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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. 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

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  1. A Prediction of Nanocomposite Permeability from Monte Carlo Simulations and the Implications of the Constrained Polymer Region SumitGogia Patrick Kim Vincent Yu

  2. 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

  3. Applications • Food packaging • Prolong shelf life • Tennis balls • Prevent depressurization • Protective equipment • Reduce thickness

  4. Tortuous path model • Impermeable clay plates create tortuous paths for permeating molecules • Nanocomposite is less permeable as a result (Nielsen, 1967)

  5. Tortuous path model • Two main factors determine the magnitude of the tortuous path • Aspect ratio (α) • Volume fraction (ϕ)

  6. Constrained polymer model • Polymer-clay interactions • May cause phase changes in the pristine polymer • Significant effect observed in amorphous polymers (Adame and Beall, 2009)

  7. Computer simulation • Allows complete control over variables • Easily reproducible and verifiable • Quicker than gas permeation measurements

  8. 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

  9. Monte Carlo simulation

  10. Monte Carlo simulation

  11. Simulation parameters • Run on a supercomputing grid over a period of one month • Data obtained for and • Other parameters (t is time)

  12. Data

  13. 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

  14. Data

  15. 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

  16. Conclusion • Established τ as a function of χ • χ is more accurate than αϕ • Monte Carlo simulations • Improved efficiency • Feasible

  17. Further research • Account for more variables in simulations • Clay plate size • Orientation • Incomplete exfoliation • Calculate effect of constrained polymer region

  18. 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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