Transcript Nanotechnology - univ

Nanotechnology
A big issue in a small world
H.Aourag
URMER, University of Tlemcen
Public concern and media hype
What Is All the Fuss About
Nanotechnology?
Any given search engine will
produce 1.6 million hits
Nanotechnology is on the way to
becoming the FIRST trillion dollar
market
Nanotechnology influences almost
every facet of every day life such as
security and medicine.
Does Nanotechnology
Address Teaching Standards?
Physical science content standards 9-12
 Structure of atoms
 Structure and properties of matter
 Chemical reactions
 Motion and forces
 Conservation of energy and increase in disorder
(entropy)
 Interactions of energy and matter
Does Nanotechnology
Address Teaching Standards?
Science and technology standards
 Abilities of technological design
 Understanding about science and technology
Science in personal and social perspectives
 Personal and community health
 Population growth
 Natural resources
 Environmental quality
 Natural and human-induced hazards
 Science and technology in local, national, and
global challenges
Does Nanotechnology
Address Teaching Standards?
History and nature of science
standards
 Science as a human endeavor
 Nature of scientific knowledge
 Historical perspective
Does Nanotechnology
Address Teaching Standards?
i
Nanotechnology Idea
Standard it can address
The idea of “Nano” – being small
Structure of Atoms
Nanomaterials have a high
surface area
(nanosensors for toxins)
Structure and properties of
matter, Personal and Community
Health
Synthesis of nanomaterials and
support chemistry (space propulsion)
Chemical Reactions
Shape Memory Alloys
Motion and Forces, Abilities of
technological design, Understanding
about science and technology
Nanocrystalline Solar Cells
Conservation of Energy and increase
in disorder (entropy), Interactions of
energy and matter, Natural Resources
Nanocoatings resistive to bacteria and Personal and Community Health,
pollution
Population Growth, Environmental
Quality, Natural and human-induced
hazards
Does Nanotechnology Address
Teaching Standards?
Nanotechnology Idea
Standard it can address
Nanomaterials, such as MR
(magneto-resistive) fluids in security
Science and technology in local,
national, and global challenges
Richard P. Feynman’s talk, “There is
plenty of room at the bottom”.
Feynman had a vision.
Science as a human endeavor,
Nature of scientific knowledge,
Historical perspective
Nanocosmetics and nanoclothing
Science as a human endeavor,
Science and technology in local,
national, and global challenges
Nanotechnology and Science Ethics
Science and technology in local,
national, and global challenges,
Science as a human endeavor,
Historical perspective, Natural and
human-induced hazards, Population
Growth, Personal and Community
Health
What is Nanotechnology?
It comprises any technological
developments on the nanometer scale,
usually 0.1 to 100 nm.
 One nanometer equals one thousandth of
a micrometer or one millionth of a
millimeter.
 It is also referred as microscopic
technology.

WHAT IS NANOTECHNOLOGY?
The intentional manufacture of large scale
objects whose discrete components are
less than a few hundred nanometers wide.
Exploits novel phenomena and properties at the
nanoscale.
Nature employs nanotechnology to build DNA,
proteins, enzymes etc.
Nanotechnology – Bottom up approach
Traditional technology – Top down approach
It is the ultimate technology.
What does Nano mean?

“Nano” – derived from an ancient Greek word
“Nanos” meaning DWARF.




“Nano” = One billionth of something
“A Nanometer” = One billionth of a meter
10 hydrogen atoms shoulder to shoulder
There are 25 million nms in a single inch.
NATIONAL NANOTECHNOLOGY ACT, October 2003
VARIOUS MATERIALS IN
NANOMETER DIMENSION
< NM  NM  1000’s of NM’s  Million NM’s  Billions of NM’s
NANOMATERIALS WITH DIFFERENT
ATOMIC ARRANGEMENTS
Carbon
Nanotube
50,000 times
Thinner than
Human hair
Buckyball
FUTURE AUTOMOBILE
Carbon nanotubes in
windshields & frames
to make them strong
& lightweight
Nano-powders
in paints for
high gloss &
durability
Nano polymer composites for lightweight
high resistance bumpers
Nano-scale metal
oxide ceramic
catalysts to
almost
eliminate
emissions
Fuel cells with nanocatalysts and
membrane
technologies
NANOMATERIALS IN CURRENT
CONSUMER PRODUCTS
Cosmetics, sunscreens
Containing zinc oxide and
Titanium oxide nanoparticles
Carbon nanotubes
Nano polymer
Composites for stain
Resistant clothing
HEALTH AND MEDICINE
•
Expanding ability to characterize genetic makeup will
revolutionize the specificity of diagnostics and
therapeutics
- Nanodevices can make gene sequencing more
efficient
•
Effective and less expensive health care using remote
and in-vivo devices
• New formulations and routes for drug
delivery, optimal drug usage
Nanotube-based
biosensor for
cancer diagnostics
• More durable, rejection-resistant artificial
tissues and organs
• Sensors for early detection and prevention
HOMELAND SECURITY
• Very high sensitivity, low power sensors for detecting
chem/bio/nuclear threats
• Light weight military platforms, without sacrificing functionality,
safety and soldier security
- Reduce fuel needs and
logistical requirements
• Reduce carry-on weight of
soldier gear
- Increased functionality
per unit weight
ESTIMATES OF THE POTENTIAL
MARKET SIZE



Conserv
ative
case
NSF
Estimate

0.5


1.1
Aggres
sive
case

Aerospace
9%

Materials
6%
31%
Chemical 9%
Manufacturing
17%
Pharmaceuticals
2.0
USD trillions
Nanotechnology related goods and services – by 2010-2015
Source: National Science Foundation
Other
28%

Electronics
18
SAFETY OF NANOMATERIALS
 Environmental impact
 Absorption through skin
 Respitory ailments
 Evidence that carbon nanotubes cause
lung infection in mice. Teflon nanoparticles
smaller than 50 nm cause liver cancer in mice.
NANOTECHNOLOGY RESEARCH AND
COMPUTATION CENTER (NRCC)
WESTERN MICHIGAN UNIVERSITY
Inter & Multidisciplinary program
Established in December 2002
www.wmich.edu/nrcc
AREAS OF RESEARCH
 Molecular Self-Assembly – organic, biological, and
composites for molecular recognition, sensors, catalysis.
 Sensors – chemical, biological, and radiological agents;
- biosensors; gases (O2, H2).
 Novel nanomaterial synthesis and characterization.
 Lab-on-chip and Lab-on-a-CD.
 Novel nanomaterials derived from biological molecules –
protein nanotubes, viral scaffolds, bacteriophages.
 Quantum mechanical modeling of nanomaterials.
 Electronic structures and properties of nanoclusters.
 Fluid dynamics in micro- and nano-channels.
 Molecular electronics.
 Toxicity of nanoparticles.
Molecular Nanotechnology

The term nanotechnology is often used
interchangeably with molecular
nanotechnology (MNT)
MNT includes the concept of
mechanosynthesis.
 MNT is a technology based on positionallycontrolled mechanosynthesis guided by
molecular machine systems.

Nanotechnology
in Field of Electronics
Miniaturization
 Device Density

History

Richard Feynman
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1959, entitled ‘There's Plenty of Room at the Bottom’
Manipulate atoms and molecules directly
1/10th scale machine to help to develop the next
generation of 1/100th scale machine, and so forth.
As things get smaller, gravity would become less
important, surface tension molecule attraction
would become more important.
History

Tokyo Science University professor Norio
Taniguchi
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1974 to describe the precision manufacture of
materials with nanometre tolerances.
K Eric Drexler
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1980s the term was reinvented
1986 book Engines of Creation: The Coming Era of
Nanotechnology.
He expanded the term into Nanosystems: Molecular
Machinery, Manufacturing, and Computation
Nanomaterial and Devices

Small Scales
Extreme Properties
 Nanobots
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Self-Assemble
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Nanodevices build themselves
from the bottom up.
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Scanning probe microscopy
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Atomic force microscopes
scanning tunneling microscopes
scanning the probe over the surface
and measuring the current, one can
thus reconstruct the surface structure of
the material
Problems in Nanotechnology

how to assemble atoms and molecules
into smart materials and working devices?
Supramolecular chemistry
 self-assemble into larger structures

Current Nanotechnology

Stanford University


extremely small transistor
two nanometers wide and regulates electric current through a
channel that is just one to three nanometers long

ultra-low-power

Intel

processors with features measuring 65 nanometers
Gate oxide less than 3 atomic layers thick
20 nanometer transistor
Atomic structure
Plasmons
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Waves of electrons traveling along the surface of
metals
They have the same frequency and
electromagnetic field as light.
Their sub-wavelength require less space.
With the use of plasmons information can be
transferred through chips at an incredible speed
Nanomaterial modeling and
simulation types
What I will cover
Carbon Nanotubes
 Bio-Nano-Materials
 Thermoelectric Nanomaterials
 What is happening at UK
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Carbon Nanotubes
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What are they?
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Who discovered them?
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Carbon molecules aligned in cylinder
formation
Researchers at NEC in 1991
What are some of their uses?
Minuscule wires
 Extremely small devices
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Potential energy
 Vk = Repulsive force
 Va = attractive force
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Morse potential equations
Carbon Nanotubes
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total potential of a system
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Adds the NB contribution
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Force of interaction
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Carbon Nanotubes
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Leonard – Jones potential with von der Waals
interaction
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Geen - Kudo relation
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Bio-Nanomaterials
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What is Bio-Nanomaterials?
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Putting DNA inside of carbon nanotubes
What can this research give us?

There are lots of chemical and biological applications
Distances over time
Van der waals engery
Radical density profiles
Thermoelectric Nanomaterials
Concepts before modeling can begin:
 ZT = TσS2/κ
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T = temperature
σ = electrical conductivity
S = Seebeck constant
κ = κph +κel

K = sum of lattice and electronic contributions
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Potential across thermoelectric material
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Boltzmann transport
The Modeling equations
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Thermoelectric Nanomaterials
Thermoelectric Nanomaterials
Thermoelectric Nanomaterials
Nanomaterials at UK
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Deformation Mechanisms of Nanostructured Materials
Synthesis of Nanoporous Ceramics by Engineered
Molecular Assembly
Carbon Nanotubes
Optical-based Nano-Manufacturing
The Grand Quest: CMOS High-k Gate Insulators
Self-assembled metal alloy nanostructures
Rare-earth Monosulfides: From Bulk Samples to
Nanowires
Thermionic Emission and Energy Conversion with
Quantum Wires
Resonance-Coupled Photoconductive Decay
Computer Simulation of
Fluorinated Surfactants
Introduction to surfactant and self-assembly

What is surfactant?
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What is self-assembly?
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Micelles, mesophases
1. Davis, H. T., Bodet, J. F., Scriven, L. E., Miller, W. G.
Physics of Amphiphilic Layers, 1987, Springer-Verlag, New
York
Introduction to fluorinated surfactants

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Unique properties introduced by the strong electronegativity of
fluorine and the efficient shielding of the carbon atoms by fluorine
atoms
Fluorocarbon chain is stiffer, and favors aggregates with low
curvature (Fig from [2])
Advantages over hydrocarbon chains: higher surface activity ,
thermal, chemical, and biological inertness, gas dissolving capacity,
higher hydrophobicity
R-NC5H5+ClCMC(mM) @ 298 K
and lipophobicity
C12H25-
15.5 3
C8H17-
275 4
C6F13C2H4-
16.2 3
C4F9C2H4-
170 4
2. M. Sprik, U. Rothlisberger and M. L. Klein, Molec. Phys. 1999 97:355
3. K. Wang, G. Karlsson, M. Almgren and T. Asakawa, J. Phys. Chem. B 1999 103:9237
4. E. Fisicaro, A. Ghiozzi, E. Pelizzetti, G. Viscardi and P. L. Quagliotto, J. Coll. Int. Sci. 1996 182:549
Motivations for the computer simulation of
fluorinated surfactants

Simulations can be treated as computer experiments that serve as
adjuncts to theory and real experiments

Experiment is a viable way to study the effect of chain stiffness, yet it
might be expensive to do a systematic study on this topic.

Computer simulations might help selecting surfactants for the right
type of mesophase, which provides a guideline for experimental
study.
Monte Carlo techniques for the simulation of
surfactant solutions

Off-lattice atomistic simulation

All atoms (or small group of atoms, e.g. CH2 ) are explicitly represented

Most interactions are included, more realistic, yet hard to model
Can simulates molecular trajectories on a time-scale of nanoseconds
Can’t simulation the self-assembly phenomena
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Off-lattice coarse-grain
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A number of atoms are grouped together and represented in a simplified
manner
Electrostatic and dihedral angle potentials are usually absent
Can simulate process happening on a time-scale of microseconds, e.g.
micelle formation
Can’t simulate equilibrium self-assembly structure at higher concentration
Monte Carlo techniques for the simulation of
surfactant solutions (continued)

Lattice coarse-grain

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
replacing the continuous space with a discretized lattice of suitable
geometry
Electrostatic and intra-molecular potentials are absent
Fast, efficient, can simulate process happening on a time-scale of a few
hours, e.g. mesophase formation
Based on Flory-Huggins Theory. Proven to be successful in polymer
science for many years for investigating universal properties of single
chains, polymer layers and solutions and melts
Utility of the model is limited
Choosing the right model for our simulation
purpose – lattice coarse-grain


Most time-consuming part in a MC simulation is the evaluation of
inter and intra-molecular potentials after each trial move
The speed of off-lattice models is limited, because

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Lattice models are fast, because


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It has to reevaluate the potential functions explicitly when calculate the
energy change after each move
The speed of the simulation is determined by the complexity of the
potential functions
Off-lattice can at most simulate the formation of a few micelles
Atoms (united atoms) are moving on the lattice, intra and intermolecular distance, bond angles are thus discretized
It’s possible to precalculate the potentials corresponding to certain
distance and angles and build look-up tables
When calculate the energy change, only need to look up the tables
Can simulate the mesophase formation efficiently
Our targeted system: mesophase formation in surfactant solutions
Larson’s Lattice Model – representation of
the system

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Targeted system: a surfactant solution consists of NA moles water, NB
moles oil and Nc moles surfactant molecules, with fixed volume and
temperature (canonical ensemble)
Surfactant: use HiTj to define a linear surfactant consisting of a string
of consecutive i head units attached to consecutive j tail units.
Whole system resides on an N×N×N cubic lattice, periodic
boundary conditions are applied
Oil and water molecules occupy single sites on the lattice, and each
amphiphile occupies a sequence of adjacent or diagonally adjacent
sites (equal molar volume for all the species)
Number of sites occupied by surfactant is,
N3 

(i  j ) N C
N A  N B  (i  j ) N C
The rest of the sites is fully occupied by water and oil according to
their volume ratio
Larson’s Lattice Model – interactions
between species

Each site interacts only with its 8
nearest, 9 diagonally nearest, and 9
body-diagonally nearest neighbors

Essentially, a square well potential is
applied

Favorable interactions are set to be 1, while unfavorable interactions are
+1

Total energy is pairwise additive
Square-well potential
A simple 2D lattice with 2
chains, 7 water (grey) and 6 oil
(red) molecules
Etotal   Eij
(i, j=water, oil, head, tail)
Larson’s Lattice Model - typical trial moves
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Pair interchange [5]
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Chain kink [5]
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A surfactant segment exchanges position with
its neighbor without breaking the surfactant
chain
Chain reptation [5]
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Exchange of positions of two simple molecules
One chain end moves to a neighboring site,
and the rest of that chain slithers a unit to keep
the chain connectivity
Chain multiple kink [6]

If a kink move creates a single break in the
chain, the simple molecule will continue to
exchange with subsequent beads along the
chain until beads on the chain are close
enough to reconnect.
5. R. G. Larson, L.E. Scriven and H. T. Davis, J. Chem. Phys. ,1985, 83, 2411
6. K.R. Haire, T.J. Carver, A.H. Windle, Computational and Theoretical Polymer Science, 2001, 11, 17
Larson’s Lattice Model – simulation process

Initialize the system
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Make a trial move
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Randomly conduct a trial move according to
its occurrence ratio
Calculate the energy change
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Put the system in a random state
Reevaluate the interactions of the moved
particles with its neighbors and calculate
the energy change
Accept the trial move with the Metropolis scheme
E

)
exp(
P
kbT

1

E  0
Keep trying the moves until system approach equilibrium


E  0
A simple 2D lattice with 2
chains, 7 water (grey) and 6 oil
(red) molecules
Either monitor the total energy change, or monitor the structure formed
in the simulation box
Sampling

Sample a certain property over a certain number of configurations
Simulation of the mesophase formation preliminary results

Simulation procedure:

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Start the simulation from a higher temperature and
equilibrate the system, in order to make the system
in a athermal state and as random as possible
Anneal the system by decreasing the temperature
in a small amount after the system reaches
equilibrium at a higher temperature
When the temperature is lower than the critical
temperature, sample the density of a certain
species
Preliminary results
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60vol% H4T4 surfactant, 40vol% water
Should form cylindrical structure according to
Larson’s report [7]
The right figures are the same self-assembly
structure viewed from two different perspectives
3D density contour plot according to the oil
concentration. 60% H4T4 surfactant, 40%
water
7. R. G. Larson; Chemical Engineering Science, 1994, 49, 17, 2833
Add the bond overlapping constraint


Bond overlapping may occurred
in the system, which is unrealistic
Simulation results after adding
the bond overlapping constrain
(other conditions are the same).
Perfect hexagonal close packing
cylindrical structure is formed.
Two chains overlaps with
each other
3D density contour plot according to the oil concentration.
60% H4T4 surfactant, 40% water
Verification of our lattice simulation program
– compare with Larson’s simulation results


Ternary phase diagram of
H4T4 surfactant in water and
oil by Larson’s lattice Monte
Carlo simulation [8]
5 data points (volume
percentage)
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40% water, 40% oil, 20%
surfactant
20% water, 40% oil, 40%
surfactant
20% water, 45% oil, 35%
surfactant
60% water, 40% surfactant
7.3% water, 32.7% oil, 60%
surfactant
40% water, 60% surfactant
8. R. G. Larson; J. Phys. II France, 1996, 6, 1441
Simulation results from our simulation
program
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40% water, 40% oil, 20%
surfactant - Bicontinuous
mesophase
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20% water, 40% oil, 40%
surfactant - lamellar without holes
mesophase

20% water, 45% oil, 35%
surfactant - lamellar with holes
mesophase
Left: oil concentration profile, Right: water concentration profile
Simulation results from our simulation
program (continued)

60% water, 40% surfactant – spherical
structure, plot according to the
surfactant tail density

7.3% water, 32.7% oil, 60% surfactant
– intermediate bicontinuous structure
(might be gyration structure), plot
according to the water density

40% water, 60% surfactant –
hexagonal close packing cylindrical
structure, plot according to the
surfactant tail density
Left: oil concentration profile, Right: water concentration profile
An application of lattice MC simulation – the effect
of wall textures on the self-assembly structure

Motivation: nanostructured materials


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SiO2 source, ethanol, water, catalyst + surfactants give ordered phases
Mimic surfactant mesophases (coassembled)
Calcination gives ordered mesopores
Figures from 9. C.J. Brinker et al. Advanced Materials 1999 11: 579
Motivations to study the textured walls
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Real substrate surface may not be flat
For hierarchical materials (macroporous / mesoporous), curved
surfaces may be present
Design of nanostructure using surface texturing – use nanopatterned substrate to control the orientation of the self-assembly
structure


Mesopores perpendicular to the substrate is desired
Use the texture on the substrate to make the mesopores perpendicular
to the substrate
Simulation results without walls and with flat
walls
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Targeted system:


The simulation without walls
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60% H4T4 surfactant, 40% water
solvent
Hexagonal close packing cylindrical
structures
From the figure, d spacing = 10.7σ ,
unit cell parameter = 12.4σ
The nano-structures prepared by the
evaporation-induced dip-coating process
The simulation results with flat walls

Whether walls are hydrophilic or
hydrophobic, cylindrical structure are
always parallel to the wall and sits on
the (1, 0, 0) plane
Self-assembly structure with hydrophobic (left) and
hydrophilic (right) walls, according to oil density
Wave-patterned wall texture applied in the
simulation

Walls are treated as a set of block sites,
which can be neither occupied nor
penetrated by any molecules

Interactions between wall site and other
components in the system are set to +10
or -10, to emphasize the wall existence

The form of the 3D wave function:

Illustration of a discretized wave pattern
with wall thickness = 2 and wave
amplitude = 2

Periodic boundary conditions
Wave pattern with wall thickness = 2
and wave amplitude = 2
Lattice Monte Carlo simulation results for the
hydrophilic textured walls

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
Simulation results of 30x30x30 and
30x30x40 simulation box, wave
amplitude = 1
Surface pattern doesn’t change the
structure much at lower wall spacing.
Walls sit on the (2 1 0) plane.
A little calculation:

Box size = 30x30x30, wave amplitude = 1,
plot according to the oil density
How many layer in the horizontal plane:
30  2 42.43

4
d
10.7

Number of layers in the vertical plane:
30  2 28

 2.25
a
12.4
40  2 38

 3.06
a
12.4
Box size = 30x30x40, wave amplitude = 1,
plot according to the oil density
Lattice Monte Carlo simulation results for the
hydrophilic textured walls (continued)
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Surface pattern changes the selfassembly structures at higher wall
spacing
Number of layers in the vertical place



30x30x50 box, wall sits on (1, 0, 0)
plane 50  2 48

 4.49
d
10.7
30x30x60 box, wave amplitude = 1,
wall sits on (1, 0, 0) plane
Box size = 30x30x50, wave amplitude = 1,
plot according to the oil density
60  2 58

 5.42
d
10.7
30x30x60 box, wave amplitude = 2,
wall sits on (2, 1, 0) plane
60  2 58

 4.68
a
12.4
Box size = 30x30x60, wave amplitude = 1
(left) and 2(right), plot according to the oil
density
Lattice Monte Carlo simulation results for the
textured walls

With higher wall spacing, the
amount of planar defects
increases, 2 layers with a different
orientation formed.

Same phenomena are not
observed in systems with
hydrophobic walls
Box size = 30x30x100, wave amplitude =
1, plot according to the oil density
Box size = 30x30x60, wave amplitude = 1,
hydrophobic walls, plot according to the
oil density
Conclusions

Cylinders always align along diagonal of texture, even with small wave
amplitude

For hydrophilic walls, small wall spacing with small wave amplitude only
distorts structure

For hydrophilic walls, large wall spacing with small wave amplitude
promotes (1 0 0) orientation

For hydrophilic walls, planar defects may be more likely if wall spacing >
space needed for # of layers

systems with hydrophobic walls may avoid planar defects, because


the deposition of a monolayer of surfactant on the wall.
The chain softness mitigates the pattern
References
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http://shasta.mpistuttgart.mpg.de/research/bionano/bionano/modeling%2
0and%20simulation%20of%20bio-nano-materials.htm
http://www.foresight.org/Conferences/MNT6/Papers/Cagi
n3/
http://www.humphrey.id.au/papers/ITC2004.pdf
http://www.engr.uky.edu/%7Emenguc/NECP_Sems/
http://pubs.acs.org/cgibin/article.cgi/nalefd/2003/3/i04/pdf/nl025967a.pdf
http://www.foresight.org/Conferences/MNT6/Papers/Cagi
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http://www.research.ibm.com/topics/popups/serious/nan
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