the d-band model and heterogeneous catalysis – part 1xes.fysik.su.se/pdf/bligaard-1.pdf · the...
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The d-band modeland
Heterogeneous Catalysis – Part 1
Chemical Surface Physics School, Stockholm May 19, 2010
Thomas BligaardCenter for Atomic-scale Materials Design
Department of PhysicsTechnical University of Denmark
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Harvesting Harvesting sunlightsunlight
SustainableSustainable butbut-- LowLow intensityintensity-- WeatherWeather, , seasonseason and and
time dependenttime dependent
Chemical Chemical storagestorage-- HighHigh energyenergy densitydensity-- Storable/moveableStorable/moveable-- Bridges temporal Bridges temporal cyclescycles ofofproductionproduction & & consumptionconsumption
-- ExploitsExploits existingexistinginfrastructureinfrastructure
Global annual energyconsumption supplied by the sun in one hour
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Part of the solution: Chemical Part of the solution: Chemical storagestorage
HoweverHowever......
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High catalytic efficiency- Large surface area – nanoparticles- Optimal surface composition and
structure – design
• More efficient catalysts
• Stable catalysts
• Catalysts made from Earth-abundant materials
A catalyst is a material that speeds up a chemical reaction
The The CatalystCatalyst ChallengeChallenge
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~80 years ago:Where is the hope ?
- for using calculations in solving atomic-scale problems
“The general theory of quantummechanics is now almost complete. The
underlying physical laws necessary for themathematical theory of a large part of
physics and the whole of chemistry arethus completely known, and the difficultyis only that the exact application of these
laws leads to equations much toocomplicated to be soluble.”
P.A.M. Dirac(Nobel PrizePhysics, 1933)
(Dirac, 1929)
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R.S. MullikenNobel Prize,
Chemistry, 1966
“In conclusion, I would like to emphasize strongly my belief that the
era of computing chemists, when hundreds if not thousands of
chemists will go to the computing machine instead of the laboratory for increasingly many facets of chemical
information, is already at hand.”
~40 years ago: Here it is !
(Mulliken, Nobel Lecture, 1966)
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~Today: The revolution is to come
New possibilities – eScience:“The next 10 to 20 years will see computational science firmly embedded in the fabric of science– the most profound development in the scientific method in over three centuries.”
A SCIENCE-BASED CASE FOR LARGE-SCALE SIMULATIONOffice of Science
U.S. Department of Energy, 2003
The big revolution is still to come !
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Traditional simulation flowComputational design at the atomic scale
Nørskov, BligaardRossmeisl, Christensen
Nature Chemistry1, 37-46 (2009)
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Outline of today’s lecture
Material design strategies- Surface activity:
• The d-band model (briefly)• Linear energy correlations/Scaling relations• Brønsted-Evans-Polanyi relations• Volcano-relations• Understanding the experimental trends for
the steam reforming reaction- Catalyst design
• Methanation• Selective hydrogenation• Hydrogen evolution
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Outline of tomorrow’s lecture
The d-band model and its implications in more detail• The Newns-Anderson model• Effective medium theory• Electronic structure effects in alloying• Structure sensitivity of catalytic reactions• The electronic and geometrical effects in
heterogeneous catalysis
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Three flavors of systematic“Computational Design”
A. Direct computational searchB. Data base screeningC. Descriptor-based search
Bligaard, Andersson, Skriver, Jacobsen, Christensen, NørskovMaterials Research Society Bulletin 31, 986 (2006)
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Three flavors of systematic“Computational Design”
A. Direct computational searchB. Data base screeningC. Descriptor-based search
Bligaard, Andersson, Skriver, Jacobsen, Christensen, NørskovMaterials Research Society Bulletin 31, 986 (2006)
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Direct Computational Search
Pick a set ofstructures/compositions
Calculate their properties
Experimental testing
Good enough ?
Yes!
No ! Choose betterStructures/compositions
+ Adaptively improving- Difficult to addconstraints after a run
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EvolutionaryAlgorithm
Johannessen, Bligaard, Ruban, Skriver,Jacobsen, Nørskov, Phys. Rev. Lett. 88,
255506 (2002)
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EvolutionaryAlgorithm
The most stable 4-component
ordered metal alloy is found in the 11th
generation, and the 20 most stable
have been determined in 45
generations
Johannessen, Bligaard, Ruban,Skriver, Jacobsen, Nørskov,
Phys. Rev. Lett. 88, 255506 (2002)
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Evolutionary algorithm for 4-component alloys
EAs outperform random search by a factor of 50 – even for this simple example
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Structural stability of ordered alloyseV/atom
75 %
25 %
Formation energyof the
L12 binaryalloy structures
with respectto pure metals
LMTO-GGAcalculations
Johannessen, Bligaard, Ruban, Skriver, Jacobsen, Nørskov,Phys. Rev. Lett. 88, 255506 (2002)
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Three flavors of systematic“Computational Design”
A. Direct computational searchB. Data base screeningC. Descriptor-based search
Bligaard, Andersson, Skriver, Jacobsen, Christensen, NørskovMaterials Research Society Bulletin 31, 986 (2006)
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Screening of Computed Data
Calculate properties for alarge number of systems
Look for systemshaving good qualities
Experimental testing
+ Ease of reusing data- Difficult to include enough
interesting systems
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Bligaard, Johannessen, Ruban, Skriver, Jacobsen, Nørskov,App. Phys. Lett. 83, 4527 (2003)
Pareto optimality
(as a methodfor searchingdatabases)
The 82 alloys with the most relevant properties areeasily obtainedfrom the full database of> 64,000 alloys.
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The Computational MaterialsData Repository
ternary
quaternary
International collaboration needed to reach relevant data base sizes
Munter, Landis et al.
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The vision
The molecular engineering workbench
experimental data theory
understanding/concepts new experiments new design tools
computation
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Three flavors of systematic“Computational Design”
A. Direct computational searchB. Data base screeningC. Descriptor-based search
Developing the descriptorsBligaard, Andersson, Skriver, Jacobsen, Christensen, Nørskov
Materials Research Society Bulletin 31, 986 (2006)
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The origin of catalytic trendsthe d-band model
Hammer, Nørskov, Nature 376, 238 (1995)Hammer, Nørskov, Adv. Catal. 45, 71 (2000)
Bligaard, Nørskov in Chemical bonding at surfaces, Elsevier (2008)
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Corollary to the d-band model:adsorbate energies scale
Nilsson, Pettersson, Hammer,Bligaard, Christensen, Nørskov
Catal. Lett. 100, 111 (2005)
The 0th order d-band model:
Adsorption energies on 3d, 4d, and 5d metals is linear in the d-band center location
Corollary to d-band model:
The adsorption energy of any adsorbate scales with the adsorption energy of any other adsorbate on the d-metals
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CHx adsorption energiesClose-packed surfaces
Scaling relations: CHx vs. C adsorption
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
Stepped surfaces CH
CH2
CH3
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d-band modelHammer and Nørskov, Nature 376 (1995) 238
+ Scaling slopeEffective Medium Theory (EMT) rationalization:Nørskov and Lang, Phys. Rev. B 21, 2131 (1980)Nørskov, Rep. Prog. Phys. 53, 1253 (1990)
Rationalization of scaling relations
ξγ +Δ=Δ AAH ExE x )(
Abild-Pedersen, Greeley, Studt, Rossmeisl, Munter, Moses, SkulasonBligaard, Nørskov, Phys. Rev. Lett. 99, 016105 (2007)
maxmax /)()( xxxx −=γ
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CHx adsorption energies Close-packed surfaces
Scaling relations: CHx vs. C
CH : 3/4
CH2 : 1/2
CH3 : 1/4
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
For AHx :slope = (xmax-x)/xmax Stepped surfaces
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Close-packed surfaces
Stepped surfaces
Scaling relations: NHx vs. N
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
NH : a=2/3
NH2 : a=1/3
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Scaling relations: OH vs. O
Stepped surfaces
Close-packed surfaces
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
OH : a=1/2
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Close-packed surfaces
Scaling relations: SH vs. S
Stepped surfaces
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
SH : a=1/2
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Predicting heats of reaction from scaling relations
Requires :1. Atomic C, O, and
S adsorption energies on all d-metals
2. Reaction intermediates on one metal (Pt)
Abild-Pedersen, Greeley, StudtRossmeisl, Munter, Moses
Skulason, Bligaard, NørskovPhys. Rev. Lett. 99, 016105 (2007)
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Scaling: Methanation
Jones, Bligaard, Abild-Pedersen, Nørskov, J. Phys.: Cond. Mat. 20, 064239 (2008)
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Scaling: Steam reforming
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Scaling: Ammonia synthesis
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Scaling: Water-gas-shift
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Scaling: Methanol synthesis
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Andersson, Bligaard, Kustov, Larsen, Greeley, Johannessen, Christensen, Nørskov, J. Catal. 239, 501 (2006)
Ediss (eV)
Brønsted-Evans-Polanyi (BEP) relations:e.g. CO dissociation
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Ediss (eV)
CO diss.slow
C, Opoisoning
Sabatier, Ber. Deutsch. Chem. Gesell. 44, 1984 (1911)Bligaard, Nørskov, Dahl, Matthiesen, Christensen, Sehested, J. Catal. 224, 206 (2004)
Bligaard, Nørskov in “Chemical Bonding at Surfaces”, Elsevier (2008)
Volcano: The methanation reaction:CO + 3H2 CH4 + H2O
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Nørskov, Bligaard,Logadottir, Bahn, Hansen,
Bollinger, Bengaard, Hammer, Sljivancanin, Mavrikakis, Xu,
Dahl, JacobsenJ. Catal. 209, 275 (2002)
Universality of BEPsBEPs exist for a number of
small molecules– and happen to be identical
Omnipresence of volcanoes– and very similar kinetics
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Generalized kinetic models
“BEPs” + “Contracted energy diagrams”
“Generalized Kinetic Models”
Models simplified to the level where they only contain the absolutely essential reaction steps
Bligaard, Nørskov, Dahl, Matthiesen, Christensen, Sehested,J. Catal. 224, 206 (2004)
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A generalized kinetic model: A2+2B 2AB
A2 + 2* 2A* R1 = 2k1PA2Θ*2 - 2k-1ΘA
2 (= r1 - r-1)
A* + B AB + * R2 = k2ΘAPB - k-2PABΘ* (= r2 - r-2)
Site conservation: 1 = ΘA + Θ*
Three equations with four unknowns (R1 , R2 , ΘA , and Θ*)
The missing equation is obtained from either:
Stationary coverage: dΘi/dt = 0: r1 + r-2 = r-1 + r2 (R1 = R2)
Rate-limitation: E.g. reaction 1 is slow: r2 = r-2 (R2 = 0)
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Stationary External ConditionsdPx/dt = 0
This reduces the differential equations to algebraic equations.
• Significantly reduces computation time.
A perfect local description of:
• Fixed bed reactors
• Fluidized bed reactors
• Trickle bed reactors
(But not applicable to Batch reactors)
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Numerical problems
General micro-kinetic model:
• Singular differential equations
Stationary solution:
• Ill-conditioned algebraic equations
Therefore specialized numeric methods are required !?
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Ill-conditioning of stationary state
-20
-15
-10
-5
0
5
-4 -3 -2 -1 0 1 2
Eadsorption (eV)
Log(
TO
F (1
/s))
r1 = r-1 , R2 << r1
Easy region
r2 = r-2 , R1 << r2
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The approach to equilibrium
- This simple model can be solved by Taylor-expanding
the equations in the limits where they are ill-defined.
R1 = 2k1PA2Θ*2 - 2k-1ΘA
2 = 2k1PA2Θ*2(1-γ1), γ1 = r-1/r1
R2 = k2ΘAPB - k-2PABΘ* = k2ΘAPB (1-γ2), γ2 = r-2/r2
γ = γ1 γ22 = PAB
2/(PA2PB2) . 1/Keq
This allows one to define the Kinetic Switching Parameter (KSP):
KSP = [ 3 + (2 Log(γ2) – Log(γ1))/Log(γ) ]/2
(which is 1 when step 1 is rate-determining and 2 when step 2 is)
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Simplest generalized kineticsA2+2B 2AB
Ea
ΔE1
BEP + All entropy lost on surface
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Dissociation is rate-limiting at optimumIf the process follows the universal BEP-relations
The switching happens to the left of the maximum !In other words: The optimal catalyst can not directly be improved by lowering the barrier of the rate-determining step
Eads (eV)
KSP
TOF
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Optimal catalysts– dependence on the approach to equilibrium
1. A2 + 2* ↔ 2A*
2. A* + B ↔ AB + *
eqBA
AB
KPPP 1
2
2
2
⋅=γ
Very exothermic reactions take place atsmall values of γ for a similar conversion
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Optimal catalysts– dependence on temperature and pressure
High temperature and low reactant pressure “moves”the optimal catalyst towards more reactive surfaces.
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Optimal catalysts– dependence on precursor stability
1. A2 + * ↔ A2*
2. A2* ↔ 2A*
3. A* + B ↔ AB + *
1. A2 + 2* ↔ 2A*
2. B + * ↔ B*
3. A* + B* ↔ AB + 2*
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Le Chatelier-like principle for optimal catalysts:coverage conservation laws
The coverage of a key reactant on the surface of the optimal catalyst under given reaction conditions is constant.
( in the simple case “coverage of A” = “1-BEPslope” )
The optimal catalyst is located where the coverage switches – or where the adsorption free energy is close to zero.
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More product poisoningnobler surface required
1. A2 + 2* ↔ 2A*
2. A* + B ↔ AB + *
eqBA
AB
KPPP 1
2
2
2
⋅=γ
Very exothermic reactions take place atsmall values of γ for a similar conversion
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Lower temperature or high pressurePoisons surface
High temperature and low reactant pressure “moves”the optimal catalyst towards more reactive surfaces.
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Stronger precursor bindingPrecursor competes with key reactant
1. A2 + * ↔ A2*
2. A2* ↔ 2A*
3. A* + B ↔ AB + *
1. A2 + 2* ↔ 2A*
2. B + * ↔ B*
3. A* + B* ↔ AB + 2*
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Le Chatelier-like principle for optimal catalysts:coverage conservation laws
The coverage of a key reactant on the surface of the optimal catalyst under given reaction conditions is constant.
( in the simple case “coverage of A” = “1-BEPslope” )
The optimal catalyst is located where the coverage switches – or where the adsorption free energy is close to zero
ΔEads = -0.6eV at 300K or ΔEads = -1.8eV at 900K
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Implications of ”Universality”
General insights into”How to pick optimal catalysts”
Bligaard, Nørskov, Dahl, Matthiesen, Christensen, Sehested,J. Catal. 224, 206 (2004)
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Which is the best catalyst?
Ammonia synthesis :N2+3H2 2NH3 (Ru, Fe, (Os))
Fischer Tropsch synthesis, methanation:nCO+(2n+1)H2 CnH2n+2+nH2O (Co, Ru, Rh, Ni)
NO reduction:2NO+2H2 N2+2H2O (Pt, Pd, Rh)
Oxidation:O2+2X 2XO (Pt, Pd, Ag)
……..
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Understanding trends in catalytic activity
Nørskov, Bligaard, Logadottir, Bahn, Hansen, Bollinger, Bengaard, Hammer, Sljivancanin, Mavrikakis, Xu, Dahl, JacobsenJ.Catal. 209, 275 (2002)
-4 -3 -2 -1 0 1 2 3 4
Ea (
eV)
-1012345
CONOO2
N2
100 bar673 KH2:N2 = 3:1
γ = 10-10, 10-5, 0.5
Flat surface
Step sites
Step kinetics
ΔE (eV)-4 -3 -2 -1 0 1 2 3 4
Nor
mal
ized
TO
F
0.0
0.2
0.4
0.6
0.8
1.0
Ea
(eV
)
-2-101234
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Understanding trends in catalytic activity-4 -3 -2 -1 0 1 2 3 4
Ea (
eV)
-1012345
CONOO2
N2
100 bar673 KH2:N2 = 3:1
γ = 10-10, 10-5, 0.5
Flat surface
Step sites
Step kinetics
ΔE (eV)-4 -3 -2 -1 0 1 2 3 4
Nor
mal
ized
TO
F
0.0
0.2
0.4
0.6
0.8
1.0
Ea
(eV
)
-2-101234
Ammonia synthesis :
N2+3H2 2NH3
Ru
Fe
CoMo
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-4 -3 -2 -1 0 1 2 3 4
Ea (
eV)
-1012345
CONOO2
N2
100 bar673 KH2:N2 = 3:1
γ = 10-10, 10-5, 0.5
Flat surface
Step sites
Step kinetics
ΔE (eV)-4 -3 -2 -1 0 1 2 3 4
Nor
mal
ized
TO
F
0.0
0.2
0.4
0.6
0.8
1.0
Ea
(eV
)
-2-101234
Fischer Tropsch synthesisand methanation:
nCO+(2n+1)H2
CnH2n+2+nH2O
Ni
Co
Fe Ru
Understanding trends in catalytic activity
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Understanding trends in catalytic activity-4 -3 -2 -1 0 1 2 3 4
Ea (
eV)
-1012345
CONOO2
N2
100 bar673 KH2:N2 = 3:1
γ = 10-10, 10-5, 0.5
Flat surface
Step sites
Step kinetics
ΔE (eV)-4 -3 -2 -1 0 1 2 3 4
Nor
mal
ized
TO
F
0.0
0.2
0.4
0.6
0.8
1.0
Ea
(eV
)
-2-101234
NO reduction:
2NO+2H2 N2+2H2O
Pt
PtRh
Pd
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-4 -3 -2 -1 0 1 2 3 4
Ea (
eV)
-1012345
CONOO2
N2
100 bar673 KH2:N2 = 3:1
γ = 10-10, 10-5, 0.5
Flat surface
Step sites
Step kinetics
ΔE (eV)-4 -3 -2 -1 0 1 2 3 4
Nor
mal
ized
TO
F
0.0
0.2
0.4
0.6
0.8
1.0
Ea
(eV
)
-2-101234
Oxidation:
O2+2X 2XO
Ag
Pt
Understanding trends in catalytic activity