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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.

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Presentation on theme: "A Prediction of Nanocomposite Permeability from Monte Carlo Simulations and the Implications of the Constrained Polymer Region Sumit Gogia Patrick Kim."— Presentation transcript:

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

2 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

3 Sumit GogiaPatrick KimVincent Yu Applications Food packaging – Prolong shelf life Tennis balls – Prevent depressurization Protective equipment – Reduce thickness

4 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)

5 Sumit GogiaPatrick KimVincent Yu Tortuous path model Two main factors determine the magnitude of the tortuous path – Aspect ratio ( α ) – Volume fraction ( ϕ )

6 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)

7 Sumit GogiaPatrick KimVincent Yu Computer simulation Allows complete control over variables Easily reproducible and verifiable Quicker than gas permeation measurements

8 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

9 Sumit GogiaPatrick KimVincent Yu Monte Carlo simulation

10 Sumit GogiaPatrick KimVincent Yu Monte Carlo simulation

11 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)

12 Sumit GogiaPatrick KimVincent Yu Data

13 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

14 Sumit GogiaPatrick KimVincent Yu Data

15 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

16 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

17 Sumit GogiaPatrick KimVincent Yu Further research Account for more variables in simulations – Clay plate size – Orientation – Incomplete exfoliation Calculate effect of constrained polymer region

18 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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