Filled elastomer mechanics and polymer dynamics ...Filled elastomer mechanics and polymer dynamics...

Filled elastomer mechanics and

polymer dynamics modification near surfaces

Julien Berriot PhD ( Michelin)Thomas Chaussée PhD( Dow Corning)Aurélie PAPON PhD ( Solvay)R. Masurel PhDHélène MONTESFrancois Lequeux.

Supported by Solvay : Laurent GUYYves BOMAL

CollaborationD. Long, PSotta (LPMA/ Lyon France)K. Saalwaechter (Halle)J. Oberdisse ( Montpellier France)S. Cantournet ( Ecole des Mines)A. Dequidt (I Chimie, Clermont ferrand )

Soft Matter Science and Engineering

Usually here

This afternoon

Glassy state

Elastomer Polymer chains

Pure elastomer matrix

Linear regime: low strain amplitudes

From

Wang, Rubb. Chem. Technol. 1998

Elastomer + solid fillersParticles

(size

10-100 nm)Polymer chains

Reinforcement is not only an geometrical effect

It reveals a deep modification of the polymer dynamics modification, in the vicinity of solid surfaces

From

Wang, Rubb. Chem. Technol. 1998

Payne effects

1.0

0.5

0.0

-0.5

-1.09876543210

Time (s)

-1.0

-0.5

0.0

0.5

1.0

9876543210temps

Stationary Regime : ~sinusoidal response

Time (s)

Reinforced elastomer

2

3

4

5

678

106

2

G' (

Pa)

0.1 1 10 100 (%)

Si = 0.2

Non reinforced matrix

Non linearities – at small amplitudes – are induced

(Data measured on SBR/Carbon black systems of different filler concentrations, Wang, Rubb. Chem. Technol. 1998 )

Montes H, Lequeux F, Berriot J Macromol.36 8107 (2003)

Payne effects

Non linearities – at small amplitudes – are induced.At Large amplitude the modulus tends towards the one of the matrix(remember Roberto lecture Monday evening)


Mechanical data indicate that the polymer dynamics is modified by the embedding of particles.

This well know results is at the origin of numerous papers.

Bound polymer

Transition layer

Free polymer

Kaufman. J. Polym. Sci. Part A-2

1971, 9, 829 Kenny et al Macromolecules

1991, 24, 436Litvinov et al. Macromolecules

1999, 32, 167

Previous NMR studies have shown that polymer dynamics is slowed down near solid surfaces.

Polymer dynamics near surfaces

There some indications that the “immobilized” ou “bound” polymer may be glassy, indeed by Struik ( 1976) in his book :

The clearest evidence is that filled rubbers age like glasses, above their glass transition.

However this observations was completely forgotten ( or rejected) by the whole community for decades,

Until ~2000

Physical aging in filled elastomers (Struik, Elsevier, 1978)

Glassy polymers at T<Tg

=> At T>Tg existence of glassy domains in a reinforced elastomer

PS at T=Tg-20K

T1 >Tg

T2 < Tgtw1 tw2

Tg

t (s)

Reinforced elastomers at T>Tg

SBR / Carbon black at T = Tg + 20 K, NC =0.4t (s)

T1 >Tg

T3 >Tgtw1 tw2

Tg

J(t)

J(t)

There some indications that the “immobilized” ou “bound” polymer may be glassy, indeed by Struik ( 1976) in his book :

The clearest evidence is that filled rubbers age like glasses, above their glass transition.

However this observations was completely forgotten ( or rejected) by the whole community for decades,

Until ~2000

Substrate

Polymer d

D.S. Fryer, P.F. Nealey, J.J. de Pablo, Macromolecules, 2000, 33

Since 1995 Polymer dynamics is modified in thin films

weakinteraction

T

T

> Tg: elastomer stateT < Tg: glassy state

In agreement withMost of the numerous data on thin films, Tg

in the bulkTg

at a distance z

from a solid surface

z

Tg

T

Log

G’

z

Tg and linear mechanical response

z

Tg

T

Log

G’

z

Tg

in the bulkTg

at a distance z


-There is a huge modification of the polymer dynamics near a solid surface, of great importance in filled elastomer:

We suggested that it is related to Tg Gradient observed on thin films

J. Berriot, H. Montes, F. Lequeux, D. Long, and P. Sotta “Evidence for the shift of glass transition near the particles in silica-filled elastomers” Macromolecules 35 p 9756-9762 (2002 )

Montes H, Lequeux F, Berriot

J «Influence of the glass transition temperature gradient on the nonlinear viscoelastic behavior in reinforced elastomers» Macromol.36 8107 (2003)

use a well controlled system with a huge confinement…

15

well controlled structure, especially particles dispersion various fillers-polymer interactions possible depending on the grafters

EtOEtO

EtOSi

Silica (Ø =30, 50 or 100 nm, volume fraction = 10-30%)

Grafters

Cross-links

PolymerOO

n

Poly(ethyle acrylate) - PEA

C8TES

SiH3CO

OCH3

OCH3

OO

TPM

Non-covalent link between the silica and the polymer

(H bonds between residual Si-OH and the polymer)

Covalent link between the silica and the polymer

Prepare model filled rubbers

or

1,4-butanedioldiacrylate

Présentateur

Commentaires de présentation

Vérifier réticulant

Make series of samples with various volume fraction of silica ( up to 25%, various diameter of silica (30 nm to 100 nm), various grafters, various stabilities)

Measure the structure factor of the silica particles by small angle neutrons scattering

Reconstruct by Inverse Monte Carlo the pair distribution of the silica particles ( with J. Oberdisse)

For each samples

about 10 years of work (initial idea of Lucien Monnerie

in 1995)

z dV(z)

Polymer volume fraction at a distance z

from solid surfaces:

1.0

0.8

0.6

0.4

0.2

0.0Poly

mer

vol

ume

fract

ion

403020100Thickness (nm)z

By inverse Monte Carlo Method /(col; J. Oberdisse)Papon et al Soft Matter

2012

Characterizeprecisely thegeometry from SANS

1H solid NMR Magic Sandwich Echo sequence

Free Induction Decay of protons: sensitive to elastomer/glassy dynamics

MSE sequence: better refocus than classical solid-echo

Col; K Saalwaechter

Measure precisely the dynamics by NMR

20

4

68

0.1

2

4

68

1

Nor

mal

ized

inte

nsity

0.100.080.060.040.020.00Time (ms)

4

68

0.1

2

4

68

1

Nor

mal

ized

inte

nsity

0.100.080.060.040.020.00Time (ms)

4

68

0.1

2

4

68

1

Nor

mal

ized

inte

nsity

0.100.080.060.040.020.00Time (ms)

T

> Tg

+ 40K: « exponential » signal elastomer state

T

< Tg

- 20K: « Gaussian » signal Glassy state

Magic Sandwich Echo sequence: measurement of the Free Induction Decay (FID)

Elastomer classical behavior:

Exponential signal => elastomer part

« Gaussian » signal=> immobilized polymer

5

6

7

8

9

1N

orm

aliz

ed in

tens

ity

0.200.150.100.050.00Time (ms)

T50 C8TES 44%T = Tg + 60K

Filled elastomer NMR response

z dV(z)

5

6

7

8

9

1

Nor

mal

ized

inte

nsity

0.200.150.100.050.00Time (ms)

T50 C8TES 44%T = Tg + 60K

I(t)

= superposition of signals I(z,t)

t

I(z1

,t)

t

I(z2

,t)

t

I(z3

,t)

+

+

…Tg gradient model

Elastomer dynamics modification

.4 ms

(Long D., Lequeux F. EPJE

2001)

Tg gradient model :

z

z

Tg(z)

Tg∞

Tg

in the bulkTg

at a distance z


Tg gradient model analysis

In agreement withMost of the numerous data on thin films,Tg gradient model Long D., Lequeux F. EPJE

2001

Fit of NMR curves between Tg

+ 10K and Tg

+ 120K with a single parameter

= 0,15 nm

1.0

0.8

0.6

0.4

0.2

0.0

Nor

mal

ized

inte

nsity

0.040.030.020.01Time (ms)

Experimental points Tg gradient fit

Tg

+ 10K

Tg

+ 50K

Tg

+ 120K

Tg

+ 30KTg

+ 20K

Tg

at a distance z



This law remains valid in presence of solvent

The gradient of Tg is also valid with the same value of

= 0,15 nm

zTzT gg

1)()(

-80-60-40-20

0

T g()

- T

g(0)

(K)

403020100Solvent weight fraction (%)

16 wt% d-toluene


Differential Scanning Calorimetry

dH/dT

signal

on pure PEA

-1.6

-1.4

-1.2

-1.0

c p (a

. u.)

300280260240220Temperature (K)

PEA

-1.6

-1.4

-1.2

-1.0

c p (a

. u.)


PEA Tg gradient prediction

-1.6

-1.4

-1.2

-1.0

c p (a

. u.)


PEA T50 TPM 30 Tg gradient prediction

shift thanks to theTg

gradient

model, using the NMR parameter

Quantitative prediction of theCalorimetric response Papon at al PRL 2012


- One parameter description fast glassy relaxation near surfaces from Tg to Tg +100K ( as measured by NMR)

-With the same parameter : quantitative prediction of the solvent effect on confinement

-With the same parameter : prediction of the DSC (calorimetric) response


4

68

0.1

2

4

68

1

Nor

mal

ized

inte

nsity

0.100.080.060.040.020.00Time (ms)

Elastomer dynamics modification analysis

In this domain there is a non exponential decay for filled elastomer

0.4ms

12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e

3 components fit eg eimm

e0 Glassy dynamics (Gaussian signal)

Elastomer dynamics (exponential signal)

Intermediate dynamics(stretched exponential)

Use a 3 components model: glassy, elastomer and intermediate

A. Papon et al Macromolecules, 2011, 44, 913.


12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e


e0 Glassy dynamics (Gaussian signal)

=similar to gradient of Tg analysis, seen by DSC


Intermediate dynamics

(stretched exponential)= Not seen by DSC



There is a gradient of glass transition

AND

A broadening of the glass transition (on the low frequency side)

Back to mechanics :

Confined polymer exhibit a different dynamics (~glassy)

Consequences on mechanics ?

z

Tg

T

Log

G’

z

Tg and linear mechanical response

z

Tg

T

Log

G’

z

68

0.1

2

4

68

1

2

S ( q

)

12 3 4 5 6 7

102

qDmean (Å-1)

68

0.1

2

4

68

1

2

S (

q )

12 3 4 5 6

102 3 4

qDmean ( Å-1)

68

0.1

2

4

68

1

2

S (

q )

12 4 6 8

102 4

qDmean ( Å-1 )

Montes et al.

EPJE

2010, 31,3

SA

NS

Nor

mal

ized

elas

ticm

odul

usInfluence of the fillers dispersion

Distance between

particles

controls

the

elastic

modulus

T-dependance

10

8

6

4

2

R( T

,,

)

654321Tg /(T-Tg)

10

8

6

4

2

R( T

,,

)

654321Tg /(T-Tg)

10

8

6

4

2

R( T

,,

)

654321Tg /(T-Tg)

Tg/(T-Tg) is proportionnal

to the glassy layer thickness

Payne effects

1.0

0.5

0.0

-0.5

-1.09876543210

Time (s)

-1.0

-0.5

0.0

0.5

1.0

9876543210temps

Stationary Regime : ~sinusoidal response

Time (s)

Reinforced elastomer

2

3

4

5

678

106

2

G' (

Pa)

0.1 1 10 100 (%)

Si = 0.2

Non reinforced matrix

Non linearities – at small amplitudes – are induced


Montes H, Lequeux F, Berriot J Macromol.36 8107 (2003)

68

0.1

2

4

68

1

2

S ( q

)

12 3 4 5 6 7

102

qDmean (Å-1)

2.0

1.6

1.2

0.8

G' (

MP

a )

0.12 4 6

12 4 6

102

(%)

2.0

1.6

1.2

0.8

G' (

MP

a )

0.12 4 6

12 4 6

102

(%)

2.0

1.6

1.2

0.8

G' (

MP

a )

0.12 4 6

12 4 6

102

(%)

68

0.1

2

4

68

1

2

S (

q )

12 3 4 5 6

102 3 4

qDmean ( Å-1)

68

0.1

2

4

68

1

2

S (

q )

12 4 6 8

102 4

qDmean ( Å-1 )

SA

NS

Mec

hani

cs

PAYNE EFFECT: Influence of the fillers dispersion

What is the range of mechanical interaction ?

Tg/(T-Tg) is proportionnal

to the glassy layer thickness

Payne effect and immobilized polymer ?

Network of particles connected by slow ( and thus non-linear) polymer ?

12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e


e0

Payne effect and immobilized polymer ?

From Structure and NMR compute the network connectivity

for various :

TemperatureSilica diameter Silica volume fraction

Payn

e ef

fect

ampl

itude

Payn

e ef

fect

ampl

itude

connectivity

connectivity

Notoo low

OK

12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e


e0Glassy dynamics similar to gradient of Tg analysis, seen by DSC



Not seen by DSC, but relevant for mechanics



Conclusion

In nanocomposite , confinements leads to :

- A shift of the glass transition temperature

- A broadening of the glassy dynamics ( for the low frequency)

calorimetry is moslty sensible to the shift of Tg

mechanics is sensible to the broadening

Papon et al Soft Matter

2012

Understanding the dynamics near the particles

Rheology prediction

log

G’ Glassy 1GPa

Rubbery 1MPa

P(log) or P(Tg)

Tg1

Tg3

Tg2

Tg1 >Tg

2 >Tg3

Slide from Peiluo Shi lecture:

Fast Domains

Slow domains

Tg gradient model Long D., Lequeux F. EPJE

2001

Percolation approximation (rigid/soft domains approximation)

Fast Domains

Slow domains

The rigidity is ensured by the percolation of slow domains, propagates neara rigid wall

Melt in the bulk at the time scale considered

Rigid substrate

Glassy

Percolation like propagation near a solid surface

=0.88 (3d percolation exponent for spatial correlation)

/1

1z

TzT gg

Rigid substrate

Beyond the rigid/soft domains approximation:

A. Dequidt lecture

Simulation paper by A. Dequidt et al ( see Friday, presentation of Alain Dequidt)

The range of the confinement effect depends on the measured parameter !!

fast modes : short range, DSC

Slow modes : long range, mechanics

12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e


e0Glassy dynamics similar to gradient of Tg analysis, seen by DSC



Not seen by DSC, but relevant for mechanics



12

8

4

0

Thic

knes

s (n

m)


Tg gradient model e


e0


Finite elements simulations similar to Peiluo SHI, but for confined system (in progress).

Log time

Relaxation modulus

Percolation vicinity in viscoelastic confined systems

Take home message

1)In nanocomposite , polymer confinements leads to a modification of the mechanics near the fillers

2) It can be approximated by a gradient of glass transition, but the range depends on the measured quantity

3) It originates mainly in the dynamical heterogeneities of glasses

Need to develop approaches that do not approximate mechanics by simple stress ( or strain) average.

Filled elastomer mechanics and polymer dynamics ...Filled elastomer mechanics and polymer dynamics...

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Transcript of Filled elastomer mechanics and polymer dynamics ...Filled elastomer mechanics and polymer dynamics...