Computer Modeling of Nematic Liquid Crystals

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Transcript Computer Modeling of Nematic Liquid Crystals

Nematic colloids for photonic systems

(with schemes for complex structures) Iztok Bajc Adviser: Prof. dr. Slobodan Žumer Fakulteta za matematiko in fiziko Univerza v Ljubljani Slovenija

Outline

• • • • • •

Motivations, classical and new applications Nematic liquid crystals Colloidal particles in nematic Modeling requirements

for

large 3D systems Test calculations

(3D)

Future work: external fields

for

photonic systems

Motivations, classical and new applications

Motivations

Why to approach this thematic?

Interesting

and

fast evolving

field.

• Liquid crystals

well represented

field in Slovenia.

• New potential applications: • •

Metamaterials .

Microcavities microresonators .

( Hot topics!

) M. Ravnik, S. Žumer,

Soft Matter

, 2009. • One of the

priorities

of the EU project (

Hierarchy

) in which I’m involved.

• Requirement of

very effective modeling codes

.

Challenge to find the

right approaches

.

M. Humar, M. Ravnik, S. Pajk, I. Muševič,

Nature Photonics

, 2009.

Classical applications of liquid crystals •

LCD

(Liquid Crystal Displays).

Eye protecting filters

for welding helmets (Balder) Liquid crystals have unique

optical

properties.

Polarizing glasses for 3D vision

New potential applications:

metamaterials, microresonators

Photonic crystals:

Nematic droplet.

Whispering Gallery Modes (WGM ) in a

microresonator.

Solid state metamaterials:

Soft metamaterials?

Figures: I. Muševič, CLC Ljubljana Conference, 2010.

Nematic Liquid Crystals

Nematic liquid crystals • • •

Liquid crystals

are a liquid, oily material.

They flow like a

liquid

...

... but can be

partially ordered

- like

crystals

.

E

 • • • Molecules are

rodlike.

Tend to align in a

preferred direction

.

Electric

or

magnetic field

can

change

their phase form isotropic liquid to

partially ordered mesophase

.

• (The same happens, if

temperature is lowered)

Description of nematic liquid crystals • Basic

quantities

Director

n

 (

r

 ) 

n

 1 Points in preferenced orientation.

Scalar order parameter

S

(

r

 )  1 2 

S

 1 Quantifies the

degree of order

of the orientation:

-1/2

ideal biaxial

liquid

0 1

 

isotropic

liquid

ideally aligned

liquid (all molecules parallel)

Alternative description with Q-tensor field New quantity:

tensor order parameter

Q

(

r

 ) :

Q

S

  3

n

 

n

I

 

P

 

e

1  

e

1  

e

2  

e

2 

S

2 2

n

 its largest eigenvector and its corrispondent eigenvalue.

Q

traceless

: •

Q

symmetric

:

Q

11 

Q

22 

Q

33  0

Q ij

Q ji Q

33  

Q

11 

Q

22 Only

5 independent components

of

Q

are required.

Q

  

Q

11

Q

12

Q

22

Q

13 

Q Q

23 11 

Q

22  

Free-energy functional

Director

and

order

nematic structure follow from the

Landau-de Gennes functional

:

minimizing

F

(

Q

)  

bulk f bulk

(

Q

, 

Q

)

dV

 

border f surf

(

Q

, 

Q

)

dV

f bulk  1 2 L  Q ij  x k  Q ij  x k  1 2 AQ ij Q ij  1 3 BQ ij Q jk Q ki  1 4 C(Q ij Q ij ) 2 Elastic energy Thermodynamic energy

L

– elastic constants

A, B, C

– material constants

W

– surface energy f surf  1 2 W (Q ij Q ij ( 0 ) ) 2 Surface energy

Colloidal particles

in nematic

Inclusion of colloidal particles • Inclusion of

colloidal particles

in a thin sheet of nematic LC.

• We get

disclination lines

(

topological defects

) around the particles: Strong

attractive forces

between particles.

Colloidal

structures -crystals

in nematic.

Structures of colloidal particles in nematic

1D

structures

3D

structures

2D

structures - crystals

Large 3D structures:

12- and 10- cluster in

90° twisted

nematic cell.

Experiments by U. Tkalec, 2010 (

to be published

).

3×3×3 dipolar crystal

in homeotropically oriented nematic. Experiment by Andriy Nych, 2010 (

to be published

).

Modeling Requirements

Computations until now Actual finite difference code in C is: •

Robust

and

effective

for smaller or periodic systems.

• But uses uniform grid (uniform resolution).

Example:

A job needs

2h

to converge.

You

double

the resolution Then it will run for

2 days

.

New modeling requirements

Mesh adaptivity

in 3D, preferably with

anisotropic metric

.

Moving objects

(due to nematic elastic forces).

Parallel processing

(computer clusters).

Meshes by

Cécile Dobrzynski,

Institut de Mathématiques de Bordeaux

.

Finite Element Method

(

FEM

)

Advantages

: – Mesh can be locally refined less mesh point needed.

– Around each point we have an interpolating function (spline).

Newton iteration of tensor fields

If

function

(of one variable):

f

' (

x

)  0 Newton iteration: First variation of

functional

: 

F

(

Q

) 

F

' (

Q

)   0 Newton iteration:

x k

 1 

x k

f f

' (

x k

'' (

x k

) )

F

' ' (

Q k

) 

Q k

  

F

' (

Q k

) 

Q k

 1 

Q k

 

Q k test functions

)

Test calculations in 3D:

One

colloidal particle

• • • Central section of 3D simulation box mesh Mesh points:

17 000

; Tetrahedra:

100 000

Mesh generation’s time:

5 sec

(TetGen)

2 microns

• • Central section:

director

field

n

(in green).

Newton’s method took

19

iterations (total time:

54 min

).

2 microns

• • Central section of the order parameter field S. In green: sections of

Saturn ring defect.

Topological defect 2 microns

Test calculations in 3D :

More

particles

Future work:

external fields

for photonic systems

Electric field on a nematic droplet

E

 0 By

tuning

electric field A large field

E

change

Q

.

   (

Q

)

Iteration needed

we

switch

between

optical modes

.

Figures: I. Muševič, CLC Ljubljana Conference, 2010.

Electromagnetic waves – linear/nonlinear optics •

Detail dimensions

comparable with

wavelength

.

2 microns

Ray optics

not adequate

.

Full system description

needed (diffraction,...).

• Nematic is a

lossy

medium.

• Also

nonhomegeneously anisotropic

.

Birefringence

Computational electromagnetics Basis:

Numerical solution of Maxwell equations Computational photonics Mature field for homogeneous medium and periodic structures (e.g. photonic crystals).

But young for nonhomegenously

anysotropic media !

Computational soft photonics

Computational approaches Book Joannopoulos et alt.,

Photonic Crystals,

points out three cathegories of problems: 1) Frequency-domain eigenproblems 2) Frequency-domain response 3) Time-domain propagation [1] Joannopoulos et alt.,

Photonic Crystals, Molding the flow of Light, 2nd ed

, Princeton University Press, 2008

.

1) Frequency domain eigenproblems • Seeking for

eigenmodes.

• Aim: 

band structure

of

photonic crystals

.

    (

r

 )  1    

H

    

H

 0 

c

2 

H

Eigenequation

(+ condition) •

Periodic

boundary conditions.

• Reduces to a matrix eigenproblem :

Ax

  2

Bx Pictures from site of Steve Johnoson (MIT).

2) Frequency domain responses • Seeking for

stationary state

.

• Aims:

absorption

&

transmittivity.

• At

fixed

?

 

E

 

H

   1

c

 (

r

 )  

t

1

c

H

 

t

 

E i

 

c J

 

H

 

i

c

 

E

+

Absorbing Boundary Conditions

(ABC).

• Reduces to a matrix linear system :

Ax

b

3) Time-domain propagation

Time evolution

of electromagnetic waves.

?

?

?

?

Micro-waveguides?

Micro-optical elements?

Start with

FDTD

(Finite Difference Time Domain) numerical method: • •

1.

Ready code

freely available.

Easily supports

nonlinear optical

effects.

Gain

feeling

and

experience

for

smaller systems

.

Next

: possibility of

passing to FEM

will be considered.

Acknowledgments:

Slobodan Žumer (adviser)

Miha Ravnik, Rudolf Peierls Centre for Theoretical Physics, Univerza v Oxfordu,

in

FMF-UL.

Frédéric Hecht, Laboratoire Jacques-Louis Lyon, UPMC, Paris 6.

Daniel Svenšek

Igor Muševič

Miha Škarabot

Martin Čopič

Uroš Tkalec

Work has been finansed by EU:

Hierarchy Project, Marie-Curie Actions