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Computational NanoEnginering of Polymer Surface Systems

Computational NanoEnginering of Polymer Surface Systems. Aquil Frost, Environmental Engineering, Central State University John Lewnard, Mechanical Engineering, University of Cincinnati Anne Shim, Biomedical Engineering, The Ohio State University. Polymers in the Real World. [10]. [11]. [12].

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Computational NanoEnginering of Polymer Surface Systems

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  1. Computational NanoEnginering of Polymer Surface Systems Aquil Frost, Environmental Engineering, Central State University John Lewnard, Mechanical Engineering, University of Cincinnati Anne Shim, Biomedical Engineering, The Ohio State University

  2. Polymers in the Real World [10] [11] [12] [13]

  3. Why Simulations? • “Because they provide the freedom to fail!” • Cost • Time • “Assess real-world processes too complex to analyze via spreadsheets or flowcharts” [1] [2]

  4. What can we see? Macro Time Meso Nano Sub-atomic Size

  5. Timeline

  6. Programs Used Large-scale Atomic/Molecular Massively Parallel Simulator Visual Molecular Dynamics

  7. Polymer Generation

  8. What Are Polymers? • Consist of repeating units called “monomers” • Polymer industry is larger than the aluminum, copper, and steel industries combined [4]

  9. Polymer Adsorption

  10. Using MATLAB to Generate “On-Lattice” Polymer Chains

  11. Using MATLAB to Generate “Off-Lattice” Polymer Chains

  12. Create surfaces

  13. Surfaces 1. Regular, Rough Oscillations in the x direction: 2 Oscillations in the y direction: 2 Amplitude: 0.1 Oscillations in the x direction: 1 Oscillations in the y direction: 1 Amplitude: 0.5

  14. Surfaces 2. Random, Rough Roughness Factor: 0.1 Roughness Factor: 0.9

  15. Testing Surfaces www-ee.ccny.cuny.edu

  16. Face Centered Cubic with MATLAB 3 rows, 3 columns, Depth of 1

  17. Face Centered Cubic with MATLAB 3 rows, 3 columns, Depth of 1

  18. Problems? • It’s not that simple!

  19. Brownian Fields • Created Using Fractals • Fractals are a mathematical concept: • Self similar with a change of scale (magnification)

  20. Brownian Field Uses Fractals • Since Brownian Field has holes or gaps we have simulated a FCC structure using fractals:

  21. Surface Area • Using axb = IaIIbIsin(Ø) (Area) we find area between those two vectors.

  22. Run simulations

  23. LAAMPS File

  24. Polymer Adsorbing onto Surface http://www.technewsworld.com/story/71829.html Polymer is randomly placed around surface while data is taken

  25. Polymers are Constantly Moving Surface

  26. Run analysis

  27. Analysis • In order to receive usable data – all variables must be controlled except one • Independent Variable: • Roughness • Dependent Variables: • Entropy • Energy • Controlled Variables: • Surface Area • Polymer make-up • Surface make-up

  28. Entropy • Entropy – How many options does the polymer have? • At bottom of trough – the polymer is compact - order • Not many options • At top of trough – the polymer is free to move - chaos • A lot of options

  29. Energy vs. Distance Analysis – “The Sweet Spot”

  30. Lennard Jones Potential Equation [2] Energy (v) is a function of distance (r). Interactive Force (Epsilon) Diameter of atom (sigma)

  31. Lennard Jones Potential Equation Distance Energy

  32. What does this analysis tell us? • The extent at which a polymer exists at a certain entropy level • Depends on roughness • The distance that leads to the lowest energy potential • Where is that “sweet spot?”

  33. Example: Conditioner! http://www.naturalcosmeticnews.com/recent-news/pg-introduces-pantene-plant-based-plastic-bottles/

  34. How does this information help us? • In the development of conditioner: • What is the total change in entropy of the conditioner when adsorbing onto hair? • What is the distance from conditioner to hair that achieves the lowest energy level? • If P&G knew these things they could make better conditioner!

  35. What will this save? • Time • Effort • Money [7] [8] [9]

  36. Works Cited [1] (2010). “Polymers”, Chemical of the Week, <http://scifun.chem.wisc.edu/chemweek/polymers/polymers.html>(May 31, 2013). [2] (2010). “Lennard-Jones Potential”,UCDavisChemWiki, <http://chemwiki.ucdavis.edu/Physical_Chemistry/Quantum_Mechanics/Atomic_Theory/Intermolecular_Forces/Lennard-Jones_Potential>(May 31, 2013). [3] (2012). “Solutions: Simulation Software Overview.” Imagine That!, <http://www.extendsim.com/sols_simoverview.html#monteCarlo>(May 29, 2013). [4] (2012). “What are Polymers? , MAST, <http://matse1.matse.illinois.edu/polymers/ware.html>(May 31, 2013). [5] (2013). “Why Simulations?” TATA Interactive Systems, <http://blog.tatainteractive.com/2013/01/why-simulations.html>(May 29,2013). [6] Landau D. P. Binder K. (2000). “Introduction,” “Simple Sampling Monte Carlo Methods ,“Monte Carlo Simulations in Statistical Physics, Press Syndicate of the University of Cambridge, Cambridge, United Kingdom, 1-6, 48-67 [7] http://www.empowernetwork.com/teameaglefreedom/blog/the-clock-is-ticking-tic-toc-tic-toc/ [8] http://emotibot.net/?i=504 [9] http://www.merchantcircle.com/business/National.Lawsuit.Funding.302-792-1400/picture/view/3137972 [10] www.idahofamilyvision.com [11] www.plasticstoday.com [12] carterpaintingboulder.com [13] www.pennysimkin.com

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