Computer Simulations, Scaling and the Prediction of Nucleation Rates

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Computer Simulations, Scaling and the Prediction of Nucleation Rates Barbara Hale Physics Department and Cloud and Aerosol Sciences Laboratory University of Missouri – Rolla Rolla, MO 65401 USA

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Page 1: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Computer Simulations, Scaling and the Prediction of Nucleation Rates

Barbara HalePhysics Department and

Cloud and Aerosol Sciences LaboratoryUniversity of Missouri – Rolla

Rolla, MO 65401 USA

Page 2: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Nucleation : formation of embryos of the new phase from the metastable

(supersaturated) parent phase

K. Yasuoka and M.

Matsumoto, J. Chem. Phys. 109,

8451 (1998)

Page 3: Computer Simulations, Scaling and the Prediction of Nucleation Rates

“Molecular dynamics of homogeneous nucleation in the vapor phase: Lennard-Jones fluid”,

K. Yasuoka and M. Matsumoto, J. Chem. Phys. 109, 8451 (1998);

Page 4: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Estimating the nucleation rate, J, from the molecular dynamics simulation at T = 80.3K. Supersaturation

ratio = P/Po = 6.8

time volume

] formed embryos phase liquid of # [ J

)s cm 10 ~(J

s cm 10

s) 10 (2.5 10 cm) 10 3.4 (60

embryos 30

-13-22classical

-13-29

-1238-

vol. = ( 60 x 60 x 60) 3;

Page 5: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Nucleation is a non-equilibrium process!

●There is no “first principles” theory from which to determine the nucleation rate. ● Most models attempt to treat nucleation as the decay of a near-equilibrium metastable (supersaturated) state.

● The classical nucleation theory (CNT) model was first developed in 1926 by Volmer and Weber, and by Becker and Döring in 1935 …. following a proposal by Gibbs.

● CNT treats nucleation as a fluctuation phenomenon in which small embryos of the new phase overcome free energy barriers and grow irreversibly to macroscopic size.

Page 6: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Classical Nucleation Theory

(vapor-to-liquid)

Jclassical = [N1 v 4rn*2/3 ] · Nn*

= [Monomer flux] · [# Critical Clusters/Vol.]

Page 7: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Nn / N1 = exp [–Work of formation / kT] Work of formation of cluster from vapor:

W(n) = 4 rn2 - n kT ln S

S = P/Po

Estimating Nn

Page 8: Computer Simulations, Scaling and the Prediction of Nucleation Rates

n* = critical sized cluster

has equal probability of growing or decaying

Page 9: Computer Simulations, Scaling and the Prediction of Nucleation Rates

n* = critical sized cluster

at n = n*: dW(n) / dn = 0 Let

W(n) = An2/3 -nlnS

where A = [36]1/3 liq-2/3 /kT

liq= liquid number density

Page 10: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Volume / Surface in W(n*)

d/dn [ An2/3 - nlnS]n* = 0

(2/3)A n*-1/3 = lnS

n* = [2A/ 3lnS]3

W(n*) /kT = ½ n* lnS

= [16/3] [/(liq2/3 kT) ]3 / [lnS]2

liq= liquid number density

Page 11: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Classical Nucleation Rate

2

liq

3

liq

22/12

oclassical

Sln

kT3

16exp

S

m

2

kT

PJ

(T) a – bT is the bulk liquid surface tension ;

Page 12: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Homogeneous Nucleation rate data for water:classical nucleation rate model has wrong T dependence

log ( Jclassical / cm-3 s-1 )

0 2 4 6 8 10 12

log

( J

/ cm

-3 s

-1 )

0

2

4

6

8

10

12

Wolk and Strey

Miller et al.

Page 13: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Motivation for Scaling J at T << Tc

The CNT nucleation rate depends exponentially on (T)3 / [ln (P/Po(T))]2 . To obtain a physically realistic T dependence of J, a good starting point is to require functional forms for (T) and Po(T) which reflect “universal” properties of surface tension and vapor pressure.

Page 14: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Scaling of the surface tension at T << Tc

Assume a scaled form for :

= o’ [Tc- T]

with =1 for simplicity. Many substances fit this form and the critical exponent (corresponding to ) is close to 1.

1

T

T1

T

T

k

'

kTcc

3/2.liq

03/2

.liq

= excess surface entropy per molecule / k 2 for normal liquids

1.5 for substances with dipole moment(a law of corresponding states result; Eötvös 1869)

Page 15: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Scaled Nucleation Rate at T << TcB. N. Hale, Phys. Rev A 33, 4156 (1986); J. Chem. Phys. 122, 204509 (2005)

2

3

c3

scaled,0scaledSln

1TT

3

16expJJ

J0,scaled [thermal (Tc)] -3 s-1

“scaled supersaturation” lnS/[Tc/T-1]3/2

Page 16: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Water nucleation rate data of Wölk and Strey plotted vs. lnS / [Tc/T-1]3/2 ; Co = [Tc/240-1]3/2 ; Tc = 647.3 K

J. Chem. Phys. 122, 204509 (2005)

lnS

1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2

log

J /(

cm-3

sec-1

)

4

6

8

10

a)

260 K 250 K

240 K 230 K 220 K

Co lnS / [Tc/T -1]3/2

1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2

log

[ J

/ cm

-3 /

sec-1

]

4

6

8

10 Wolk and Strey H2O data

b)

255 K

240 K 230 K

Page 17: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Toluene (C7H8) nucleation data of Schmitt et al plotted

vs. scaled supersaturation, Co = [Tc /240-1]3/2 ; Tc = 591.8K

Co lnS/[Tc/T-1]3/2

2 3 4

log(

J / c

m-3

s-1)

1-

0

1

2

3

4

259K

217K

233K

Jexp (O) Jscaled (+)

Schmitt et al. toluene data b)

lnS

2 3 4

log(

J / c

m-3

s-1)

1-

0

1

2

3

4

259K

217K

233K

Jexp (O) Jscaled (+)

Schmitt et al. toluene data a)

Page 18: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Nonane (C9H20) nucleation data of Adams et al. plotted

vs. scaled supersaturation ; Co = [Tc/240-1]3/2 ; Tc = 594.6K

lnS

2 3 4 5

log(

J / c

m-3

s-1)

1

2

3

4

5

6

259K

217K

233K

Jexp (O) Jscaled (+)

Adams et al. nonane data a)

Co lnS/[Tc/T-1]3/2

2 3 4 5

log(

J / c

m-3

s-1)

1

2

3

4

5

6

259K

217K

233K

Jexp (O) Jscaled (+)

Adams et al. nonane data b)

Page 19: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Comparison of Jscaled with water data from

different experimental techniques: plot log[J/J0,scaled] vs.

J0,scaled 1026 cm-3 s-1

for most materials (corresponding states)

2

3

c3

Sln

1T

T

Page 20: Computer Simulations, Scaling and the Prediction of Nucleation Rates

23.1 [Tc/T -1]3/ (lnS)2

0 10 20 30

- lo

g [

J /

10 2

6 c

m-3

s-1 ]

0

20

D2O, H2O

Wyslouzil et al.

H2O: Miller et al.

H2O: Wolk and Strey

Page 21: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Missing terms in the classical work of formation?

?..

Sln

kT3

16exp

S

m

2

kT

P2

liq

3

liq

22/12

oclassicalJ

2

3

c3

scaled,0scaledSln

1TT

3

16expJJ

Page 22: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Monte Carlo Simulations

Ensemble A: (n -1) cluster plus monomerprobe interactions turned off

Ensemble B: n cluster withprobe interactions

normal

Calculate f(n) =[F(n)-F(n-1)]/kT

Page 23: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Monte Carlo Helmholtz free energy differences for small water clusters: f(n) =[F(n)-F(n-1)]/kT

B.N. Hale and D. J. DiMattio, J. Phys. Chem. B 108, 19780 (2004)

n-1/3

0.0 0.5 1.0

- f

c(n

) / [

Tc /

T - 1

]

0

2

4

6

8

10

12H2O TIP4P clusters

Tc = 647 K exp. values 260 K

280 K300 K

192 20 6 2 n

Page 24: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Nucleation rate via Monte Carlo

Calculation of Nucleation rate from Monte Carlo -f(n) :

Jn = flux · Nn* Monte Carlo

= [N1v1 4rn2 ] · N1 exp 2,n(-f(n´) – ln[liq/1o]+lnS)

J -1 = [n Jn ]-1

The steady-state nucleation rate summation procedure requires no determination of n* as long as one sums over a sufficiently large number of n values.

Page 25: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Monte Carlo TIP4P nucleation rate resultsfor experimental water data points (Si,Ti)

log ( JMCDS TIP4P x 10-4 / cm-3 s-1 )

0 2 4 6 8 10 12

log ( J

/ cm

-3 s

-1 )

0

2

4

6

8

10

12

Wolk and Strey

Miller et al.

Page 26: Computer Simulations, Scaling and the Prediction of Nucleation Rates

23.1 [Tc/T -1]3/ (lnS)2

0 10 20 30

- lo

g [

J /

10 2

6 c

m-3

s-1 ]

0

20

Wyslouzil MC TIP4P

Vehkamaki Hale, DiMattio

MD TIP4P: Yasuoka et al. T = 350K, S = 7.3

Miller et al.

Wolk and Strey

Page 27: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Comments & Conclusions

• Experimental data indicate that Jexp is a function of lnS/[Tc/T-1]3/2

• A “first principles” derivation of this scaling effect is not known;

• Monte Carlo simulations of f(n) for TIP4P water clusters show evidence of scaling;

• Temperature dependence in pre-factor of classical model can be partially cancelled when energy of formation is calculated from a discrete sum of f(n) over small cluster sizes.

• Can this be cast into more general formalism?

Page 28: Computer Simulations, Scaling and the Prediction of Nucleation Rates

Molecular Dynamics Simulations

Solve Newton’s equations,

mi d2ri/dt2 = Fi = -i j≠i U(rj-ri),

iteratively for all i=1,2… n atoms;

Quench the system to temperature, T, and

monitor cluster formation.

Measure J rate at which clusters form