Computational Nano Enginering of Polymer Surface Systems Aquil
- Slides: 36
Computational Nano. Enginering 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 1
Polymers in the Real World [10] [12] [11] [13] 2
Why Simulations? �“Because they provide the freedom to fail!” • Cost • Time [1] �“Assess real-world processes too complex to analyze via spreadsheets or flowcharts” [2] 3
What can we see? Time Macro Meso Nano Subatomic Size 4
Timeline Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Training Literature Review Create Surfaces Create Polymers Run Simulations Analyze Simulations Work on Deliverables Finish Research Paper Finish Final Presentation Finish Research Poster 5
Programs Used Visual Molecular Dynamics Large-scale Atomic/Molecular Massively Parallel Simulator 6
POLYMER GENERATION 7
What Are Polymers? � Consist of repeating units called “monomers” � Polymer industry is larger than the aluminum, copper, and steel industries combined [4] 8
Polymer Adsorption 9
Using MATLAB to Generate “On. Lattice” Polymer Chains 10
Using MATLAB to Generate “Off. Lattice” Polymer Chains 11
CREATE SURFACES 12
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 13
Surfaces 2. Random, Roughness Factor: 0. 1 Roughness Factor: 0. 9 14
Testing Surfaces www-ee. ccny. cuny. edu 15
Face Centered Cubic with MATLAB 3 rows, 3 columns, Depth of 1 16
Face Centered Cubic with MATLAB 3 rows, 3 columns, Depth of 1 17
Problems? �It’s not that simple! 18
Brownian Fields �Created Using Fractals �Fractals are a mathematical concept: ◦ Self similar with a change of scale (magnification) 19
Brownian Field Uses Fractals �Since Brownian Field has holes or gaps we have simulated a FCC structure using fractals: 20
Surface Area �Using axb = Ia. IIb. Isin(Ø) (Area) we find area between those two vectors. 21
RUN SIMULATIONS 22
LAAMPS File 23
Polymer Adsorbing onto Surface http: //www. technewsworld. com/story/71829. html Polymer is randomly placed around surface while data is take 24
Polymers are Constantly Moving Surface 25
RUN ANALYSIS 26
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 27
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 28
Energy vs. Distance Analysis – “The Sweet Spot” 29
Lennard Jones Potential Equation [2] Energy (v) is a function of distance (r). Interactive Force (Epsilon) Diameter of atom (sigma) 30
Lennard Jones Potential Equation Energy Distance 31
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? ” 32
Example: Conditioner! http: //www. naturalcosmetic news. com/recent-news/pgintroduces-pantene-plantbased-plastic-bottles/ 33
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! 34
What will this save? �Time [7] �Effort [8] �Money [9] 35
Works Cited [1] (2010). “Polymers”, Chemical of the Week, <http: //scifun. chem. wisc. edu/chemweek/polymers. html>(May 31, 2013). [2] (2010). “Lennard-Jones Potential”, UCDavis. Chem. Wiki, <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#monte. Carlo>(May 29, 2013). [4] (2012). “What are Polymers? , MAST, <http: //matse 1. 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, United Kingdom, 1 -6, 48 -67 [7] http: //www. empowernetwork. com/teameaglefreedom/blog/the-clock-is-ticking-tic-toc-tictoc/ [8] http: //emotibot. net/? i=504 [9] http: //www. merchantcircle. com/business/National. Lawsuit. Funding. 302 -7921400/picture/view/3137972 [10] www. idahofamilyvision. com [11] www. plasticstoday. com [12] carterpaintingboulder. com [13] www. pennysimkin. com 36
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