The technology vision 2020 Nanoscale science and engineering · Modellazione di mesoscala (insiemi di atomi o molecole) Simulazione di processoSimulation FEM FEM Engineering design

1

Multiscale modelling of nanostructured materials

Maurizio Fermeglia

MoSE - University of Trieste

[email protected]

WWW. MOSE.UNITS.IT

Trieste, 20 A pril, 2010 - slide 3 Maurizio Fermeglia – MO SE - UNITS

The technology vision 2020


Nanoscale science and engineering

Ability to work at molecular level, atom by atom, to create large structures with fundamentally new properties and functions* At least one dimension is of the order of nanometers

Functionality is critically dependent on nanoscale size

Promise of unprecedented understanding and control over basic building blocks and properties of natural and man-made objects*

Theory, modeling and simulation (TMS) Expected to play key role in nanoscale science and technology

McCurdy, C. W., Stechel, E., Cummings, P. T., Hendrickson, B., and Keyes, D.,

"Theory and Modeling in Nanoscience: Report of the May 10-11, 2002, Workshop

Conducted by the Basic Energy Sciences and Advanced Scientific Computing

Advisory Committees of the Office of Science, Department of Energy

Published by DOE

Also available on the web at

http://www.sc.doe.gov/bes/Theory_and_Modeling_in_Nanoscience.pdf

*M. Roco, FY 2002 National Nanotechnology

Investment Budget Request


Meccanica Quantistica (elettroni)

Meccanica molecolare (atomi)

Modellazione di

mesoscala (insiemi di atomi o molecole)

Simulazione di processo

FEM

Engineering design

1Å

Characteristic Length

1nm 1μm 1mm 1m

hears

seconds

nanoseconds

picoseconds

femtoseconds

Quantum Mechanics (electrons)

Molecular Mechanics (atoms)

Mesoscale modeling

(segments)

Process Simulation

FEM

Engineering design

1Å

Characteristic Time

1nm 1μm 1mm 1m

hours

minutes

microseconds

Multiscale Molecular Modeling


Hierarchy of Scales

Polymer morphology;

blend phase

separation; composite

structure

Molecular structure; free energies

of formation, reaction; dipole

moments and other spectroscopic

properties; reaction rates

Critical constants; phase

equilibria; PVT data;

diffusivity, viscosity

mailto:[email protected]

2


Hierarchy of Scales:

nanocomposites

Polymer morphology;

blend phase separation

Information for

building the force field

Information for building

the mesoscopic model;

effect of surface modifier

and its determination

Nanocomposite

mechanical and other

properties


Molecular Simulation vs Theory

Advances in computational hardware and algorithms

Moore‟s law

Gordon Bell Prize: 1Gflop/s in 1988 vs. 27 Tflop/s in 2002

More than four order of magnitude increase in 14 years

Add 2-3 orders of magnitude from parallelizat ion (cheap today)

Costs driven by consumer market


Number of transistors on a

sliver of silicon would double

every two years

Moore‟s law


Molecular Simulation vs Theory

Advances in computational hardware and algorithms

Moore‟s law

Gordon Bell Prize: 1Gflop/s in 1988 vs. 27 Tflop/s in 2002

More than four order of magnitude increase in 14 years

Add 2-3 orders of magnitude from parallelizat ion (cheap today)

Costs driven by consumer market

Costs for experiment? Labor-intensive, high capital costs

Costs for theory?

Labor-intensive 2

Do graduate students and/or lab personnel/equipment improve

by an order of magnitude every five years?


Moore‟s law and Molecular modeling


Algorithms vs Hardware

Monte Carlo algorithms to simulate Ising model

David Landau, U. of Georgia

•1970 •1975 •1980 •1985 •1990 •1995 •2000 •1

•10

•100

•1000

•10000

•100000

•1000000

•1E7

•1E8

•1E9

•1E10

•relative performance

•computer speed

3


Molecular Modeling: traditional approaches

Computational quantum chemistry

Solve Schrödinger equation numerically

Computationally intensive even for small molecules

In principle, yields exact electronic structure and energy as limiting case of increasingly accurate methods (HF, MP2, MP4,…)

Density functional theory (DFT) is approximate but fast

Atomistic simulation

Molecular dynamics Solve dynamical equations of motion for positions, velocities of atoms

Monte Carlo Generate configurations of equilibrium system stochastically according

to know distribution

Both require intermolecular and intermolecular potentials (force fields) as input


Theory Modeling and Simulation advances over past 15 years

Molecular dynamics on as many as billions of atoms

Car-Parrinello and related methods for ab initio dynamics Reactions, complex interfaces,…

Revolution in Monte Carlo methods (Gibbs ensemble, continuum configurational bias, tempering, connectivity alterig, etc) Extraordinarily fast equilibrat ion of systems with long relaxation times

New generation Force Fields transferable force fields: TraPPE

Quantum Monte Carlo methods for nearly exact descriptions of the electronic structures of molecules

New mesoscale methods (including dissipative particle dynamics and field-theoretic polymer simulation) Applicable to systems with long relaxation times and large spatial scales

Finite elements simulations: Gusev at ETH (MatSim)

Hybrid methods (Fraajie)


Molecular Modeling: innovative approaches

Mesoscale simulation

Dissipative Particle Dynamics – Mesoscopic Particle based model Solve dynamical equations of motion for positions, velocities of beads in the Langevin

dynamics

Mesoscale Dynamics (MesoDyn) - Mesoscopic Field based model Dynamic mean-field density functional theory coupled with Langevin equation of

motion

Both require the definition of beads and bead interaction energies

Flow and continuum mechanics models

Solve PDA and continuum models including hydrodynamic description and phenomena occurring at macroscopic level Finite elements simulation

Starting from a grid definition

To be applied for nano-materials a detailed description of the properties at mesoscale level is needed


What is Molecular Simulation

Molecular simulation is a computational “experiment” conducted on a molecular model

Could be a single molecule (computational quantum chemistry)

Could involve O (10) – O (106) molecules

Computational quantum chemistry generally provides results for isolated or pairs of molecules Geometry

Thermochemistry

Frequencies

Anything associated with electronic structure


What is Molecular Simulation?

Molecular dynamics provides results for a system of molecules undergoing dynamic (deterministic)

motion Generates many configurations which are

averaged to provide measurements

Forces are used to evolve the system

The system is dynamic !!!

Alder e Wainwright 1957 - 1959

Monte Carlo provides results for a

system molecules undergoing stochastic motions Generates an ensemble average with no

element of time

Variation of energy is computed

1953 - Metropolis et al. in Los Alamos


Simulation Force Fields

Intramolecular forces

4


Simulation Force Fields

Intermolecular forces


'

00

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3210

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3210

321000

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00'

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0

2

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33

0

22

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11

4

04

3

03

2

02

4

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03

2

02

'cos

3cos2coscos

3cos2coscos''

3cos2coscos

'

3cos12cos1cos1

''

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c

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K

VVV

VVVbb

VVVbbbbF

FbbbbFK

VVV

HHH

bbKbbKbbK

b

bb

b

b bbb

bpotE

ji ij

ji

elecr

qqE

60

90

32ij

ij

ij

ij

jiijvdW

r

r

r

rE

Force Field: COMPASS


Definition of mesoscale

Does “mesoscale” have a precise meaning?

… has to do with equilibration

All the degrees of freedom

Of the smaller scale down

When seen from THAT scale

i.e. they have a relaxation time which is much shorter

than the time scale of interest

Generic definition: any intermediate scale at which

the phenomena at the next level down can be regarded as

always having equilibrated

at which new phenomena emerge

new phenomena with their own relaxation times


Molecular Dynamics Dissipative Particle Dynamics

ForceField based calculations

Soft potentials calculations

Fi = f (aii, aij, …, rc )

From atoms … to beads


Mesoscale Modelling Techniques


Continuum mesoscale methods: particle based methods

Diffusive and hydrodynamic particulate methods

Dissipative particle dynamics (DPD)

It is possible to coarse – grain a system without moving into a lattice

5


DPD: Equations of motion Dynamics: an extension of MD, with a short-ranged, soft, pair-wise force law

i and j label DPD „beads‟, and a ij is an interaction parameter between „beads‟ of type i and j

Polymeric materials are modeled by connecting beads by harmonic springs


Continuum mesoscale methods: free energy functional methods

An alternative to particulate or lattice techniques: MESODYNE

Evolution of the distribution of the order parameter, i.e.

densities or orientations

Free energy includes both ideal contributions (from non-

interacting parts of system) and non-ideal terms (representing interactions)

The intrachain correlations is described by a Gaussian chain

model is used because it allows a factorization of the interactions

hence is computationally very efficient

The noninteracting Gaussian chains are hence the ideal

system, interchain, i.e., non-bonded, interactions are treated as non-ideal

Interchain reactions enter into the effective external potential


Mesodyn Approach


Aqueous Pluronics L64 structures PEO-PPO-PEO block copolymers

Triblock Poly(ethylene oxide)-Poly(propylene oxide)

nonionic surfactant

used in detergency, dispersion stabilisation, lubrication, drug delivery etc

Predicted mesosphases: (a) (70% ) lamellar

(b) (60%) bicontinuous

(c) (55%) hexagonal

(d) (50%) micellar

Excellent agreement with experiments.

Same parameters also give correct predictions for other Pluronics.


Input parameters for Mesoscale simulation (MesoDyn or Dissipative Particle Dynamics)

The parameters for MesoDyn are 1. the bead size and Gaussian chain architecture

2. the bead mobilities M,

3. the effective Flory-Huggins interactions ij

Obtained by molecular modeling tools: bead size and Gaussian chain architecture: by MD

from characteristic ratio (C) in terms of Kuhn length

mobility: by Molecular Dynamics

Bead self diffusion coefficients

FH interactions: by Molecular Dynamics

Differences in non bonded energies between bulk and isolated

chain

Coslanich A, Fermeglia M, Ferrone M, Martinelli L, Pricl S, FOMMS 2003 and Cimtec 2004


Rapporto caratteristico

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200

N

Cin

f(%

)

Parameter 1: bead length, chain architecture

Molecular dynamics NPT runs on homo polymers C∞ calculation for the three

species

Monomer length

Kuhn lenght

Chain architecture

Polymer C∞ at 298 lk nm

PMMA 6.5

(exp 6.0-9.0)

2.259

PC 7.95

(exp 8.10)

8.370

22

0

2 NLnlCr

NLr max

6


Parameter 2: Bead self-diffusion coefficients

Molecular dynamics NPT runs on the polymers Slope of the MSD vs. time

Einstein equation D = b-1 M

Bead self-diffusion coefficient is necessary Input to MesoDyn

Conversion of the mesoscopic dimensionless time step (t ) to an effective time scale

t is the adimensional time step (integration algorithm)

h is the grid dimension

tMh 21bt

Self Diffusion coeff. 1543.1h

lk

Fermeglia M., Pricl S., Chem. Eng. Comm., 1267, 2003

N

i

iit

rtrdt

d

ND

1

2)0()(lim

6

1

MSD vs Time (LDPE)

y = 0,0388x + 3,2678

0

5

10

15

20

0 100 200 300 400

Time [ps]

MS

D [

A2]


Parameter 3: F-H interaction parameter chi

Molecular dynamics NPT and NVT runs

monmix V

RT

E

c

mix

coh

pure

coh

pure

cohmix

V

E

V

E

V

EE

2

2

1

1

isolated

nb

periodic

nbcoh EEE

Fermeglia, M. and Pricl S., AIChE J., 2619 (45), 1999


From Mesoscale to macroscale •Mechanical Prop. •Swelling •Thermal Prop.

•Electrical prop. •Transport prop. •…

•Component properties •Interactions

•Mesoscale parameters

Molecular Modeling

•Morphology – Phase behavior •Composition and density •Geometry of inclusions •Distribution of inclusions

Finite Elements Modeling

Mesoscale Modeling


Dissipative Particle Dynamics

Soft potentials calculations

Fi = f (aii, aij, …, rc )

Characteristic

dimension of

mesoscopic system

Micro - FEM Simulation

FEM Analysis:

Macroscopic properties

From beads … to micro


Fixed and variable grid Micro FEM

Fixed grid

Mesoprop

Variable

grid Palmyra

Mesoscale

DPD Mesodyn

Atomistic

MD o MC

Physical Prop.

Physical Prop.


Fixed and variable grid micro FEM

Interfacing Palmyra and MesoDyn Palmyra: each finite element = one phase , with property tensor Pi

MesoDyn: each element contains mixture of phases, conc. Ci

Geometry: map MesoDyn cubic elements to Palmyra tetrahedrons

Laplace equation is solved for electric conductance, diffusion and permeability

Local deformation allow the calculation of mechanical prop

7


Variable Grid micro FEM

MATSIM Palmyra

Variable grid


Barrier effect

MMT: prediction of permeability P=DK D=diffusivity, K=solubility

Important factors (A.Gusev,

H.Lusti, ):

Aspect ratio a

a = diameter/height quat

The larger the diameter, the longer the O2 path

Per cent of MMT in the system

Permeability

P/P0= realtive permeability

x=af

FEM simulation (Palmyra)

Examples and applications


Nano-structured materials


Automotive rear lamps

Rear lamp of the FIAT Idea

Different possibilities: PC/ABS blends are promising

PC: transparency, good mechanical properties

ABS: reflecting properties, lower viscosity

Polymer Clay nanocomposite Improve Mechanical properties

EU directive for the end-of-life treatment of vehicles (2000/53CE) before 2015 95% of industrial plastic scraps have to be recycled in all vehicles.

Side view Front view

Year 2006

Material recycle

Energy recovery

Material landfilled

Year 2015 (predicted)

Material recycle

Energy recovery

Material landfilled


Polymer – Layered Silicate Nanocomposites

Montmorillonite

~ 1

nm

100 - 500 nm

8



Intercalation vs Exfoliation

D.R. Paul et al., Polymer 49 (2008) 3187-3204



Organic Modifiers

~ 1 nm



Organic Modifiers 2 ÷ 4 nm

Quats


Modeling and Simulation Methods

1 nm 10 nm 100

nm

Molecular

Dynamics

(MD)

1 μm

~ 1 nm

~ 3 nm


Multicarbon layers of quats

The quat layer is thicker for larger quats

M3C6 -

smallest

M2(C18)2 -

largest


Predicted Binding energy vs. quat volume V

()

()

(∆)

6MMT/nylonbind

E

6/quatnylonbind

E

MMT/quatbind

E

9


(nylon-quat) + (nylon-MMT) binding energies vs. quat volume V


The role of alkyl tails on exfoliation

Decreasing platelet-platelet

attraction Increasing access for nylon-

silicate contacts Decreasing no. of nylon-alkyl

interactions Fornes T.D., Hunter D.L., Paul D.R., Macromolecules 2004, 37, 1793-1798


The role of alkyl tails on exfoliation TEM photomicrographs nylon 6 - MMT effect of compatibilizer A M4

B M3 (C18)

C M2 (C18)2

Fornes T.D., Hunter D.L., Paul D.R., Macromolecules 2004, 37, 1793-1798

Un-exfoliated clay

Well-exfoliated clay

Un-exfoliated clay +

dispersed platelets


Influence of alkyl tail number Tail Number - Binding PM

0.00

0.01

0.02

0.03

0.04

0.05

0.06

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

M2(C18)2 M3C18

Tail Number - Binding PQ

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

Tail Number - Binding PM

0.00

0.01

0.02

0.03

0.04

0.05

0.06

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

Scocchi G., 2009, Multiscale S imulation of Polymer-Clay Nanocomposites, phd Thesis, University of Trieste


Influence of alkyl tail length

Tail Length - Eff. Binding

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

M3C12 M3C18

Tail Length - Binding PM

0.00

0.01

0.02

0.03

0.04

0.05

0.06

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

Tail Length - Binding PQ

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)




Influence of hydroxyethyl groups

EtOH - Binding PM

0.00

0.01

0.02

0.03

0.04

0.05

0.06

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

MC18(EtOH)2 M3C18

EtOH - Binding PM

0.00

0.01

0.02

0.03

0.04

0.05

0.06

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

EtOH - Binding PQ

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

PA6 PET PP PE

Polymer

Bin

din

g E

ne

rgy

(k

ca

l/m

ol*

a)

10


Thermodynamics and spacing NVT Molecular Dynamics simulations for calculating the binding energies in

polymer-surfactant-MTM systems

NPT Molecular Dynamics simulation for predicting the equilibrium spacing (basal spacing) between the

MTM platelets as a function of surfactants

Toth R., Coslanich A, Ferrone M, Fermeglia M, Pricl S, Miertus S, Chiellini E, Polymer, 45: 8075 -8083 (2004)


Simulated basal spacing

System Calc. (Å) Exp. (Å)

MMT/20A M2(C18)2

24.9 24.2

MMT/30B MC18C2OH 19.3 18.5

MMT standard cation-exchange capacity CEC = 90 meq/100g

20A 30B smaller than 20A



1 nm 10 nm 100

nm

Molecular

Dynamics

(MD)

1 μm

~ 1 nm

~ 3 nm



1 nm 10 nm 100

nm

Molecular

Dynamics

(MD)

1 μm

~ 1 nm

~ 3 nm



1 nm 10 nm 100 nm

Molecular

Dynamics

(MD)

Dissipative Particle

Dynamics (DPD)

1 μm

~ 1 nm

~ 3 nm



1 nm 10 nm 100 nm

Molecular

Dynamics

(MD)


Dynamics (DPD)

1 μm

~ 1 nm

~ 3 nm

~ 15 nm

11


Beads definition for M3C18:

ijijjjjjiiii

tot

sysEnEnEnE 2

From MD to DPD

Calculation of DPD parameters from MD (method 2)

For the system PA6 – quat – MMT

MD simulations NVT (500ps – 298 K - 10 conformations)

Estimation of Binding Energies

Calcualtion of energy per bead aij

Scocchi, Posocco, Fermeglia &

Pricl, J. Phys. Chem. B,

(2007), 111, 2143


DPD simulation for PCN

Calculation of DPD parameters from MD (method 2) For the system PA6 – quat – MMT

MD simulations NVT (500ps – 298 K - 10 conformations)

Estimation of Binding Energies

Calcualtion of energy per bead aij

DPD model to predict morpholgy of PCN stack Basal spacing from MD (or from

experiments)

Density distribution between the platelets is calcualted

Morphology is transferred to microFEm (variable grid) Calulation of mechanical properties,

barrier properties,…

Scocchi et al., Fluid Phase

Equilibria, (2007), 261, 366



1 nm 10 nm 100

nm

Molecular

Dynamics

(MD)


Dynamics (DPD)

1 μm

~ 1 nm

~ 3 nm

~ 15 nm



1

nm

10 nm 100

nm

Molecular

Dynamics

(MD)


Dynamics (DPD)

1 μm

~ 1 nm

~ 3 nm

~ 15 nm

~ 120 nm



1 nm 10 nm 100 nm

Molecular

Dynamics

(MD)


Dynamics (DPD)

1 μm

~ 1 nm

~ 3 nm

~ 15 nm

~ 120 nm

Finite Elements

Method (FEM)

~ 0.3 μm


T. D. Fornes, D. L.Hunter, D. R. Paul, Macromolecules 37 (2004) 1793-1798

Property calcualtion for the PCN

Properties calculated

Young modulus

Barrier properties

Scocchi et al., Fluid Phase

Equilibria, (2007), 261, 366

12


microFEM simulation for PCN Different degree of exfoliation

1.9 vol % MMT

4.5 W % MMT


PA6 – M2(C18)2 – CEC90

exp

1.20

1.25

1.30

1.35

1.40

1.45

1.50

1.55

1.60

1.65

1.70

A B C D E F

Decreasing Exfoliation Degree >>>

E/E

m

Cation Exchange

Capacity: 90

meq/100g

M2(C18)

2

PA6

T .D Fornes et al., Polymer 42 (2002) 5915

Scocchi G., 2009, Multiscale Simulation of Polymer-Clay Nanocomposites, phd Thesis, University of Trieste


PP – M2(C18)2 – CEC90

M2(C18)

2

exp

1.60

1.65

1.70

1.75

1.80

1.85

1.90

A B C D E F


E/E

m

PP

M. T . Ton-That et al., Polym Eng Sci 44 (2004) 1212

Cation Exchange

Capacity: 90

meq/100g



PP – M2(C18)2 – CEC120

M2(C18)

2

PP

exp

1.40

1.50

1.60

1.70

1.80

1.90

2.00

A B C D E F


E/E

m

C. Deshmane et al., Mat. Sci. Eng. 458 (2007) 150

Cation Exchange

Capacity: 120

meq/100g



Model performance

Mechanical properties Young module (E / E°)

Loading of MMT 4.6 wt %

The model … Effect of the surface modifier

Cation Exchange Capacity considered in the model

Degree of exfoliation is considered in the model

Good agreement with experiments

System Calcualted

data E/E°

Exp. Literature

data E/E°

Exp. Project

data E/E°

CEC

meq/100g

PA6 – C20A 1.53 1.55 - 90

PA6 – M3C18 1.68 1.71 - 90

PA6 – C30B 1.65 1.63 1.63 90

PP – PpgMA – C20A 1.55 1.53 1.44 90

PP – PpgMA – C15A 1.52 1.51 1.44 120


… also for ABS - MMT Previous scale results and experiments:

ABS with MMT creates domains: MMT sheets packed in layers (spacing ≈ 3nm)

Only SAN enters MMT layers creating “stacks”

Islands of rubber phase outside the stacks

SAN

(1)

The stack

(2)

The bulk

Young‟s module of B-SAN and SAN from previous simulations**

2 FEM simulations (total MMT is 2%):

1) The stack (MMT platelet in SAN matrix)

2) The complete nanocomposite (stacks into a matrix of rubber islands + SAN)

Young‟s modulus of the stack

is obtained form 1° s imulation

stack

**MMT Young modulus from A.R. Pawley et.al., American Mineralogist (2002) 87, 1172–1182 *H.A. Stretz et al. / Polymer 46 (2005) 3818–3830

P. Cosoli, G. Scocchi, S. Pricl, M. Fermeglia, Microporous and Mesoporous Materials, 107: 169-179, 2008. .

13


… also for ABS MMT – system.

Young Modulus [GPa] 3,15

(2,416 Young‟s modulus of the blend)

Bulk Modulus [GPa] 4,42

Poisson ratio 0,38

Dependence of E/E0 over aspect ratio

0

2

4

6

8

10

12

14

16

0 50 100 150 200

a

E/E

0 .

Results of mechanical properties

Results are in an acceptable range (H.A. Stretz et al. / Polymer 46 (2005) 3818–3830)

Influence of aspect ratio and loading

E does not change if a>70 Aspect ratio a=Ø/l;

E0=E bulk(=E blend); E=E nanocomposite

x=const

The behavior is linear

E/E0 dependence over MMT % (x)

0

2000

4000

6000

8000

10000

12000

0 1 2 3 4 5 6

x

E/E

0.

x=%MMT

P. Cosoli, G. Scocchi, S. Pricl, M. Fermeglia, Microporous and Mesoporous Materials, 107: 169-179, 2008. . Trieste, 20 A pril, 2010 - slide 75 Maurizio Fermeglia – MO SE - UNITS

Software used

Atomistic simulationa

Material Studio 4.x – Discover

FF: COMPASS

Mesoscale simualtion

Material Studio 4.x – DPD

Material Studio 4.x – MEsodyn (ABS)

microFEM

Material Studio 4.x – Mesoprop (fixed grid)

MATSIM Palmyra (variable grid)


Nano-structured materials


MULTISCALE SIMULATION OF HYBRID ORGANIC-INORGANIC (O/I) NANOCOMPOSITES

DESIGN OF NANOFILLED MULTIFUNCTIONAL MATERIALS BY MULTISCALE SIMULATION

Automotive industry: rear/front lamps with LED technology

Fiat Automotive Industry, Italy Cima Nanotech, Israel SOL-GEL, Israel Fraunhofer Institute Bremen, Germany

University of Padova, Italy E. Hala Laboratory of Thermodynamics, Czech Republic Neotech, Germany Dymax, Germany

1. Refractive index improvement with ZnS and TiO2 nanoparticles

2. Mechanical properties

Industrial Application


I-O composite materials

Type I

Type II

Semi, Full, Covalent Interpenetrating Network (IPN)


Atomistic simulation for the GPTMS matrix: improvement of refractive index

Single molecules of hydrolyzed GPTMS

Bond analysis and individuation of reactive

sites

Creation of a new bond and elimination of the water molecule from the system

(3-Glycidoxypropyl)trimethoxysile (GPTMS)

14


Filmato

Atomistic simulation for the GPTMS matrix: reactive MD

Geometry optimization

Molecular dynamics

Bond analysis Individuation of reactive sites of

condensation (max 3 each step)

Creation of a new Si-O-Si bond

Elimination of the water

molecule from the system


Mechanical Properties - Young’s modulus E 2.1 GPa (1- 4 GPa) @ 0.93

a (-) E calc (GPa)

E exp (GPa)

0.30 0.028 0.024

0.50 0.063 0.072

0.70 0.880 0.910

0.90 2.066

0.93 2.103

Young modulus as function of a

Experiments from Guglielmi, Martucci, Brusatin,

UNIPD


The hybrid O/I system with ZnS

Atomistic simulation for estimating mesoscale parameters


Rings

0 4 8 12

Pro

babili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Atomistic simuls

DPD

Mesoscale parameter estimation: architecture

ZnS Nanoparticles Covered by MPTMS

“Effectively” as ICOSAHEDRON (13 DPD

Particles)

GPTMS representation

Beads of 4-8 and 12 rings

ZnS+MPTMS vs. Particle Representation of GPTMS Networking

DPD

4-Ring (4 GPTMS)

8-Ring (8 GPTMS)

DP

D

DP

D

12-Ring (12 GPTMS)

DPD DPD

DPD


Mesoscale parameter estimation: interactions

5 independent configuration for each system

Energy calculation average of trajectories

BAtot

AB EEEE int


Mesoscale: 2% nanoparticle loading

Reactive DPD mesoscale simulation results DPD combined with fractional particle approach

ZnS covered by 25% MPTMS GPTMS not visible

Density field (left) isodensity surface (right)

M. Lisal, J. K. Brennan, W. R. Smith, J. Chem. Phys. 125, 16490

(2006).

15


Fixed grid MicroFEM simulation

GPTMS-MPTMS-ZnS system

Mechanical properties as function of volume loading NP = 15%

Calc Exp Calc Exp

Volume loading (%) 2 15

Young’s modulus (GPa) 4.08 4.20 9.25 10.3

Poisson ratio (-) 0.265 0.257

Shear modulus (GPa) 1.59 3.6

Bulk modulus (GPa) 2.9 6.4

Density (g/cm3) 1.411 1.782

Matrix Young’s Module: 2.10 GPa at a =o.93

Experiments from Guglielmi, Martucci, Brusatin,

UNIPD Trieste, 20 A pril, 2010 - slide 87 Maurizio Fermeglia – MO SE - UNITS

TiO2 - anatase


Software used

Atomistic simulationa

Material Studio 4.x – Discover

FF: COMPASS

Scripting in MS

Mesoscale simualtion

Material Studio 4.x – DPD

Reactive DPD (developed within MULTIPRO)

microFEM

Material Studio 4.x – Mesoprop


Self-Assembly of Nanoparticle Mixtures in

Lamellar Diblock Copolymers Experimental evidence (Chiu et al. JACS 2005)

Polymer: poly styrene-b-2 vinyl pyridine

Figure 1: PS coated gold nanoparticles: PSD distribution

Figure 2a: PS-PVP with PS coated nanoparticles: nanoparticles segregates in the center of the PS domains

Figure 2c: PS-PVP with particles coated with PS-PVP: nanoparticles are located mainly at the interfaces.

Figure 1 Figure 2a Figure 2c

Chiu, J.J, Bumjoon J.K., Kramer E.J. Pine, D.J., JACS, 2005, 127, 5036


The system: symmetric diblock copolymer poly(styrene-b-2 vinyl pyridine) (PS-PVP)

Nanoparticles covered by PS or PVP or mixture of PS and PVP icosahedral structure

neutral, central DPD bead

surrounded by 12 DPD beads

of type A (PS), or B (PVP)

A and B at the vertex of the

icosahedron

90 Fermeglia, M., Posocco P., Maly M., Pricl S., IEC, (2008)


Lamellae: A or B covering

Repulsive forces between

nanoparticles and corresponding blocks weak and identical aAA=25

and aAB =39.84

remarkable tendency to segregate to the center of the corresponding

domain;

perfect agreement with the

corresponding experimental evidences.

91

16





and aAB =39.84


domain;



92 Trieste, 20 A pril, 2010 - slide 93 Maurizio Fermeglia – MO SE - UNITS




and aAB =39.84


domain;



93

Segregation inside the domains

Enthalpic positive effect (similar interactions);

Entropic positive effect: the chains

can accommodate the particles without substantial deformations.

The particle localization results in a

decrease of their translational entropy;

Trieste, 20 A pril, 2010 - slide 94 Maurizio Fermeglia – MO SE - UNITS 94

Lamellae: equal mixture of homogeneous A and B covering (A6B6(h))

An opposite trend: the

nanoparticles locate at the interfaces between the A-B

blocks Same if the particle is only covered by A (other

beads are neutral)

Trieste, 20 A pril, 2010 - slide 96 Maurizio Fermeglia – MO SE - UNITS 96

Lamellae: excess of A or B covering A1B11

A particle distribution utterly similar to the case of full A

(or B) coverage is obtained.


Lamellae: the hybrid behavior: A3B9(h) and A3B9(r)

The maxima of the nanoparticle density are located between the centers of the compatible lamellas and the block interfaces. Same if the particle is only covered by A (other beads are neutral)

97 Trieste, 20 A pril, 2010 - slide 98 Maurizio Fermeglia – MO SE - UNITS 98

Cylinders: A and B covering only: 5% loading

Particles segregate in the middle of the domain (similar as

the lamellae case)

17


Cylinders: A and B covering with 3% loading

Decreasing the particle loading much less perturbation

of the cylindrical geometry (particles in the middle of the domains)


Cylinders: equal mixture of homogeneus A and B covering (A6B6(h)): 3% loading

The morphology is preserved

Nanoparticles location at the interface.

100


Cylinders: equal mixture of homogeneus A and B covering (A6B6(h)) 5% loading

Destruction of the hexagonal geometry of the matrix, well-oriented lamellar morphology, particles are ultimately segregated at the block interfaces


One single A-type bead is sufficient to lead all particles to

locate themselves at the interfaces between the blocks.

Opposite to the lamellar case

102

A1B11 B12

Cylinders: excess of A or B covering (A1B11)


Compatibility of CNT with polymers

MD simulation FH parameter for polymers

NCT and NPT simulation for CNT

De bundling energies Estimating FH parameter for

CNT

Experimental data for CNT-Poly(m-phenylenevinylene) (PmPV) composites for CNTs of diameters ranging between 1.35 and 1.55 nm. (Dalton, A. B., et al., J. Phys. Chem. B, 104, 10012, (2000)).

consistent with strong hydrophobicity known for all CNTs (Note: δ water ~ 47.9 (J/cm3)1/2

Solub. Parameter


Mesoscale morphology of polymer CNT: DPD

A. Maiti, J.T. Wescott and P. Kung, Molecular Simulation 31, 143 (2005).

Stiffness of the CNT is considered in

DPD

PMMA + CNT (10,10) left

PMMA + CNT (15,15) right with and

without compatibilizer

18


Macroscopic simulation by FEM

Average electric conductance in the XY plane versus vol%

CNT for A10-, B10-, A6B14-and A10B10- CNT composites.

A. Maiti, J. T. Wescott and G. Goldbeck-Wood, Int. J. Nanotechnology, 2, No. 3, (2005)


Conclusions

Theory, modeling and simulation (TMS) play vital

role in nanoscale science and engineering Interpretation of experiments

Design of experiments

Characterization and design of nanostructured materials

Design and control of manufacture

TMS in nanoscale science and engineering Typically requires many different techniques

Future advances in field will result from development of additional methods

Reverse mapping multiscale methods, electron transport

dynamics, optical properties, self-validating forcefields,…


Further reading and materials

Slides in the MOSE web sites

PDF of most of MOSE publications

An introductory paper

MOSE.UNITS.IT Trieste, 20 A pril, 2010 - slide 108 Maurizio Fermeglia – MO SE - UNITS

MOSE – THE MOVIE

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