On the understanding of self-assembly of anisotropic colloidal particles using computer simulation methods Nikoletta Pakalidou1✣ and Carlos Avendaño1 1.

Slides:



Advertisements
Similar presentations
PRAGMA – 9 V.S.S.Sastry School of Physics University of Hyderabad 22 nd October, 2005.
Advertisements

OF NANOCOLLOIDAL SYSTEMS
Introduction to Statistical Thermodynamics (Recall)
Anisimov/Sengers Research Group HOW PURE WATER CAN UNMIX Mikhail Anisimov Institute for Physical Science &Technology and Department of Chemical.
Modeling Green Engineering of Dispersed Nanoparticles: Measuring and Modeling Nanoparticle Forces Kristen A. Fichthorn and Darrell Velegol Department of.
Self-Assembly of Colloidal Systems and Its Application Keng-hui Lin 林耿慧 Institute of Physics Academia Sinica.
Statistical Models of Solvation Eva Zurek Chemistry Final Presentation.
The Calculation of Enthalpy and Entropy Differences??? (Housekeeping Details for the Calculation of Free Energy Differences) first edition: p
Entropy of Concentrated Systems. Story begins with a virus that killed tobacco plants Tobacco mosaic virus (TMV)
END-FUNCTIONALIZED TRIBLOCK COPOLYMERS AS A ROBUST TEMPLATE FOR ASSEMBLY OF NANOPARTICLES Rastko Sknepnek, 1 Joshua Anderson, 1 Monica Lamm, 2 Joerg Schmalian,
Thermal Properties of Matter
Forces, Energies, and Timescale in Condensed Matter 2004/10/04 C. T. Shih Special Topics on Soft Condensed Matters.
Continuous Time Monte Carlo and Driven Vortices in a Periodic Potential V. Gotcheva, Yanting Wang, Albert Wang and S. Teitel University of Rochester Lattice.
Advanced methods of molecular dynamics Monte Carlo methods
1 Hydrophobic hydration at the level of primitive models Milan Predota, Ivo Nezbeda, and Peter T. Cummings Department of Chemical Engineering, University.
Introduction to (Statistical) Thermodynamics
Monte-Carlo simulations of the structure of complex liquids with various interaction potentials Alja ž Godec Advisers: prof. dr. Janko Jamnik and doc.
LECTURE 9 CHROMATOGRAPHIC SEPARATIONS The “stuff” you do before you analyze a “complex” sample.
Free energies and phase transitions. Condition for phase coexistence in a one-component system:
Surface and Interface Chemistry  Rheology Valentim M. B. Nunes Engineering Unit of IPT 2014.
1 CE 530 Molecular Simulation Lecture 18 Free-energy calculations David A. Kofke Department of Chemical Engineering SUNY Buffalo
PART 2 ELECTRORHEOLOGICAL SUSPENSIONS. ELECTRORHEOLOGICAL SUSPENSIONS  SUMMARY –Review of electrorheological suspensions (ERS) –Classification of ERS.
10 SOLIDS, LIQUIDS, AND PHASE TRANSITIONS
Ensembles II: 1.The Gibbs Ensemble (Chap. 8) Bianca M. Mladek University of Vienna, Austria
8. Selected Applications. Applications of Monte Carlo Method Structural and thermodynamic properties of matter [gas, liquid, solid, polymers, (bio)-macro-
Surface and Bulk Fluctuations of the Lennard-Jones Clusrers D. I. Zhukhovitskii.
1 M.Sc. Project of Hanif Bayat Movahed The Phase Transitions of Semiflexible Hard Sphere Chain Liquids Supervisor: Prof. Don Sullivan.
Understanding Molecular Simulations Introduction
Ch. 11 States of matter. States of Matter Solid Definite volume Definite shape Liquid Definite volume Indefinite shape (conforms to container) Gas Indefinite.
7. Lecture SS 2005Optimization, Energy Landscapes, Protein Folding1 V7: Diffusional association of proteins and Brownian dynamics simulations Brownian.
Fluid-substrate interactions; structure of fluids inside pores.
Controlled Self-assembly of Colloidal Cobalt Nanocrystals Yuping Bao, Michael Beerman and Kannan M. Krishnan Cobalt Nanocrystals Synthesis BF TEM image.
Introduction. Zn 2+ homeostasis is regulated at the transcriptional level by the DNA-binding protein SmtB. Manipulation of Zn 2+ homeostasis could act.
Inverse melting and phase behaviour of core-softened attractive disks
Collective diffusion of the interacting surface gas Magdalena Załuska-Kotur Institute of Physics, Polish Academy of Sciences.
Monte Carlo method: Basic ideas. deterministic vs. stochastic In deterministic models, the output of the model is fully determined by the parameter values.
Interacting Molecules in a Dense Fluid
An Introduction to Monte Carlo Methods in Statistical Physics Kristen A. Fichthorn The Pennsylvania State University University Park, PA
Monte Carlo in different ensembles Chapter 5
EPSRC Portfolio Partnership in Complex Fluids and Complex Flows Use Of Protein Structure Data For The Prediction Of Ultrafiltration Separation Processes.
Review Session BS123A/MB223 UC-Irvine Ray Luo, MBB, BS.
Generalized van der Waals Partition Function
Monte Carlo methods (II) Simulating different ensembles
Monatomic Crystals.
--Experimental determinations of radial distribution functions --Potential of Mean Force 1.
Effects of Arrays arrangements in nano-patterned thin film media
Corey Flack Department of Physics, University of Arizona Thesis Advisors: Dr. Jérôme Bürki Dr. Charles Stafford.
Extensive  depend on the amount of matter present (such as mass, volume, and the amount of energy of a substance) OR Intensive  do not depend on the.
A Little Gas Problem Ideal Gas Behavior.
On the understanding of self-assembly of anisotropic colloidal particles using computer simulation methods Nikoletta Pakalidou1✣ and Carlos Avendaño1 1.
Non-equilibrium theory of rheology for non-Brownian dense suspensions
Leslie V. Woodcock Department of Physics University of Algarve
Simulation Study of Phase Transition of Diblock Copolymers
Overview of Molecular Dynamics Simulation Theory
ENERGY LOADING AND DECAY OF N2 VIBRATION
物 理 化 學 Physical Chemistry matter logic change study origin
Solids Chem 112.
Effect of Electric Field on the Behaviors of Phase and Phase Transition of Water Confined in Carbon Nanotube Zhenyu Qian, Zhaoming Fu, and Guanghong Wei.
Kinetics of Phase Transformations
Scalar Properties, Static Correlations and Order Parameters
Thermal Properties of Matter
Unit 2 Matter and Temperature.
Recall the Equipartition Theorem: In Ch 6,
Heating Curves & Phase Change Diagrams
The effect of the protein dielectric coefficient and pore radius on the Na+ affinity of a model sodium channel Dezső Boda1,2, Mónika Valiskó2, Bob Eisenberg1,
Computational Materials Science Group
Colloidal matter: Packing, geometry, and entropy
Physical Science Chapter 16
Brownian Dynamics of Subunit Addition-Loss Kinetics and Thermodynamics in Linear Polymer Self-Assembly  Brian T. Castle, David J. Odde  Biophysical Journal 
Thermodynamics and Statistical Physics
Presentation transcript:

On the understanding of self-assembly of anisotropic colloidal particles using computer simulation methods Nikoletta Pakalidou1✣ and Carlos Avendaño1 1. School of Chemical Engineering and Analytical Science, The University of Manchester ✣ nikolettapakalidou@postgrad.manchester.ac.uk Introduction and background Self-assembly is the process in which building blocks, such as colloidal and nanoparticles, self-assembly to form organised structures under specific conditions. This process takes place under equilibrium conditions and is driven by non-covalent interactions such as van der Waals and electrostatic forces. For self-assembled colloidal materials, it is known that the shape of the particles influences the behaviour of the system. To understand the stability of the new materials formed, it is important to understand both thermodynamic and kinetics of self-assembly. While thermodynamic allow us to determine if a system can be formed, kinetics provide information about the time scale required for the process. In this work, we present preliminary work to understand the thermodynamics and kinetics of self-assembly of colloidal suspensions comprised of particles with different shapes. Methodology Particle models The phase diagram for a hard-sphere system is mapped out using Monte Carlo simulation in the NPT ensemble. TI method is applied in order to calculate the free energy of hard spheres: A reversible path connects the system of interest and a reference system (the free energy is known). Two methods are used in performance of the TI method: Widom particle insertion method for a fluid (reference systems: ideal gas, extremely dilute fluid) and Einstein Crystal method for a solid (reference system: non-interacting einstein crystal). Phase diagram and several order parameters are used to detect the position of phase transition for platelets for two degrees of roundness. Rounded square Spherical caps1,4 Hard spheres colloidal plateles2,3 Simulation results Simulation details and theory Phase diagram for hard spheres The question is about the liquid-solid coexistence point. The solution is the determination of hysteresis phenomenon range (from green dotted line to blue one). To this end, the free energy calculations using Widom method is performed. (a) Reference system: very low density (b) Reference system: ideal gas Widom method is applied for hard spheres. Free energy can be calculated using that method for a hard-sphere system. We can use as a reference system either an ideal gas, or a dilute liquid, to create a reversible path between the reference system and system of interest. Phase diagram for rounded square colloidal platelets (roundness 0.25, 0.50) Potential energy of a hard sphere: Simulation details: A Monte Carlo simulation and a Metropolis method is applied for a constant-NPT and a constant-NVT ensemble, for 1372 hard spheres particles. The simulation runs for 250,00 total cycles. For each cycle the average of packing fraction η for each value of dimensionless pressure P* is calculated, and the phase diagrams for each particle shape (hard spheres and platelets) are mapped out. Widom method5: A “ghost” particle is added into a configuration at liquid state. Helmholtz free energy in reduced units F* of the system is calculated to compute the chemical potential for a liquid. (a) Reference system: very low density6 , where (b) Reference system: ideal gas6 Einstein Crystal method7: Each particle of a solid is connected with an harmonic spring λ: for very high density, switch on the springs and switch off the interactions. Helmholtz free energy is computed. ri = center-of-mass position for i ro,i = ideal lattice position for i σ = diameter of the particle Bond order parameters: Global orientational order: Solid Liquid A B Gas Conclusions and future work References Using MC simulation and Metropolis method, the phase diagrams for hard spheres and rounded squares are mapped out. The basic aim is to study liquid-solid phase transition, using both TI (Widom and Einstein Crystal method) and order and global parameters. Simulation for hard spheres agrees well with theory for both liquid and solid. The hysteresis phenomenon is observed and the free energy is calculated using Widom method for liquid phase. Degree of roundness (larger roundness [0.50] is connected with more squared shape than smaller one [0.25]) plays significant role for phase transition, as the parameters indicate. It has been observed that as the density is increased, isotropic phase is transformed into a hexagonal rotator phase (RHX) as a result of the first-order phase transition. In the future, Einstein Crystal code will be structured in order to be applied for TI method for a solid. Also, the range of hysteresis phenomenon an dthe position of interface will be determined. C. Avendaño, C.M. Liddell-Watson, and F.A. Escobedo, Soft Matter, 9:9153 (2013). C. Avendaño and F.A. Escobedo, Soft Matter, 8:4675 (2012). K. Zhao, R. Bruinsma, and T.G. Mason, PNAS, 108:2684 (2010). S. Sacanna and D. Pine, Curr. Opin. Colloid Interf. Sci., 16:96 (2011). B. Widom, J. Chem. Phys., 39:2802 (1963). M. Dijkstra, Physics of Complex Colloids, 184:229 (2013). D. Frenkel and A. Ladd, J. Chem. Phys., 81:3188 (1984). Acknowledge I gratefully acknowledge the funding received towards my Ph.D. from the Faculty of Engineering, The University of Manchester.