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

Slides:



Advertisements
Similar presentations
Forging new generations of engineers
Advertisements

Major Concepts Activity 19 Plastics are a part of a group of chemicals known as polymers, which are made of repeating molecules (called monomers) linked.
TAREK A. TUTUNJI Rapid Prototyping. Prototype A prototype can be defined as a model that represents a product or system. This model is usually used for.
Overarching Goal: Understand that computer models require the merging of mathematics and science. 1.Understand how computational reasoning can be infused.
PRINCIPLES OF PRODUCTION ENGINEERING
Length-scale Dependent Dislocation Nucleation during Nanoindentation on Nanosized Gold Islands Alex Gonzalez Department of Mechanical Engineering University.
SIMULATIONS. Simulations are used by engineers, programmers, and other scientists to produce the probable results of an experiment or happening.
Approaches to Data Acquisition The LCA depends upon data acquisition Qualitative vs. Quantitative –While some quantitative analysis is appropriate, inappropriate.
Statistical Models of Solvation Eva Zurek Chemistry Final Presentation.
Reduced Support Vector Machine
Characterization, applications
UW SMG Quantum Mechanics H  = E  1 st Principles Simulations Time Distance femtosec picosec nanosec microsec seconds minutes hours years 1 Å1 nm10 nmmicronmmmeters.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
1 Monte Carlo methods Mike Sinclair. 2 Overview Monte Carlo –Based on roulette wheel probabilities –Used to describe large-scale interactions in biology.
EXPLORING PROPERTIES OF MATERIALS
Rapid prototyping is a computer program that constructs three-dimensional models of work derived from a Computer Aided Design (CAD) drawing. With the use.
Elec471 Embedded Computer Systems Chapter 4, Probability and Statistics By Prof. Tim Johnson, PE Wentworth Institute of Technology Boston, MA Theory and.
1 CE 530 Molecular Simulation Lecture 7 David A. Kofke Department of Chemical Engineering SUNY Buffalo
1 Statistical Mechanics and Multi- Scale Simulation Methods ChBE Prof. C. Heath Turner Lecture 11 Some materials adapted from Prof. Keith E. Gubbins:
1 Physical Chemistry III Molecular Simulations Piti Treesukol Chemistry Department Faculty of Liberal Arts and Science Kasetsart University :
WEEK 2 STRUCTURE OF MATERIALS MATERIALS SCIENCE AND MANUFACTURING PROCESSES.
Chapter 13: States of Matter
Computational NanoEnginering of Polymer Surface Systems Aquil Frost, Environmental Engineering, Central State University John Lewnard, Mechanical Engineering,
Computational NanoEnginering of Polymer Surface Systems Aquil Frost, Environmental Engineering, Central State University John Lewnard, Mechanical Engineering,
Free energies and phase transitions. Condition for phase coexistence in a one-component system:
Stochastic Algorithms Some of the fastest known algorithms for certain tasks rely on chance Stochastic/Randomized Algorithms Two common variations – Monte.
Simulating PEO melts using connectivity-altering Monte Carlo Simulating PEO melts using connectivity-altering Monte Carlo by Collin D. Wick and Doros N.
Molecular Dynamics Simulations of Diffusion in Polymers Zach Eldridge Department of Mechanical Engineering University of Arkansas Fayetteville, AR
High Resolution Models using Monte Carlo Measurement Uncertainty Research Group Marco Wolf, ETH Zürich Martin Müller, ETH Zürich Dr. Matthias Rösslein,
Chapter 13: States of Matter
Topic 6.2 – Collision Theory.  According to the kinetic theory, all matter consists of particles (atoms or molecules) that are in constant motion. 
1 SMU EMIS 7364 NTU TO-570-N Inferences About Process Quality Updated: 2/3/04 Statistical Quality Control Dr. Jerrell T. Stracener, SAE Fellow.
1 Peter Fox GIS for Science ERTH 4750 (98271) Week 8, Tuesday, March 20, 2012 Analysis and propagation of errors.
Monte Carlo Methods Versatile methods for analyzing the behavior of some activity, plan or process that involves uncertainty.
8. Selected Applications. Applications of Monte Carlo Method Structural and thermodynamic properties of matter [gas, liquid, solid, polymers, (bio)-macro-
Outline of Chapter 9: Using Simulation to Solve Decision Problems Real world decisions are often too complex to be analyzed effectively using influence.
EEE 3394 Electronic Materials
1 Everyday Statistics in Monte Carlo Shielding Calculations  One Key Statistics: ERROR, and why it can’t tell the whole story  Biased Sampling vs. Random.
Computer Graphics: Programming, Problem Solving, and Visual Communication Steve Cunningham California State University Stanislaus and Grinnell College.
, Patrik Huber.  One of our goals: Evaluation of the posterior p(Z|X)  Exact inference  In practice: often infeasible to evaluate the posterior.
Understanding Molecular Simulations Introduction
Protein Folding and Modeling Carol K. Hall Chemical and Biomolecular Engineering North Carolina State University.
Aquil Frost, Environmental Engineering, Central State UniversityGraduate Student Mentor: Abishek Venkatakrishnan John Lewnard, Mechanical Engineering,
Validating a Random Number Generator Based on: A Test of Randomness Based on the Consecutive Distance Between Random Number Pairs By: Matthew J. Duggan,
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
Austin Howard & Chris Wohlgamuth April 28, 2009 This presentation is available at
An Introduction to Monte Carlo Methods in Statistical Physics Kristen A. Fichthorn The Pennsylvania State University University Park, PA
Project #6 Surface Modification of Carbon Nanotubes for Property Improvement Cuong Diep – pre-junior, Chemical Engineering Milena Fernandez – pre-junior,
Shibo He 、 Jiming Chen 、 Xu Li 、, Xuemin (Sherman) Shen and Youxian Sun State Key Laboratory of Industrial Control Technology, Zhejiang University, China.
Javier Junquera Importance sampling Monte Carlo. Cambridge University Press, Cambridge, 2002 ISBN Bibliography.
Polymer Properties Exercise Crystallinity Polyethylene is crystalline polymer which forms orthorhombic unit cell, i.e. a=b=g=90ᵒC, where a, b, a.
CfE Higher Supported Study Week 1 Rates of Reaction Bonding in first 20 elements.
States of Matter and Mixtures and Solutions Carl Wozniak Northern Michigan University.
Engineering. What is Engineering & What do Engineers Do? Engineering involves developing innovative solutions to benefit humanity Engineering is essential.
Viscoelasticity.
Sampling Distributions
Chapter Outline 1.1 What is Materials Science and Engineering?
Monte Carlo: A Simple Simulator’s View
Monte Carlo methods 10/20/11.
Engineering Materials: Chemistry, Pollution, and Solutions
Modern Materials And Junk and Stuff.
Chapter 12 – Solids and Modern Materials
Ising Model of a Ferromagnet
Adsorption and Chromatographic Separation of Chain Molecules on Nanoporous Substrates Alexander V. Neimark, Department of Chemical and Biochemical Engineering,
Sponsored ByThe National Science Foundation Grant ID No.: DUE
Jan Genzer (Department of Chemical & Biomolecular Engineering)
Statistics: Analyzing Data and Probability Day 5
Name:________________ Date:_________________ Class Period:___________
Genzer Research Group How does substrate geometry affect the surface-initiated controlled polymerization? Jan Genzer (Department.
Introduction to Sampling Distributions
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

Why Simulations? “Because they provide the freedom to fail!” Cost Time “Assess real-world processes too complex to analyze via spreadsheets or flowcharts” 2 [3] [5]

Research Triangle Simulations Experiment (3) Theory Nature

Programs Used 4 Large-scale Atomic/Molecular Massively Parallel Simulator Visual Molecular Dynamics

General Task Overview 1. Literature Review 2. Generation of Surfaces and Polymers 3. Run Simulations and Analyze 4. Project Deliverables 5

Task 1: Literature Review 6

What Are Polymers? Substances which consist of a large number of repeating units called “monomers” Polymers are used as adhesives, coatings, foams, and packaging materials to textile and industrial fibers, composites, electronic devices, biomedical devices, optical devices, and precursors for many newly developed high-tech ceramics The polymer industry has grown to be larger than the aluminum, copper, and steel industries combined [4]

Physical Properties Of Polymers Are dependent upon interchain and intrachain bonding, nature of the back bone, processing events, presence or absence of the additive, chain size and geometry, and molecular weight distribution. Polymers are semi-crystalline Polymers that crystallize are different in characteristics to equivalent low molecular weight materials. [1]

Energy vs. Distance (2)

Molecular Structure Molecular arrangement of polymers can be compared to that of spaghetti Amorphous organization of molecules has no long- range order or form in which the polymer chains arrange themselves Amorphous polymers are generally transparent, which is an important characteristic for many applications including food wrap, plastic windows, headlight senses, and contact lenses [1]

Monte Carlo Method Dependent on numbers randomly generated within confined spaces ◦ The data from the random generation are averaged in some way Time is irrelevant ◦ Less time is required compared to wet lab ◦ MC Method is not proof that something has happen – it probably will happen [6]

Monte Carlo Method - Area Probability can be used to calculate area! [6] Probability that ½ of dots will land in green section Probability that ¼ of dots will land in green section

Monte Carlo Method - Area The integral of this curve can be found if enough dots are taken and the total area of the rectangle is known. [6] The value of pi can be calculated if enough dots are taken, the total area of the rectangle is known, and the radius of the circle is known. [6]

Monte Carlo Method: “On-Lattice” Random Walks [6]

Monte Carlo Method: “Off-Lattice” Random Walks [6] “Off-Lattice” means it is not based on a lattice ◦ Infinite directions available ◦ Can have diagonals and crossing lines rtements-scientifiques/biologie-cellulaire-et- infection/unites-et-groupes/g5-imagerie-et- modelisation/objectives

How Monte Carlo Method will be Used The creation of polymer chains ◦ “Off lattice” Random Walks The placement of polymer chains When polymer is adsorbing with surface randomly: ◦ Energy,entropy, and distance from surface will be measured for each random placement ◦ These values will be averaged for each simulation

Task 2: Generate Surfaces and Polymers 17

Surfaces Regular Random 18

Testing Surfaces 19 www-ee.ccny.cuny.edu

Using MATLAB to Generate “On- Lattice” Polymer Chains

Using MATLAB to Generate “Off- Lattice” Polymer Chains

Task 3: Run Simulations and Analyze 22

Programs Used 23 Large-scale Atomic/Molecular Massively Parallel Simulator Visual Molecular Dynamics

Polymer Adsorbing onto Surface Polymer is randomly placed around surface while data is taken

Data that might be taken from simulation Entropy – How many options does the polymer have? ◦ At bottom of trough – the polymer is compact  Not many options ◦ At top of trough – the polymer is free to move  A lot of options Energy – What is energy at a certain distance? ◦ Each distance corresponds to a certain energy level

Analysis of Data Analysis will answer questions such as: ◦ What was the average entropy level? ◦ What was the average energy level? ◦ What was the average distance from the polymer to the surface?

Why is this important? Companies may want to know how well there product is adsorbing onto a surface. ◦ Ex. P&G wants to know how well their conditioner is adsorbing onto hair news/pg-introduces-pantene-plant-based-plastic- bottles/

Task 4: Project Deliverables Research Paper Final Poster Final Presentation 28

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 29

Works Cited [1] (2010). “Polymers”, Chemical of the Week, (May 31, 2013). [2] (2010). “Lennard-Jones Potential”,UCDavisChemWiki, (May 31, 2013). _Theory/Intermolecular_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,