Computational NanoEnginering of Polymer Surface Systems Aquil Frost, Environmental Engineering, Central State University John Lewnard, Mechanical Engineering,

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Presentation transcript:

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 1

2 ws.com/recent-news/pg- introduces-pantene-plant- based-plastic-bottles/ Conditioner!

How do Simulations Help Pantene? Simulation will help discover whether conditioner is sticking onto hair If P&G knew these things they could make better conditioner! ◦ Save time ◦ Save effort ◦ Save money 3

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

Timeline/Tasks 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 6 Large-scale Atomic/Molecular Massively Parallel Simulator Visual Molecular Dynamics

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

Rough Surfaces

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

Surfaces Random, Rough Hurst Parameter 0.9Hurst Parameter: 0.1

Testing Surfaces 16 www-ee.ccny.cuny.edu

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

Fixing the Problem Fractals 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

23

24

RUN SIMULATIONS 25

The Simulation Process In order to receive usable data – all variables must be controlled except one Independent Variable: ◦ Roughness  Sinusoidal Surface – Amplitude and Wavelength  Brownian Surface – Hurst Parameter Dependent Variables: ◦ Entropy ◦ Energy Controlled Variables: ◦ Surface Area ◦ Polymer make-up ◦ Surface make-up 26

LAMMPS Data File 27

LAMMPS Input File Controls and sets parameters for data file such as: ◦ Bond Coefficients ◦ Angle Coefficients ◦ Dihedral Coefficients ◦ Mass 28

Polymer Adsorbing onto Surface Polymer adsorbs onto surface using molecular dynamics while data Is taken. 29

Polymers are Constantly Moving 30 Surface

VMD Image of Polymer Adsorbing onto Surface 31 Image courtesy of Dr. Anderson

RUN ANALYSIS 32

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 33

Energy vs. Distance Analysis 34

Lennard Jones Potential Equation [2] Energy (v) is a function of distance (r). Interactive Force (Epsilon) Diameter of atom (sigma) 35

Lennard Jones Potential Equation 36 Energy Distance

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 37

What will this save? Time Effort Money 38 [7] [8] [9]

Further Applications

Works Cited [1] (2010). “Polymers”, Chemical of the Week, (May 31, 2013). [2] (2010). “Lennard-Jones Potential”,UCDavisChemWiki, (May 31, 2013). olecular_Forces/Lennard-Jones_Potential [3] (2012). “Solutions: Simulation Software Overview.” Imagine That!, (May 29, 2013). [4] (2012). “What are Polymers?, MAST, (May 31, 2013). [5] (2013). “Why Simulations?” TATA Interactive Systems, (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, [7] [8] [9] /picture/view/ http:// 1400/picture/view/ [10] [11] [12] carterpaintingboulder.com carterpaintingboulder.com [13]