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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
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Sumit GogiaPatrick KimVincent Yu 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
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Sumit GogiaPatrick KimVincent Yu Applications Food packaging – Prolong shelf life Tennis balls – Prevent depressurization Protective equipment – Reduce thickness
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Sumit GogiaPatrick KimVincent Yu Tortuous path model Impermeable clay plates create tortuous paths for permeating molecules Nanocomposite is less permeable as a result (Nielsen, 1967)
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Sumit GogiaPatrick KimVincent Yu Tortuous path model Two main factors determine the magnitude of the tortuous path – Aspect ratio ( α ) – Volume fraction ( ϕ )
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Sumit GogiaPatrick KimVincent Yu Constrained polymer model Polymer-clay interactions – May cause phase changes in the pristine polymer – Significant effect observed in amorphous polymers (Adame and Beall, 2009)
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Sumit GogiaPatrick KimVincent Yu Computer simulation Allows complete control over variables Easily reproducible and verifiable Quicker than gas permeation measurements
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Sumit GogiaPatrick KimVincent Yu 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
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Sumit GogiaPatrick KimVincent Yu Monte Carlo simulation
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Sumit GogiaPatrick KimVincent Yu Monte Carlo simulation
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Sumit GogiaPatrick KimVincent Yu Simulation parameters Run on a supercomputing grid over a period of one month Data obtained for and Other parameters ( t is time)
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Sumit GogiaPatrick KimVincent Yu Data
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Sumit GogiaPatrick KimVincent Yu 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-sectional area of a clay plate – is the number of clay plates per volume
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Sumit GogiaPatrick KimVincent Yu Data
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Sumit GogiaPatrick KimVincent Yu 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
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Sumit GogiaPatrick KimVincent Yu Conclusion Established τ as a function of χ χ is more accurate than α ϕ Monte Carlo simulations – Improved efficiency – Feasible
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Sumit GogiaPatrick KimVincent Yu Further research Account for more variables in simulations – Clay plate size – Orientation – Incomplete exfoliation Calculate effect of constrained polymer region
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Sumit GogiaPatrick KimVincent Yu 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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