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Studies on chemical heterogeneity ofimulti-component polymers:
Sequence Length Distributionsq g
Woosung Jung, T. A. Duever, A. PenlidisWoosung Jung, T. A. Duever, A. Penlidis
Department of Chemical Engineeringp g gIPR annual symposium, University of Waterloo
May 13, 2008
IPR 20
08
Outline
• Introduction
• Copolymerization characteristics
• Macroscopic approach
• Microscopic approach
• Summary
1/31
IPR 20
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Introduction- Multicomponent polymerization
M1
O
O
R
Propagation-M1M2M1M3-……-M2M3M1M1-
M2
O
R
2 3 1 1
Polymer chain
M3
O
R
Composition & Arrangement
2/31
3
Monomer speciesIP
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Introduction- Multicomponent polymerization
• Competitive reactions between same or• Competitive reactions between same or
different radical/monomer speciesp
• Governed by probabilistic nature of reactions
• Numerous combinations of monomer species
• Macroscopic approach (Composition)• Macroscopic approach (Composition)
• Microscopic approach (SLD & Triad fraction)
3/31
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Copolymerization characteristics
Model• Random (Bernoullian)• Statistical (1st/2nd order Markov)
Structures• Alternating (-M1M2M1M2M1M2M1M2-)• Block ( M M M M M M M M M M M )• Block (-M1M1M1M1M1M2M2M2M2M1M1-)• Graft (-M1M1M1M1M1M1M1M1M1M1-)
4/31M2M2M2M2M2-
IPR 20
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Copolymerization characteristicst- 1st order Markov (terminal) model
Mi+ Mj
Mjkpij
Reactivity of the propagating chain depends only on the monomer unit at the growing endy g gand independent of chain composition.
5/31
IPR 20
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Copolymerization characteristicst- 1st order Markov (terminal) model
kM1 + M1kp11 M1
k 12
111
p
p
kk
r =
M1 + M2kp12 M2
k
12p
M2 + M1kp21 M1
k 22
222
p
kk
r =M2 + M2
kp22 M2 21pk
6/31
IPR 20
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Copolymerization characteristicsd- 2nd order Markov (penultimate) model
MiMj+ Mk
MjMkkpijk
Reactivity of the propagating chain is affected by the last and the next-to-last monomer unitsyand independent of chain composition.
7/31
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Copolymerization characteristics- Reactivity ratios
• Relative rate of homo- to cross-propagation• Relative rate of homo- to cross-propagation• Estimated from experimental (NMR) data• r > 1 : homo-propagation favored• r < 1 : cross-propagation favoredr 1 : cross propagation favored• r = 0 : No homo-propagation (alternating)• Q-e scheme, Hammet and Taft equation for
starting values
8/31• Determines polymerization tendencyIP
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Macroscopic approach- Instantaneous composition
∑ •N
i MRkMd ]][[][Material balances∑
=
•=−j
ijpjii MRk
dtMd
1]][[][
]][[]][[ MRkMRk ••Steady-state hypothesis]][[]][[ ijpjijipij MRkMRk •• =
iMf ][Monomer feed composition
∑=
= N
ii
ii
Mf
1][
Instantaneous compositionof multi-component polymerN = 2: Mayo-LewisN 3 W lli B i∑
= N
i
ii
Md
MdF][
][
9/31
N = 3: Walling-Briggs∑=i
i1
][IP
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Macroscopic approach- Instantaneous composition
0.8
0.9
1Instantaneous Copolymer Composition of Sty and AN
rSty = 0.36
0.5
0.6
0.7
F Sty
rAN = 0.078FSty > fSty
FSty < fSty Direction
0.2
0.3
0.4
Experimental data
Sty fSty Directionof Drift
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
fSty
Experimental dataModel predictionAzeotropic point
10/31Figure 1. Simulation of Styrene-Acrylonitrile bulk co-polymerization
T = 60 (Experimental data from Hill et al., 1982)
IPR 20
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Macroscopic approach - Instantaneous composition
0.8
0.9
1Instantaneous Copolymer Composition of MMA and MA
rMMA = 2.6FMMA > fMMA
0.5
0.6
0.7
F MM
A
rMA = 0.27
Direction
0.2
0.3
0.4
F Directionof Drift
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
fMMA
Experimental dataModel prediction
11/31Figure 2. Simulation of Methyl methacrylate-Methyl acrylate bulk co-polymerization
T = 50 (Experimental data from Kim and Harwood, 2002)
IPR 20
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Macroscopic approach- Cumulative (average) composition
∫if df
Skeist, Meyer-Lowry∫ −=−
if ii
i
fFdfX
0
)1ln(
][][][ PMMConversion][][
][][
][][
0
0
PMP
MMMX
+=
−=
Cumulative compositionof multi-component polymer∑
= N
i
ii
P
PF__
][
][
Composition changes during polymerization;
∑=i 1
12/31‘Composition drift’
IPR 20
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Macroscopic approach - Cumulative composition drift
0.7
0.8Cumulative polymer composition vs conversion
rSty = 0.717
0.5
0.6
sitio
n(ac
c.) rEA = 0.128
0.3
0.4
Com
po
f10 = 0.152
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.1
0.2
Conversion
f10 = 0.453
f10 = 0.762
13/31Figure 3. Simulation of Styrene-Ethyl acrylate bulk co-polymerization
T = 50 (Experimental data from McManus and Penlidis, 1996)
IPR 20
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Macroscopic approach - Limitation
• Does not give information on monomerDoes not give information on monomer arrangements
M1M1M1M1M1M2M2M2M2M2 Block
Diff i i i
M1M2M1M2M1M2M1M2M1M2 Alternating
• Different properties, same composition
F1 Bl k = F1 Al i = 0 514/31
F1, Block F1, Alternating 0.5IP
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Microscopic approach - Sequence Length Distribution (SLD)
• Shows intramolecular heterogeneityShows intramolecular heterogeneity
M1M1M1M1M1M2M2M2M2M2 BlockM1M1M1M1M1M2M2M2M2M2
M M M M M M M M M M
Block
AlternatingM1M2M1M2M1M2M1M2M1M2 Alternating
Sequence length of M = 5Sequence length of M1, Block = 5
Sequence length of M1 Al = 115/31
Sequence length of M1, Alter. 1IP
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Microscopic approach - Sequence Length Distribution (SLD)
M MkMi+ Mj
Mjkpij
∑∑∑ •
•
=== N
jpij
jpijN
jpij
jpijN
jipij
jipijij
fk
fk
Mk
Mk
MRk
MRkP
][
][
]][[
]][[
∑∑∑=== j
jp jj
jp jj
jp j111
)(1 ikPPPN
ikii
N
ij ≠=+= ∑∑
Probability of reaction between radical species id i j
11 kj ==
16/31
and monomer species jIP
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Microscopic approach - Sequence Length Distribution (SLD)
Probability of having1 PPNN
n= − ∑ Probability of havingn consecutive units
of monomer species i( ) )(111
ikPP
PPN
iin
ii
kikiiin
≠−=
=
−
=∑
-M1M1M1M1M1M2- N15 = (P11)4P12
( ) )(iiii
1 1 1 1 1 2 15 ( 11) 12
Long Chain Approximation1PPN nN
k ⎟⎞
⎜⎛
= ∑∑∑∞
−∞
Long Chain Approximation
)(111111
ikPP
PPN
iiN
ik
nii
kik
nin
≠=−
=≈
⎟⎠
⎜⎝
=
∑
∑∑∑===
17/31
)(111 PP iiiik
ik −−∑=
IPR 20
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Microscopic approach - Sequence Length Distribution (SLD)
Sequence length distribution of Sty monomerq g y
0.7
0.8
0.9
n)
0 3
0.4
0.5
0.6
babili
ty (
N1n
0.0
0.1
0.2
0.3
Pro
b
1 2 3 4 5 6 7 8
Chain length (n)
f10 = 0.4 f10 = 0.5 f10 = 0.6 f10 = 0.7 f10 = 0.8 f10 = 0.9
18/31Figure 4. Sequence Length Distribution of Sty monomer
in Styrene-Acrylonitrile co-polymer, T = 60
IPR 20
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Microscopic approach - Average Sequence Length
∑∞
( ){ }∑∑∑
∑ ∞
=
−∞
=∞= −=+++===
nii
nii
niiiin
nin
i PnPNNNnNN
nNn
1
1
1321
1__
132 L
( ) ( )∑ ∑
∑∞ ∞
−
=
==−≈−= Niin
iin
ii
nin
PnPnP
N
221
1
11
11( ) ( ) ∑
∑ ∑=
= = −−− N
kik
iiiiiin niiii
PPPP1
221 1 111
Instantaneous number average sequence length
19/31
IPR 20
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Microscopic approach - Average Sequence Length
∑∑∞∞
22
nii
n
N
kik
nin
N
kik
nin
nin
i PnPNnPn
Nn
nN
Nnw ⎟
⎠
⎞⎜⎝
⎛==== ∑∑∑∑
∑
∑
∑−
∞
==
∞
==
=∞= 1
1
22
11
2
1__
1
2
1
2__
( ) ( )( )
iiiiii
niiii
in
in
PPPPnP
nnN
+=
+−≈−= ∑
∑∞
−
=
1111 32122
1
( ) ( )( ) iiii
iin
iiii PP −−∑= 11 3
1
Instantaneous weight average sequence length
20/31
IPR 20
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Microscopic approach - Sequence Length Distribution (Ray)
∫∫ ==X
iin
X
iinRayin dXFNdXNNi
00
________
,
( ){ }∫∫ ∑ −=⎟⎠
⎞⎜⎝
⎛= −
=
−X
iiin
ii
X
i
N
kik
nii dXFPPdXFPP
0
1
0 1
1
00
1⎠⎝ 00
Probability of having n consecutive unitsof monomer species i during polymerization
21/31
IPR 20
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Microscopic approach - Sequence Length Distribution (HMP)
2⎪⎫⎪⎧ ⎞⎛⎟
⎞⎜⎛
⎟⎞
⎜⎛
∫∫X N
XX NNi
0 1
1
0__
0__
_________
,
⎟⎞
⎜⎛
⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧
⎟⎠
⎞⎜⎝
⎛
=⎟⎟⎟
⎠⎜⎜⎜
⎝=⎟⎟⎟
⎠⎜⎜⎜
⎝=
∫ ∑
∫ ∑
∫
∫
∫
∫=
−
X N
i
N
kik
nii
Xi
i
i
in
X
i
i
in
HMPin
dXFPP
dXF
dXFn
N
dX
dXn
N
Ni
( ){ } ( )1 221
0 10__
0__
⎫⎧
⎟⎠
⎞⎜⎝
⎛
∫∫
∫ ∑∫∫=
XX
ik
ik
i
i
i
i dXFPn
dXF
n
dX
( ){ }
( )
( )
( )1
1
11
1
,1
10
2
1
_________
,0
21
=−
⎭⎬⎫
⎩⎨⎧
−−
=−
−=
∫
∫∑
∫
∫ ∞
=
−
X
iiiii
nHMPinX
iiin
ii
dXFP
dXFPP
NdXFP
dXFPP
( ) ( )1100∫∫ iiiiii dXFPdXFP
Probability of having n consecutive units
22/31
Probability of having n consecutive unitsof monomer species i during polymerization
IPR 20
08
Microscopic approach - Differences between Ray & HMP
• Acc Number average Sequence LengthAcc. Number average Sequence Length
( )∫ −
X
idXFP1
1∫X
idXF
Ray HMP( )
∫X
i
ii
dXF
P
0
0 1
( )∫ −X
iii dXFP0
0
1
• Acc. Weight average Sequence Length
HMPRay( )
∫
∫ −+
X
X
iii
ii
dXF
dXFPP
02
111
∫
∫ −+
X
X
iii
ii
dXF
dXFPP
0 11
23/31
∫ − iii
dXFP0 1 ∫ idXF
0
IPR 20
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Microscopic approach - Differences between Ray & HMP
Cumulative average sequence length of Sty vs conversion Cumulative average sequence length of Sty vs conversion
2
2.1
2.2
2.3
h Number-average (Ray)3
3.2
3.4
3.6
h
Number-average (Ray)Number-average (HMP)Weight-average (Ray)Weight-average (HMP)Experimental data
1.6
1.7
1.8
1.9
Seq
uenc
e le
ngth Number average (Ray)
Number-average (HMP)Weight-average (Ray)Weight-average (HMP)Experimental data
2 2
2.4
2.6
2.8
Seq
uenc
e le
ngth
Figure 5 Figure 6
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11.3
1.4
1.5
Conversion
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1.8
2
2.2
Conversion
Cumulative average sequence lengths of Sty in Styrene-Acrylonitrile co-polymerT = 60 , [AIBN]0 = 0.05 M, fSty0 = 0.6 (Fig. 5), and fSty0 = 0.7 (Fig. 6)
(N b l th i t l d t f G i R bi t l 1985)
Figure 5. Figure 6.
24/31
(Number avg. sequence length experimental data from Garcia-Rubio et al., 1985)IP
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Microscopic approach - Differences between Ray & HMP
Cumulative average sequence length of Sty vs conversion Cumulative average sequence length of Sty vs conversion
8
9
10
11
h
Number-average (Ray)Number-average (HMP)Weight-average (Ray)Weight-average (HMP)Experimental data
40
50
60
h
Number-average (Ray)Number-average (HMP)Weight-average (Ray)Weight-average (HMP)Experimental data
4
5
6
7
Seq
uenc
e le
ngth
20
30
Seq
uenc
e le
ngth
Figure 7 Figure 8
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 12
3
4
Conversion
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
10
Conversion
Cumulative average sequence lengths of Sty in Styrene-Acrylonitrile co-polymerT = 60 , [AIBN]0 = 0.05 M, fSty0 = 0.8 (Fig. 7), and fSty0 = 0.9 (Fig. 8)
(N b l th i t l d t f G i R bi t l 1985)
Figure 7. Figure 8.
25/31
(Number avg. sequence length experimental data from Garcia-Rubio et al., 1985)IP
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Microscopic approach - Dyad/triad fractions
• Dyad fractionsDyad fractions
-M1M1- -M2M1-
T i d f ti
-M1M2- -M2M2-
• Triad fractions
-M1M1M1- -M1M1M2-1 1 1 1 1 2
-M1M2M1--M2M1M2--M2M1M1--M1M2M2-
26/31
1 2 1 1 2 2-M2M2M1- -M2M2M2-
IPR 20
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Microscopic approach - Triad fraction calculation
2
2 ⎟⎞
⎜⎛ iij fr
PA
Triad fractions( )
2
1
⎟⎟⎠
⎜⎜⎝ +
==
jiij
iijj
iijiiiii
ffrPPPPAA
frff
PA
centered onmonomer species i
( )
2
2)(1
⎟⎞
⎜⎛
+=−===
iijj
jiijiiiiijiijiiiij
f
frfff
PPPPAA
2
⎟⎟⎠
⎞⎜⎜⎝
⎛
+==
iijj
jijjij frf
fPA
( )
( )+++=
∑jijjiiiijiii AAAA
imonomeroncenteredfractions
27/31( ) 12 222 =+=++= ijiiijijiiii PPPPPP
IPR 20
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Microscopic approach - Triad fraction calculation
Simulation of triad fraction data for Sty/AN Simulation of Triad fraction for Sty/ANSimulation of triad fraction data for Sty/AN
0.7
0.8
0.9
1
n
Simulation of Triad fraction for Sty/AN
0.7
0.8
0.9
1
n
0.2
0.3
0.4
0.5
0.6
Tri
ad f
ract
ion
0.2
0.3
0.4
0.5
0.6
Tri
ad f
ract
ion
0
0.1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
f1 (Sty)
A111 A211+A112 A212 A111 (exp.) A112+A211 (exp.) A212 (exp.)
0
0.1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
f1 (Sty)
A222 A122+A221 A121 A222 (exp.) A221+A122 (exp.) A121 (exp.)
Triad fraction calculation of Styrene-Acrylonitrile co-polymerT = 60 Sty-centered (Fig 9) and AN-centered (Fig 10)
Figure 9. Figure 10.
28/31
T = 60 , Sty-centered (Fig. 9), and AN-centered (Fig. 10)(Experimental data from Hill et al., 1982)
IPR 20
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Microscopic approach - Triad fraction calculation
Simulation of triad fraction data for MMA/MA
0 70.80.9
1
0.30.40.50.60.7
Tria
d fr
actio
n
00.10.20 3
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Figure 11 Triad fraction calculation of Methyl methacrylate-Methyl acrylate co-polymer
f1 (MMA)
A222 A122+A221 A121 A222 (exp.) A122+A221 (exp.) A121 (exp.)
29/31
Figure 11. Triad fraction calculation of Methyl methacrylate-Methyl acrylate co-polymerT = 50 , MA-centered (Experimental data from Kim and Harwood, 2002)
IPR 20
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Summary
• Even with a limited number of monomers,Even with a limited number of monomers,
a very large number of combinations
• Complicated multi-component system
• Lack of experimental data
30/31
IPR 20
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Many Thanks to…
• Prof. T. A. DueverProf. T. A. Duever
• Prof. A. Penlidis
• BASF SE
Questions?
31/31
IPR 20
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Supplementaries- Simulation work
Kinetic studyM l i
Literature searchModel testing
Multi-componentFree-radical
P l i ti
Modeling study
PolymerizationSimulation
Modeling studyCodingTrouble shootingParameter estimation
32/31
IPR 20
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Copolymerization characteristicsd- 2nd order Markov (penultimate) model
M M + M kp111 M MM1M1 + M1kp111 M1M1
M1M1 + M2kp112 M1M2
112
1111
p
p
kk
r =212
2111
p
p
kk
r =′
M1M2 + M1 M2M1
M1M2 + M2 M2M2
kp121
kp122
p p
2222
pkr = 122
2pk
r =′1 2 2 2 2
M2M1 + M1 M1M1
M M + M M M
kp211
kp212
2212
pkr
1212
pkr
k kM2M1 + M2 M1M2
M2M2 + M1 M2M1kp221
k
111
2111
p
p
kk
s =222
1222
p
p
kk
s =
33/31M2M2 + M2 M2M2
kp222IP
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Microscopic approach - Average Sequence Length (Ray)
( )∫ ∑∑ ⎟⎞
⎜⎛ ∞∞ X
1 ( )
( )∫ ∑
∫ ∑
∑
∑
⎟⎞
⎜⎛
−⎟⎠
⎞⎜⎝
⎛
==∞
−
=
−
∞=
Xn
iiin
nii
nRayin
Rayi
dXFPP
dXFPnP
N
NnN
1
0 1
1
_______1
_______
,_______
,
1
1
( )
( ) ∫∫
∫ ∑∑ −⎟⎠
⎜⎝ ==
XX
iiin
nii
nRayin
dXFdXFP
dXFPPN0 1
1
1,
111
1
( )( )
( ) ( )
( )
∫
∫
∫
∫ −=
−
−−
≈ X
iii
X
iiiii
dXF
dXFP
dXFP
dXFPP 00
2 1
11
11
Cumulative number average sequence length
( ) ( ) ∫∫ − iiiiii
dXFdXFPP 00
11
34/31
Cumulative number average sequence lengthIP
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Microscopic approach - Average Sequence Length (Ray)
( )∫ ∑∑ ⎟⎞
⎜⎛ ∞∞ X
12 ( )
( )∫ ∑
∫ ∑
∑
∑
⎟⎞
⎜⎛
−⎟⎠
⎞⎜⎝
⎛
==∞
−
=
−
∞=
Xn
iiin
niiRayin
nRayi
dXFPP
dXFPPn
Nn
NnW
1
0 1
12
________
________
,1
2_______
,
1
1
( )
( ) ∫∫
∫ ∑∑
++
−⎟⎠
⎜⎝ ==
Xii
Xii
iiin
nii
nRayin
dXFPdXFPP
dXFPnPNn0 1
1
1,
111
1
( )( )
( )
( )
∫
∫
∫
∫ −=
−
−−
≈ X
iii
ii
X
iiiii
ii
dXF
dXFP
dXFP
dXFPP 0
20
3
11
11
11
Cumulative weight average sequence length
( )( ) ∫∫ −− i
iiiii
ii
dXFP
dXFPP 00
2 11
1
35/31
Cumulative weight average sequence lengthIP
R 2008
Microscopic approach - Average Sequence Length (HMP)
∫⎞⎛ ∞∞ X
( )
( )∫
∫ ∑
∑
∑ −⎟⎠
⎞⎜⎝
⎛
== =
−
∞
∞
=X
iiin
nii
nHMPin
HMPi
d
dXFPnP
N
NnN 0
2
1
1
_________1
_________
,________
,
1
( )
( ) ∫∫
∫∑ −=
XX
iiin
HMPin
dXFdXFP
dXFPN
2
01,
11
1
( )( )
( ) ( )∫
∫
∫
∫=
−−
≈ X
i
X
iiiii
dXFP
dXF
dXFP
dXFPP 00
22
11
11
1
( ) ( )∫∫ −− iiiiii dXFPdXFP00
11
C l ti b l th36/31
Cumulative number average sequence lengthIP
R 2008
Microscopic approach - Average Sequence Length (HMP)
( )∫ ∑∑∑ −⎟⎠
⎞⎜⎝
⎛ −∞∞∞ X
iiin
iiHMPiHMPi dXFPPnNnNn 212_________2
_________2 1( )
( )∫
∫ ∑∑
∑
∑
−
⎟⎠
⎜⎝=== ==
∞
=
=X
iiiHMPi
iiiiin
HMPi
HMPinn
nHMPin
HMPinn
HMPi
dXFPNN
Nn
Nn
NnW
0
________
,
0 1________
,
,1
1
_________
,
,1
________
,
1
( ) ( ) ∫∫
∫
∫+
−+
+ XX
X
iii
ii
X P
dXFPP
P0
2 111
1( )
( )
( )
( )
∫
∫
∫
∫
∫
∫ −+
=−
=−
−−+
≈ X
i
iii
ii
X
i
iii
X
iiiHMPi
iiiii
ii
dXF
dXFPP
dXF
dXFP
dXFPN
dXFPPP
00________
,
0
23 1
11
1
111
( )
( )
∫
∫
∫∫
−
i
X
iii
iiiiHMPi
dXFP
0
0
00,
1
37/31Cumulative weight average sequence length
IPR 20
08
Microscopic approach - Triad fractions
No ofNo. ofMonomerSpecies
Distinguishabletriads
Total possibletriads
1 (homo-) 1 1( )2 (co-) 6 83 (ter-) 18 274 (t t ) 40 644 (tetra-) 40 645 (penta-) 75 1256 (hexa-) 126 2167 (hepta-) 196 343… … …19 3610 6859
38/3120 4200 8000
IPR 20
08