national science foundation where discoveries begin controlling polymer rheological properties using...
TRANSCRIPT
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Controlling Polymer Rheological Properties Using Long-Chain Branching
PI: Ronald LarsonUniv. of Mich., Dept of Chem. Eng., Macromolecular Science and
Engineering ProgramPossible co-PI: Michael Solomon
Univ. of Mich., Dept of Chem. Eng., Macromolecular Science and Engineering Program
Possible co-PI: Jimmy MaysUniv. of Tennessee, Dept of Chemistry
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Industrial Relevance
“The flow behavior (‘rheology’) [of polymers] is enormously sensitive to LCB [long chain branching] concentrations far too low to be detectable by spectroscopic (NMR, IR) or chromatographic (SEC) techniques. Thus polyethylene manufacturers are often faced with ‘processability’ issues that depend directly upon polymer properties that are not explainable with spectroscopic or chromatographic characterization data. Rheological characterization becomes the method of last resort, but when the rheological data are in hand, we often still wonder what molecular structures gave rise to those results.”
Janzen and Colby, J. Molecular Structure, 1999
Innovation through Partnerships
2
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Rheology, Processing and Long-Chain Branching
Innovation through Partnerships
3
10-2 10-1 100 101 102101
102
103
104
105
106
G' (
Pa
)
(rad/s)
AFFINITYTM PL1880 PL1880_80 PL1880_60 PL1880_40 PL1880_20 PL1880_05 PL1880_02 Exact 3128
< 1 LCB’s per million carbons significantly affects rheology!
branched thread-like micelles
branched polymers
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Project Goals
• Develop industrially useful tools for inferring long-chain branching levels from rheology
• Develop optimization strategies for improving processing and product properties through control of long-chain branching
• Provide software tools and training as needed for industrial applications
Innovation through Partnerships
4
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Objectives & Research Methods• Measure rheology of commercial polymers • Combine this with conventional
characterization by SEC, light scattering, and knowledge of reaction kinetics
• Use “Hierarchical model”, a computational tool, to determine a long-chain branching profile of commercial polymers.
• Determine how changes in the long-chain branching profile could alter rheological properties in desirable ways.
Innovation through Partnerships
5
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Hierarchical Model
Larson et al., (2001, 2006, 2011)
comb
H
star
linear
• A complex commercial branched polymer is represented by an ensemble of up to 10,000 chains.
• This ensemble represents the range of branching structures and the molecular weight distribution of the commercial polymer.
•The ensemble is generated from a combination of GPC characterization, knowledge of reaction kinetics, and rheology.
•The ensemble is fed into the “Hierarchical Code,” and a prediction of the linear rheology (G’ and G”) emerges.
Das, Inkson, Read, Kelmanson, J. Rheol. (2006)
Relaxation of each molecule is tracked in the time domain, as it relaxes from the tips of the branches, inwards towards the backbone. At long times, branches act as drag centers, slowing down motion of the branch or backbone to which they are attached. The contributions of all molecules in the ensemble to the rheology are combined, and converted to the frequency domain to predict G’ and G’’.
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Example 1: Characterization of Anionically Synthesized “H” Polymer
Synthesized by Rahman and Mays
LinearMw/Mn=1.01
StarMw/Mn=1.03
HMw/Mn=1.07
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Chemically Likely Structures
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Identification of Structures
0 5 10 15 20 25 30 35
n (
a.u
.)
tR (min)
17
18
19
20
21
22
T (
oC)
Mw 38.5k
54.8k
73.6k
Unfractionated Star
0 5 10 15 20 25 30
n (a
.u.)
tR (min)
T (oC
)
14
16
18
20
22
24
26
28
30
32Fractionated H
71k
129k95k
114k
Star (Semi-H):
H:
TGIC from Hyojoon Lee and Taihyun Chang
Using TGIC: Temperature Gradient Interaction Chromatography
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Identification of Structures
0 5 10 15 20 25 30 35
n (
a.u
.)
tR (min)
17
18
19
20
21
22
T (
oC)
Mw 38.5k
54.8k
73.6k
Unfractionated Star
0 5 10 15 20 25 30
n (a
.u.)
tR (min)
T (oC
)
14
16
18
20
22
24
26
28
30
32Fractionated H
71k
129k95k
114k
Star (Semi-H):
H:
TGIC from Hyojoon Lee and Taihyun Chang
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
11
Comparisons of theoretical predictions and experimental measurements
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04
Freq (rad/s)
G',
G''
(Pa)
Me=1620,Gn0=1.095MPa, tau_e=5E-7
Star (semi-H)
H
blend
from Chen, Rahman, Mays, Lee, Chang, Larson
Xue Chen
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
12
Example 2: Blends of Linear Exact 3128 and Branched PL1880 Polyolefins
Sample name AFFINITY™
PL1880 (branched)
Exact 3128
(linear)
LCB (n/1000C) cn Mw
(kg/mol)
AFFINITY™ PL 1880
100% 0% 0.0177 0.0482 116
PL1880_80 80% 20% 0.0142 0.0502 116 PL1880_60 60% 40% 0.0106 0.0521 116 PL1880_40 40% 60% 0.00709 0.0541 116 PL1880_20 20% 80% 0.00355 0.0560 115 PL1880_05 5% 95% 0.000887 0.0575 115 PL1880_02 2% 98% 0.000355 0.0578 115 Exact 3128 100% 0% 0 0.0580 115
X.Chen, C. Costeux, R. Larson. J. of Rheology 54(6) 1185-1206, 2010
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
13
Rheology of Blends of Linear Exact 3128 and Branched PL1880 Polyolefins
10-2 10-1 100 101 102101
102
103
104
105
106
G
' (P
a)
(rad/s)
AFFINITYTM PL1880 PL1880_80 PL1880_60 PL1880_40 PL1880_20 PL1880_05 PL1880_02 Exact 3128
T=150C
Increasing LCB
0.3 LCB’s per million carbons!
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Start
Generate randomnumber R: U(0,1)
R>ppPropagation Termination
Save molecule
Add monomer Add macromonomer
Generate randomnumber R: U(0,1)
R>lp
NO YES
NO YES
monomer addition
addition of unsaturated chain
generation of dead structured chain
-hydride elimination
Reaction kinetics of LCB PE using single-site catalyst
Algorithm for Monte Carlo simulation of LCB PE using
single-site catalyst
Monte Carlo probabilities
Costeux et al., Macromolecules (2002)propagation probability
monomer selection probability
Generating an Ensemble of Chains for a Commercial Single-Site Metallocenes
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
15
A Priori Predictions of Commercial Branched Polymer Rheology
101
102
103
104
105
106
107
108
PL1880_60
PL1880_40PL1880_80
G
' & G
'' (P
a)
AFFINITYTM PL1880
10-2 10-1 100 101 102101
102
103
104
105
106
107
108
10-2 10-1 100 101 102101
102
103
104
105
106
107
108
PL1880_20
PL1880_05
G' &
G''
(Pa)
(rad/s)
10-2 10-1 100 101 102101
102
103
104
105
106
107
108
Exact3128
PL1880_02
(rad/s)
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Outcomes/Deliverables• Measurements of rheological properties of
commercial polymers• Measurement of SEC curves for select commercial
polymers• Computer software and training for predicting
rheological properties• Assessment of impact of changing • branching structure on rheology
Innovation through Partnerships
16
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Impact
• Improved ability to design and control polymer processing properties
• Ability to infer likely branching characteristics from rheology
• Develop methods of extracting “hidden” features of molecular structure through rheology of samples blended with simpler linear polymers
Innovation through Partnerships
17
Nat
iona
l Sci
ence
Fou
ndat
ion
WH
ER
E D
ISC
OV
ER
IES
BE
GIN
Project Duration and Proposed Budget
• 1-4 years, depending on polymer to be tackled, number of samples to be studied, availability of industrial data, such as GPC data, and the solvents/conditions required for characterization
• Budget: $75,000/year
Innovation through Partnerships
18