H* ↔ C2H5** + *C2H5** + 3* ↔ σ-C2H4**** + H*σ-C2H4**** ↔ C2H3** + H* + *C2H2** + H* ↔ C2H3** + *CHCH3** ↔ CCH3* + H*CCH3* + 2* ↔ CCH2** + H*C2H5** + * ↔ CHCH3** + H*CHCH3** + *↔ C2H3** + H*C2H3** + * ↔ CCH2** + H*C2H** + H* ↔ CCH2** + *

10-510-410-310-210-1100101

1 10 100 1000

Reduced modelExperimentalOne step rate expressionC

H4 T

OF

[1/s

]

Ethane Pressure [Torr]

673K623K573K

0.01

0.1

1

10

100

100 1000

ExperimentalReduced microkinetic model Reduced rate expression

C2H

6 TO

F [s

-1]

Hydrogen Pressure [Torr]

336 K

298 K

273 K

248 K

223 K

H2 + 2* ↔ 2H*CH4 + 2* ↔ CH3* + H*C2H4 + 2* ↔ π-C2H4**C2H4 + 4* ↔ σ-C2H4****C2H2 + 2* ↔ C2H2**C2H6 + 3* ↔ C2H5** + H*π-C2H4** + 2* ↔ σ-C2H4****π -C2H4** + H* ↔ C2H5** + *C2H5** + 3* ↔ σ-C2H4**** + H*σ-C2H4**** ↔ C2H3** + H* + *C2H2** + H* ↔C2H3** + *C2H** + H* ↔ C2H2** +*CHCH3** ↔ CCH3* + H*CCH3* + 2* ↔ CCH2** + H*C2H5** + * ↔ CHCH3** + H*CHCH3** + *↔ C2H3** + H*C2H3** + *↔CCH2** + H* C2H** + H* ↔CCH2** + *C2H5** ↔ CH3* + CH2*2CH2* + 2* ↔ σ-C2H4****C2H3** ↔ CH* + CH2*C2H2** ↔ 2CH*C2H** ↔ CH* + C*CHCH3** ↔ CH3*+ CH*CH3* + C* ↔ CCH3* + *CCH2** ↔ CH2* + C*CHCH3** + 2* ↔ C2H4****C2H3** ↔ CCH3* + *C2H2**↔ CCH2** C* + H* ↔ CH* + *CH2* + * ↔ CH* + H*CH3* + * ↔ CH2* + H*

Ethane Hydrogenolysis ReactionH2 + 2* ↔ 2H*CH4 + 2* ↔ CH3* + H*C2H6 + 3* ↔ C2H5** + H*C2H5** + 3* ↔ σ-C2H4**** + H*σ-C2H4**** ↔ C2H3** + H* + *CHCH3** ↔ CCH3* + H*CCH3* + 2* ↔ CCH2** + H*C2H5** + * ↔ CHCH3** + H*CHCH3** + *↔ C2H3** + H*C2H3** + *↔CCH2** + H*C2H5** ↔ CH3* + CH2*CHCH3** ↔ CH3*+ CH*CH2* + * ↔ CH* + H*CH3* + * ↔ CH2* + H*

Model Reduction to Rate Expressions

Full mechanism

Reduced mechanism

Rate equation

Sensitivity analysisPrincipal component analysis

Elementary rate comparisonRate determining stepPartial equilibriumAbundant adsorbate assumptions

Full mechanism

Reduced mechanism

Rate equation

Full mechanism

Reduced mechanism

Rate equation

Full mechanism

Reduced mechanism

Rate equation

10-510-410-310-210-1100101

Reduced modelExperimental One step rate expressionC

H4 T

OF

[1/s

] 673K623K573K

PC2H6

= 5 Torr

10-410-310-210-1100101

10 100 1000

Reduced modelExperimental One step rate expressionC

H4 T

OF

[1/s

]


673K623K573K

PC2H6

= 25Torr

Ethane Hydrogenolysis

Ethylene Hydrogenation


Ethylene Hydrogenation Rate Expression

Due to multiple abundant adsorbates, the reduced rate expression is too complex for a closed form equation

0.01

0.1

1

10

100

100 1000

ExperimentalReduced microkinetic model Reduced rate expression

TOF

[s-1]


336 K

298 K

273 K

248 K

223 K


Search is done on atomic descriptors while running the full chemistry and reactor modelsOptimal catalyst properties are identified

High Throughput MultiscaleModel‐based Catalyst Design

350 oC1 atm

Prasad et al., Chem. Eng. Sci. 65, 240 (2010)

4550

5560

6570

110120

130140

0

0.1

0.2

0.3

0.4

0.5

QH

[kcal/mol]QN

[kcal/mol]

Con

vers

ion

NH3 decomposition*NH*NH 33

*H*NH**NH 23 *H*NH**NH 2

*H*N**NH *2N*2N 2 *2H*2H 2


Identifying Bimetallic Catalysts

• Optimum heat of chemisorption of N of ~130 kcal/mol

• NiPtPt is a good prospective bimetallic surface

Surface Sub-surface

Metals BEN (kcal/mol)PtTiPt 56.5PtVPt 59.5PtCrPt 72.6PtMnPt 84.9PtFePt 83.9PtCoPt 87.0PtNiPt 89.8NiPtPt 137.5CoPtPt 159.9FePtPt 169.9MnPtPt 162.2CrPtPt 166.5VPtPt 184.1TiPtPt 191.5

Pt 102.1 Ni 113.8


Emergent Behavior Verified Experimentally

3.0 Langmuir NH3

at 350K at UHV

Ammonia decomposes on Ni‐PtNo decomposition on other surfacesN‐Pt is the most active catalyst

Inte

nsity

(arb

. uni

ts)

750700650600550500450400350Temperature (K)

Thick Ni

Ni-Pt

Pt-Ni-Pt

Pt(111)

14 amu

Hansgen, Chen, and Vlachos, Nature Chem. 2, 484-489 (2010)

Ni-Pt-Pt


End

Documents

Modeling reaction pathways - Brookhaven National … · Modeling reaction pathways ... CHEMKIN3, Cantera2, or Matlab An example 1 2( ) 2( ) 2 ( )2 ... 2Cantera (MatlabChemKinetics