2.76/2.760 Multiscale Systems
Design & Manufacturing
Fall 2004
Polymer, Protein, Complexity
Sang-Gook Kim,
MIT
Nanoimprinting
Dry etching
PMMA
silicon
Heat &
Pressure
Metal
Lift-off
Cooling &
Separation
Sang-Gook Kim,
MIT
Remove
polymer
S. Chou, Princeton
Nanoimprinting
Photos removed for copyright reasons.
Soft Lithography: PDMS
Sang-Gook Kim,
MIT
Nanopatterning by Diblock
Copolymer
Diagrams and photos removed for copyright reasons.
See Park, M. et al. Science, Vol 276, 140, 1997
Sang-Gook Kim,
MIT
∆G m = ∆H m − T∆Sm
∂ 2 ∆G
<0
2
∂X
composition
Di-block copolymers
Spinodal
decomposition
Free energy, G
Spatial coordinate
s2
s1
∂2 G
=0
∂X 2
X1 X2 composition
Sang-Gook Kim,
MIT
Di-block copolymer, PS-PMMA:
PS+PMMA copolymers
Diagrams removed for
copyright reasons.
PMMA
<10%
Sang-Gook Kim,
MIT
PMMA
<30%
PMMA
50%
Polymers, Macromolecules
• Poly (many) + mer (structural unit)
-[C2H4]n- ,poly[ethylene]
H
H
H
H
C
C
C
C
H
H
H
H
Sang-Gook Kim,
MIT
spaghetti
Configurational Entropy
high
Sang-Gook Kim,
MIT
entropy
low
A singly bonded carbon chain
• N-2 angles
θ, ϕ
g+
g−
ϕ
Cn
Cn-1
θ
Cn-2
Cn-3
Sang-Gook Kim,
MIT
θ=68°
Diffusion
• Brownian motion
• Albert Einstein: Worked out a quantitative description
of Brownian motion based on the Molecular-Kinetic
Theory of Heat (Nobel Prize 1921)
2
R
Sang-Gook Kim,
MIT
1
2
= 6Dt
Random Walk-1D
-n
•
•
•
•
n
o
A simple coin toss
Head +1 step
Tail -1 step
After n steps, where you will be
located?
n
lim
n→∞
∑λ
k =0
k
=0
frequency
Gaussian
RMS= n λ
Sang-Gook Kim,
MIT
-Na
0
Na
Random walk -2D
A drunkard
Sang-Gook Kim,
MIT
nλ
Entropic Behaviour
•
•
•
•
Size & Shape of Polymer
Configurational Entropy
Paul Flory (Stanford): Nobel Prize 1974
Pierre-Gilles de Gennes (Paris): Nobel
Prize (1991)
Stretched
na
a
Coiled
End to end distance= a n
Sang-Gook Kim,
MIT
Possible Configurations
P(r)
1
P( r ) =
e
n
−r2
2 na 2
0
P(r); the number of possible
configurations of a random polymer coil
with “n” segments of size “a” with an
end-to-end distance (stretch) of r
Sang-Gook Kim,
MIT
R0 = a n
na
Entropy
• Stretching the coil
• Compressing the coil
3 r 2 R02
∆S = − [ 2 + 2 ]
2 R0 r
• Random walk of 10000 unit polymer chain of 5
Angstroms
• Length=5 micron
• Ro=5 angstroms x 100=50 nm
• Volume at the highest entropy
• Real volume is twice bigger than that of RWM.
Sang-Gook Kim,
MIT
Self Avoiding Walk
• Polymers cannot cross its own path.
• ∆Stotal = ∆Spressure + ∆Sdeformation
RW
SAW
2
2
0
2
3 r
R
∆S = − a n r − [ 2 + ]
2 R0 r
3 2 −3
For maximum entropy
Sang-Gook Kim,
MIT
R0 = an 0.6
Nano-scale Phase Separation
Random walk, Gaussian distribution
e-to-e distance, R = aN1/2
Rg = aN1/2/6
N: number of monomers
Micro-domain periodicity, L
L ∝ R g ∝ aN
1
2
N=1,000
a=5 angstroms
Then, L is around 15 nm.
Sang-Gook Kim,
MIT
Polymers
Synthetic versus Biological
Synthetic Polymer
• Mostly chain like structure
of repeated units (mers)
• Molecular weight
distribution (chemical
synthesis)
• Self avoiding walk model
Protein
• Primary structure exactly
defined
• Fixed molecular weight
• Unique folding
Diagram removed for
copyright reasons.
Sang-Gook Kim,
MIT
Proteins
• Proteins are polymers composed of amino acid monomers
• Proteins are characterized by a specific primary structure –
order of mers in the backbone and DP
• Control of primary structure leads to control of 3D structure
• Secondary structure refers to local chain conformations –
four types are known:
– α helix – regular helix
– β sheet – extended zig-zag
– β turn – puts fold into β sheet
– Globular or random coil
• Tertiary structure refers to secondary structure stabilized by
H bonds – defines protein folding
• The control of protein structure builds information into the
molecule that translates into function
Sang-Gook Kim,
MIT
Folding Summary
Figure by OCW.
(a) Linear
Sequence of
peptides.
Sang-Gook Kim,
MIT
(b) Local folding
into specific
peptide backbone
conformations.
(c) Folded 3-dimensional
structure of a single
polypeptide chain.
(d) Specific aggregation of
two or more, identical or
not, polypeptide chains.
Common 2ndary Structures
•
•
•
•
α helix
β sheet
Collagen triple helix
Globular or random coil
Sang-Gook Kim,
MIT
Diagrams removed for
copyright reasons.
Polymers vs. Protein
• Structure formation in
synthetic polymers is
statistically driven.
• Structure is
metastable
• Interpenetration
Sang-Gook Kim,
MIT
• Structure formation in
proteins is site specific
chemistry driven.
• Structure is stable
• No interpenetration
• Misfolded proteins lead
to serious diseases.
What is Complexity?
Design for Manufacturing?
DNA
~2-1/2 nm diameter
natural
MIT Stata Center by Gehry
Human heart
Human body
(circulatory
system)
Diagrams removed
for copyright reasons.
manmade
Carbon nanotube
~2 nm diameter
Nanotube transistor
Sang-Gook Kim,
MIT
$300 million, 5years
MIT Simmons Hall
$ 90million, 2 years
Scale Orders
Scale order, N =
size of the system
smallest characteristic length
N
•
•
•
•
104
Cars: 5 m ÅÆ 500 µ
106
Jig Machines: 5 m ÅÆ 5 µ
Lithography M/C: 30 cm ÅÆ 30 nm 107
109
Human Body: 2 m ÅÆ 2 nm
• Length scale of the periodicity?
Sang-Gook Kim,
MIT
Micro-phase Separation
Random walk, Gaussian distribution
e-to-e distance, R = aN1/2
Rg = aN1/2/6
N: number of monomers
Micro-domain periodicity, L
L ∝ R g ∝ aN
1
2
N=1,000
a=5 angstroms
Then, L is around 15 nm.
Sang-Gook Kim,
MIT
Complex Problems
• Gaussian integrals like
• Stock market index one year from today
• Weather one year from today
Sang-Gook Kim,
MIT
Complexity Universe
(Complexity)
Real
Complexity
(Uncertainty)
Combinatorial
Complexity
(highly coupled, high scale order)
(Difficulty)
Imaginary
(decoupled,
path dependent)
Periodic
(uncoupled, smaller order)
Sang-Gook Kim,
MIT
Good Design
“What” to “How”
“Top” to “Bottom”
What
How
- Does scale matter?
Axiomatic approach
• Independence Axiom
• Information Axiom
– Prof. Nam Suh @MIT
Design
Parameters
2.882
– Evolution to
“Complexity Theory for Nano
Systems”
How
Information Axiom
• Minimize the Information Content
Probability density
1
I = log 2 = −log 2 P
P
P : Probability of success =common range/system range
Sang-Gook Kim,
MIT
bias
Design range
System range
FR
Common range
Complexity
Time
Dependent
Combinatorial
Complexity
Time
Independent
Real
UncertaintyComplexity
Time
Independent
Imaginary
Time
Dependent
Periodic
Difficulty
Sang-Gook Kim,
MIT
Causality of Complexity
Type III: difficulty
Type I: coupling
Combinatorial
complexity
C
Imaginary
complexity
A
D
Large scale order
complexity
Real Complexity
B
E
Type IV: non-equilibrium
Sang-Gook Kim,
MIT
Small scale order
complexity
Type II: uncertainty
Complexity
A
•
•
•
•
system is complex when;
A design is coupled.
System ranges vary with time.
The outcome is uncertain. (low probability of success)
The scale order is very high. (over 109)
Complexity can be reduced by;
• Periodic functions (temporal, spatial, etc.)
Sang-Gook Kim
Functional Periodicity
• Time indepenedent real and imaginary complecity.
• Time dependent combinatorial and periodic complexity.
• Time dependent combinatorial complexity can become
periodic complexity by functional periodicity. [Suh, MIT]
–
–
–
–
–
–
–
Sang-Gook Kim
Temporal
Geometrical
Biological
Manufacturing process
Chemical information
Circadian
etc.
Multi-scale system assembly by
periodic building blocks?
• Periodic microdomains
• Functionally
uncoupled domains
• Periodicity,
• Nano to Macro
• Biomimetic?
MIT Simmons Hall
Tendon hierarchy
Figure by OCW.
L ∝ R g ∝ aN
Sang-Gook Kim,
MIT
1
2
Block Assembly
Nanopelleting
Technique
Silicon trenches
DBCP pellets on Silicon
Self-assembly
Sang-Gook Kim,
MIT
Detachment (lift-off)
Bundle CNT nanopellet
CMPed and Transplanted
Photos removed for copyright reasons.
See T. El-Aguizy, J-h Jeong, Y. B. Jeon, W. Z. Li, Z. F. Ren and
S.G. Kim, "Transplanting Carbon Nanotubes", Applied Physics
Letters, Vol 85, No. 25, P.5995, 2004
Sang-Gook Kim,
MIT
High aspect ratio nanopellets
Cold cathode array,
FED, Data storage,
Multi-E-beam array for NGL
Nanocandles
1-2 microns
bundle
Single strand
Sang-Gook Kim,
MIT
In-Plane Assembly of High-Aspect
Ratio Nanocandle
•
Mechanical in-plane assembly of nanocandle in the V-groove
with a micro probe tip
•
Bonding of nanocandle in the V-groove with a drop of epoxy
Micro probe tip
SU-8
SEM of the assembly
CNT
Sang-Gook Kim,
MIT
Nanocandle
MIT Nanopipette
•
Nanotube assembly to the
tip of a micropipette
micropipette
• Nanotube assembly to
the tip of an in-plane
AFM
- Parallel Imaging and
Pipetting
- Multi-energy probing
- Manufacturable
SCNT
Nanopellet
Sang-Gook Kim,
MIT
- Arrayable
A multiscale system design…
Customer’s
needs
Functionalities
m
Design
parameters
Process
variables
Nano-enabled products
+
µm
Functionality
Microstructures
+
Manufacturability
Nano-structures
nm
Sang-Gook Kim,
MIT
Complexity
NanoManufacturing