A Prediction of Nanocomposite Permeability from Monte Carlo Simulations and the Implications of the ...
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A Prediction of Nanocomposite Permeability from Monte Carlo Simulations and the Implications of the Constrained Polymer Region. Sumit Gogia Patrick Kim Vincent Yu. Introduction. Nanoparticles Generally between 1-100 nm in length High surface area to volume ratio Nanocomposites

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Sumit gogia patrick kim vincent yu

A Prediction of Nanocomposite Permeability from Monte Carlo Simulations and the Implications of the Constrained Polymer Region

SumitGogia

Patrick Kim

Vincent Yu


Introduction

Introduction

  • Nanoparticles

    • Generally between 1-100 nm in length

    • High surface area to volume ratio

  • Nanocomposites

    • Polymers with dispersed nanoparticles

    • Polymer-clay nanocomposites

      • Increased tensile strength

      • Increased elastic modulus

      • Decreased gas permeability


Applications

Applications

  • Food packaging

    • Prolong shelf life

  • Tennis balls

    • Prevent depressurization

  • Protective equipment

    • Reduce thickness


Tortuous path model

Tortuous path model

  • Impermeable clay plates create tortuous paths for permeating molecules

  • Nanocomposite is less permeable as a result (Nielsen, 1967)


Tortuous path model1

Tortuous path model

  • Two main factors determine the magnitude of the tortuous path

    • Aspect ratio (α)

    • Volume fraction (ϕ)


Constrained polymer model

Constrained polymer model

  • Polymer-clay interactions

    • May cause phase changes in the pristine polymer

    • Significant effect observed in amorphous polymers (Adame and Beall, 2009)


Computer simulation

Computer simulation

  • Allows complete control over variables

  • Easily reproducible and verifiable

  • Quicker than gas permeation measurements


Quantifying tortuosity

Quantifying tortuosity

  • Tortuosity

    • is the diffusion coefficient of pristine polymer

    • is the diffusion coefficient of resulting nanocomposite

    • is the distance that a molecule has to travel to diffuse through the nanocomposite

    • is the distance that a molecule has to travel to diffuse through the pristine polymer


Monte carlo simulation

Monte Carlo simulation


Monte carlo simulation1

Monte Carlo simulation


Simulation parameters

Simulation parameters

  • Run on a supercomputing grid over a period of one month

  • Data obtained for and

  • Other parameters (t is time)


Sumit gogia patrick kim vincent yu

Data


Results and discussion

Results and discussion

  • We suggest considering τ as a function of χ, where

    • μ is a geometric factor depending on clay shape

    • s is the cross-sectionalarea of a clay plate

    • is the number of clay plates per volume


Sumit gogia patrick kim vincent yu

Data


Results and discussion1

Results and discussion

  • χ is composed of two main components:

    • Cross-sectional area of clay plates per volume of polymer

    • Average distance travelled by a molecule to get around a clay plate


Conclusion

Conclusion

  • Established τ as a function of χ

  • χ is more accurate than αϕ

  • Monte Carlo simulations

    • Improved efficiency

    • Feasible


Further research

Further research

  • Account for more variables in simulations

    • Clay plate size

    • Orientation

    • Incomplete exfoliation

  • Calculate effect of constrained polymer region


Acknowledgements

Acknowledgements

  • Gary Beall, Texas State University

  • Max Warshauer, Texas State University

  • Siemens Foundation

  • University of Texas at Austin

  • Our families

Further information

Website:code.google.com/p/rwalksim


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