2/3/2005 Page 1

Mesoscale Modeling of Organic-Inorganic Hybrid Materials at The Dow Chemical Company

Valeriy V. Ginzburg,Physical Sciences Group, Materials Science Dept.

The Dow Chemical Company

In collaboration with: Jozef Bicerano, Joey Storer, Michael Elwell, Mark Bernius, Robert Cieslinski, Kyle Myers (Dow

Core R&D)

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Introduction

• Dow’s Mission: “To constantly improve what is essential to humanprogress by mastering science and technology”

• Today, Dow is one of world’s premiere chemical and plastics companies

• Materials Science group in Core R&D plays important role in the development of new products and improvement of existing product portfolios

• Modeling – as well as analytical tools and other testing – is a significant contributor to this process

• Today, I will be talking about the role of modeling in the development of new hybrid organic-inorganic materials

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Why Hybrid Organic-Inorganic?• In plastic industry, conventional inorganic fillers have been used for decades:

• Mechanical strength• Flame resistance

• Last twenty years – nanocomposites (mainly polymer-clay)• Significant increase in modulus and strength compared to unmodified polymer• Potential improvement in barrier properties, thermal expansion coefficient, heat

distortion temperature, and flame resistance• Properties critically depend on clay exfoliation and dispersion

• Most recently – polymer/nanoparticle nanocomposites• Primary applications are high-end (electronics, biology, pharmaceuticals)• Inorganic components are primarily coated metal and/or ceramic nanoparticles• Science of these systems is still poorly understood

• Dow – like many other companies in the industry – is actively involved in all these areas

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Understanding Hybrid Materials

Composite Type Conv. Composite Polymer-Clay Nanocomp. Polymer-Nanopart. Mix.

Filler Size > 1 micron H ~ 1 nm; D ~ 0.1--1 micron R ~ 2--20 nm

Filler Metal, ceramics, etc. Silicates (clays) Nanoparticles

Equilibrium? No Maybe Yes

• Various types of hybrid materials require variety of theoretical and experimental techniques to describe their behavior and predict structure-property relationships

• We will concentrate more on “nanocomposite” systems

11µµ 10nm10nm

Dow’s PP/MMT nanocomposite

(J.M.Garces et al., Adv. Mater. 12, 1835 [2000])

Co-nanoparticles in P2VP-PS lamellar block copolymer

(J. Abes, R. Cohen, C. Ross, Chem. Mater. 15, 1125 [2003]).

1010µµ

Polymer/PZT piezoelectric

composite (Univ. of Southhampton, UK)

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Polymer-Clay Nanocomposites

• First successful application – clay in Nylon 6, Toyota group, 1980-s

• Clay platelets (H ~ 1nm, D ~ 30—1000 nm) dispersed in polymer matrix

• Due to high surface-to-volume ratio, most of the material is effectively “interface” (R. Vaia)

• Clay platelets can dramatically improve modulus, strength, heat distortion temperature, and barrier properties compared to pure polymer

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Immiscible Intercalated Exfoliated

Classification of Nanocomposite Morphologies

• Desirable to have “exfoliated” or “intercalated” morphology, so that clay is well-mixed with the matrix

• In practice, clay platelets attract (electrostatic, chemical, van-der-Waals forces) and form stacks (“immiscible” morphology)

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Multiscale Modeling of Polymer/Clay NanocompositesSCFT

Clay chemistry,surfactant chemistry and

architecture,surfactant density and

MW,matrix chemistry and

MW

Effective clay-clay interactions

Clay aspect ratio,organoclay loading

DFT

WAXS and SAXS

TEM

Phase behavior, morphology

FEAMechanical Properties(Young’s Modulus)

Physical Properties(Thermal

Conductivity,CTE)

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Modeling Polymer-Clay Phase Diagrams

n1

n2

R12

V(R12,n1,n2)

• Model system (pictured on the left):• Organically modified clay (φ,D,Leff)• Matrix polymer (P)• End-functionalized polymer (Φ,N,ε)

• Replace the problem with the simpler one of oblate ellipsoids of revolution interacting via long-range potential V(R12,n1,n2) (pictured on the right)• Potential V is the sum of “true” and “polymer-induced” clay-clay interaction

• Polymer-induced clay-clay interaction is calculated using polymer Self-Consistent Field theory(V. V. Ginzburg and A. C. Balazs, Advanced Materials 12, 1805 [2000])

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Self-Consistent Field Theory for Polymer-Mediated Clay-Clay Interaction

H

Immiscible

Intercalated

Exfoliated

Free

Ene

rgy

H, nmFrom: R. Vaia and E. Giannelis, Macromol. 30, 7990 [1997]

Average over lattice configurations and

calculate free energy as function

of HLattice mean-field model developed by Scheutjens and Fleer

• Use lattice SCFT to calculate polymer-mediated interaction between clay platelets• Next Step: Balance the calculated interaction potential with steric and entropic free energy terms

for the clay particles themselves using DFT

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Polymer-Clay Nanocomposites: Phase Diagram Calculation

Clay-Clay Potentials (SCMFT)

Increase inend-functionalizedpolymer adsorption

enthalpy(units: kT)

onto clay surface

Two-Phase (Part. Exf., Part. Stacks)

Isotropic Exf. Aligned

Exf. Aligned Int.

Clay StacksIso. Gel

Dispersion Phase Behavior (DFT)

Immiscible

Exfoliated

Figure taken from: J. Bicerano et al, J. Macromol. Sci.-Polymer Reviews, 44, 53 [2004].

• For given clay type/aspect ratio, our theory predicts phase behavior as function of clay volume fraction and strength of adsorption of end-groups to the surface

• Other polymer-clay systems can be considered using similar approach

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Verifying Dispersion Morphology using WAXSSystem 1: <10% single layers,

the rest are stacks of 20-30 plates each

XRD Intensity

0

5000

10000

15000

20000

25000

30000

0 2 4 6 8

2 Theta, deg

I, ar

b. u

nits

ExperCalc

System 2: ~50% single layers, the rest are stacks of 6-8 plates each

X R D In ten sity

0

10000

20000

30000

40000

50000

60000

70000

80000

0 2 4 6 8

2 T h eta , d eg

I, ar

b. u

nits

ExperC a lc

• XRD fitting model can determine degree of exfoliation in clay dispersions• Results in qualitative agreement with TEM pictures

Model by Vaia and Liu, 2001

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Predicting Mechanical Properties of Nanocomposites using Finite Element Models

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0 1 2 3 4 5 6Clay Loading, Wt %

E(c

omp)

/E(m

atri

x)

No Stacks, Aligned, In-plane

50%Stacks, Aligned, In-plane

100%Stacks, Aligned, In-plane

50% Stacks, Random

100% Stacks, Random

No Stacks, Random

•For randomly aligned disks, no significant dependence of

Young’s modulus on exfoliation•For aligned disks, in-plane

modulus improves with exfoliation

See also: Brune and Bicerano, Polymer 43, 369 (2002);

Gusev, Macromol. 34, 3081 (2001)

• Young’s modulus strongly depends on clay platelet alignment and degree of exfoliation

• Similar methods applicable to predict other properties

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Polymer-Clay Nanocomposite Modeling -- Summary• Implemented multi-scale model correlating nanocomposite

properties with its formulation:• Polymer/Clay Miscibility -- Mean-Field Theory of Polymers• Phase Behavior of Dispersions -- DFT of Colloids• Morphology Verification -- XRD Analysis & Modeling• Property Prediction -- Finite Element Analysis

• Model was applied for:• Design of compatibilizer formulation to improve clay dispersion in

aqueous and other solutions• Estimating mechanical performance of specific nanocomposites

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Polymer/Nanoparticle Mixtures• Clays are still too large to self-assemble together with polymers

without “external help” – can we do better?• Nanoparticles (Au, Ag, carbon, silica, CdS) with R ~ 2—10 nm can

successfully self-assemble together with “templating” polymers (block copolymers or blends) into completely new structures

• Potential applications:• Photonic bandgap materials (E. Thomas, MIT)• Sensors for biological and chemical active agents (S. Asher, Pitt;

C. Mirkin, Northwestern)• Conductive films with nanowires (T. Russell, U-Mass.)• and many others…

• Same question as before: can we model phase behavior of polymer/nanoparticle systems?

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Nanoparticles and Polymers – Recent Experiments

Cobalt-nanoparticles in P2VP-PS lamellar block copolymer (J. Abes, R. Cohen, C. Ross, Chem. Mater. 15, 1125 [2003]). Note the preferential segregation of nanoparticles into P2VP-domains.

Nano-SiO2-particles compatibilize immiscible PP/PS blend (Q. Zhang, H. Yang, Q. Fu, Polymer 45, 1913 [2004]). Addition of particles reduce effective PS-droplet size from 2.5 µm to 0.5 µm.

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Nanoparticles and Polymers – Theory and Simulations

• Dynamics• Hybrid Cahn-Hilliard/Langevin model for nanoparticle/polymer blend

phase separation dynamics (V. Ginzburg et al., PRL 82, 4026 [1999])• Molecular Dynamic model for nanoparticle/polymer blend phase

separation dynamics (M. Laradji et al., 119, 2275 [2003])• Thermodynamics (Particles+BCP)

• Monte Carlo simulations and Strong Segregation Theory for diblock/nanoparticle system (J. Huh et al., Macromol. 33, 8085 [2000])

• General model for multicomponent polymer/nanoparticle mixtures (TGMB Model) (R. Thompson et al., Science 292, 2469 [2001])

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Mixture Rule

Actual Data

Dow Polymer/Metal Nanoparticle Composites for Electronic Applications

• Combining functionalized metal nanoparticles with multicomponent polymer matrix results in new high-dielectric constant materials

• Measured dielectric constant significantly exceeds theoretical (mixture-rule) prediction• This discrepancy indicates likely formation of nanoscale structures that can be

investigated both experimentally (AFM) and theoretically (SCMFT)

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Self-Consistent Mean-Field Theory for Polymer-Nanoparticle Systems – TGMB Formalism

(From: R. Thompson, V. Ginzburg, M. Matsen, A. Balazs, Macromol. 35, 1060 [2002])

• TGMB combines SCFT for polymeric components of the mixture with DFT for spherical nanoparticles• Particle-related free energy contribution includes “non-ideal” factor evaluated using Tarazona-Carnahan-

Starling DFT• Partition functions for polymeric (including solvent) components are evaluated as in “standard” SCFT• Original TGMB paper was for diblock/nanoparticle system; we now expanded the approach to account for

multicomponent polymer systems• Fredrickson-Drolet real-space approach is used to study system morphology• Theory is used primarily for qualitative guidance rather than for quantitative phase diagram prediction

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Comparing AFM Images and SCMFT Simulations

• SCMFT gives qualitative similarity with AFM• Particles are well-dispersed• Particles are localized within the A-rich domains

• SCMFT allows us to qualitatively explore relationship between mixture composition and morphology

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S1 S2 S3 S4 S5 S6 S7 S8 S9 S10S11S12S13S14S15S16S17S18S19S20S21S22S23S24S25S26S27S28S29S30S31S32

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S1 S2 S3 S4 S5 S6 S7 S8 S9 S10S11S12S13S14S15S16S17S18S19S20S21S22S23S24S25S26S27S28S29S30S31S32

Au-nanoparticles

Minority (A) block

Majority (B) block

Experimental AFM Image

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Polymer-Nanoparticle Mixtures -- Summary

• Mixtures of nanoparticles (1nm < R < 20 nm) and multicomponent polymer systems produce various self-assembling equilibrium morphologies useful for variety of applications

• Chemical modification of particles (typically, using short ligandswith preferential affinity to a specific polymer) helps in affecting these morphologies

• Modeling tools such as combined SCFT-DFT (TGMB model) approach offer help in the design of mixture formulations

• Further modeling tasks include going from morphologies to properties (e.g., electrical and optical) – for example, using FEA

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Conclusions• Hybrid organic-inorganic nanomaterials offer substantial promise in

both near and distant future• Medicine• Electronics• New and improved plastics

• Unlike conventional composites, both organic and inorganic components could be equal players in determining self-assembly on a nanoscale

• Theory and simulations can offer guidance to chemists and chemical engineers in terms of selecting formulations to obtain appropriate self-assembled morphologies

• This is relatively new field and much is to be done yet!!!!!

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Thank You!Thank You!