Putting a speed gun on macromolecules: what can we learn from how fast they go, and can we do something useful with that information?

Monday, October 31 Cleveland State University National Science Foundation

Generic Talk Outline • Thank hosts • Tell joke, story or limerick • Explain what we’re trying to do • Explain what we actually did • Today, that will lead naturally to applied things • Thank accomplices This is what I mainly came to say!

There once was a theorist from France who wondered how molecules dance.

“They’re like snakes,” he observed, “As they follow a curve, the large ones can hardly advance.”

D ~ M

-2 Tons per mole!

P. G. de Gennes Nobel Prize Physics Diffusion

P.G. de Gennes Scaling Concepts in Polymer Physics Cornell University Press, 1979

When does the speed of polymers (and stuff dispersed in them) matter?

• How fast can it dissolve?

• How fast can we process it?

• How long until the additives ooze out? • How long does it take to weld polymers together?

• How fast do chain termination steps occur during polymeriztion?

• How fast will phase separation destroy the polymer?

• Will an image on film (remember film?) stay sharp?

• Speed  Viscosity

DLS for Molecular Rheology of Complex Fluids: Prospects & Problems

Studied a lot Barely studied

+ + + Wide-ranging autocorrelators > 10 decades of time in one measurement!

– – – Contrast stinks: everything scatters, esp.

in aqueous systems or most supercritical fluids, where refractive index matching cannot hide the matrix.

Translational Diffusion Leads to Intensity Fluctuations

Intensity t

Rotational Diffusion Between Polarizers Leads to Intensity Fluctuations Polarizer Looking into the laser, vertically polarized Analyzer Crystalline inclusion

dim dim bright

Dynamic Light Scattering LASER  Uv =

q

2

D

trans

V



Uv Geometry (Polarized)

LASER

V

 Hv =

q

2

D

trans + 6

D

rot

q



H

4 

n

sin   

o

/ 2  

Hv Geometry (Depolarized)

DLS can be used for sizing if viscosity is known, for viscosity if size is known

Large, slow molecules Small, fast molecules

I

s

t

DLS diffusion coefficient, inversely proportional to size.

R h

  

kT

6 πη o

D

trans   Stokes-Einstein Law

D

trans h = constant Also D rot h = constant

g

(

t

)  

Correlation Functions etc.

G

(  ) exp(  

t

)

d

 Where:  = q 2 D  R h G(  ) ~ cMP(qR g ) = XR g q 2 kT/(6 h R h ) g(t) G(  ) ILT CALIBRATE log 10 D log 10 t   1/2 MAP c  q 2 D log 10 M M

Strategy •Find polymer that

should

(???) “entangle” Dextran •Random coil •Polysaccharide •Invisible in HvDLS •Find polymer that should

not

“entangle” Ficoll •Highly-branched •Polysaccharide •Invisible in HvDLS •Find a rodlike probe that is

visible

in DDLS TMV •Measure its diffusion in solutions of each polymer separately •Rigid rod •Virus •Visible in HvDLS BARELY

11 10 9 8 7 4 3 6 5 2 1 0 0 As expected, viscosity rises with BothViscosity c Dextran 670,000 Ficoll 420,000 5 10 15 25 30 35 40 20 c/g-dL -1

DIY farming--keeping the “A” in LSU A&M

Seedlings



Sick Plants



And close-up of mosaic pattern.

TMV Characterization Sedimentation, Electron Microscopy and DLS

•

Most TMV is intact.

•

Some TMV is fragmented

–

(weaker, faster mode in CONTIN)

•

Intact TMV is easy to identify

–

(stronger, slower mode in CONTIN)

All measurements made at low TMV concentrations —no self-entanglement 1 500 0 n L 3 6 400 Translation Rotation 300 200 0.0

Experiments are in dilute regime.

0.5

1.0

1.5

2.0

c/mg-mL -1 2 TMV overlap (1/L 3 ) 1 2.5

3.0

0 5 4 3

1.0

1.2

1.2

1.1

1.0

Matrix is invisible

1.3

TMV + Dextran 215 s acquisition

Dextran >6000 s acquisition 10 100 Hv correlation functions for 14.5% dextran and 28% ficoll with and without added 0.5 mg/mL TMV 0.9

1E-6 1E-5 1E-4 1E-3 0.01

t/s 1.4

0.1

TMV + Ficoll 600s aquisition

1 The dilute TMV easily “outscatters” either matrix

Ficoll >6000 s acquisition

1 10 100 1E-6 1E-5 1E-4 1E-3 0.01

t/s 0.1

Hey, it works!

4000 3500 3000 2500 2000 1500 1000 500 0 0 1

Hv TMV / Buffer Uv TMV / Buffer Hv TMV / Dextran / Buffer

4 5 2 3

q 2 /10 10 cm -2

I didn’t think—I experimented.

---Wilhelm Conrad Roentgen

Early results —very slight errors 350 300 250 200 150 100 50 0 0 2 4 6 8 10 wt% dextran 12 14 16 6 3 2 5 4 1 0 0 2 4 6 8 10 wt% dextran 12 14 16 rotation translation

Macromolecules

1997

,30, 4920-6.

Stokes-Einstein Plots: if SE works, these would be flat. Instead, apparent deviations in different directions for

D

rot and

D

trans 0 2 4 6 8 10 12 14 16 1.5

4 1.0

2 0.5

0 0 2 4 6 8 wt% Dextran 10 12 14 16 0.0

At the sudden transition: L/ x c.m.

~ 13 and L/ x ~ 120 x Dextran overlap

5 0 10 15 20 8 80

x cm

6 60 4 40 2 0 0 5 10

wt % dextran

15 20 20 0

L

Macromolecules

1997

,30, 4920-6.

We believed that the transition represented topological constraints.

It was suggested that more systems be studied. BEGIN FICOLL When we did Ficoll,

many

more points were added!

350 300 250 rotation 200 150 100 50 4 5 6 0 0 2 4 6 8 10 12 14 16 18 wt% ficoll 20 22 24 26 28 30 translation 3 2 1 0 0 2 4 6 8 10 12 14 16 18 wt% ficoll 20 22 24 26 28 30

rotation

Huh?

D

rot

still diving in Ficoll?

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0 5 10 15 20 wt% ficoll 25 0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

30 0.0

translation

Uh-oh, maybe we should think now.

The chiral dextran and ficoll alter polarization slightly before and after the scattering center.

With a strongly depolarizing probe, this would not matter, but… r TMV = I Hv /I Uv ~ 0.003

While matrix scattering is minimal, polarized scattering from TMV itself leaks through a “twisted” Hv setup. Most damaging at low angles

Mixing in Polarized TMV Light

Uv light from misalign True Hv light

  

D rot too low

q 2 6Drot q 2 6Drot q 2

Even at the highest concentrations, only a few degrees out of alignment.

300 250 200 150 100 50 Dextran Ficoll 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 wt %

Slight, but important, improvement.

NewFicollRatio_PR 350 300 250 Right way Wrong way 200 150 100 50 0 0 5 10 15 20 wt% ficoll 25 30 35

Improved

D

rot /

D

trans Ratio Plots NewDexConcStudy_PR 5 4 3 8 7 6 2 1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 wt% dextran 8 7 6 5 4 3 2 1 0 0 NewFicollRatio_PR 5 10 15 20 wt% ficoll 25 30 35 40

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0 2 Improved Stokes-Einstein Plots Black = TMV Translation Blue = TMV Rotation NewFicollRatio_PR NewDexConcStudy_PR 5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

0 4 6 8 10 wt% dextran 12 14 0.8

0.6

0.4

0.2

16 0.0

5 10 15 20 wt% ficoll 25 30 35 0.8

0.6

0.4

0.2

0.0

Hydrodynamic Ratio —Effect of Matrix M at High Matrix Concentration DextranMWStudy_PR 9 5 4 3 8 7 6 2 1 0 0 2 4 6 8 10 12 14 dextran MW/ 10 5 daltons 16 18 20

1 10 Effect of Dextran Molecular Weight — High Dextran Concentration (~ 15%) TMV Translation DextranMWStudy_PR TMV Rotation DextranMWStudy_PR 100 -0.62 ± 0.04

-0.72 ± 0.01

10 0.1

10000 100000 1000000 Dextran MW 1E7 1 10000 100000 1000000 Dextran MW 1E7

Summary: Depolarized DLS = new opportunities in nanometer-scale rheology.

Randy Cush David Neau Ding Shih Holly Ricks Jonathan Strange Amanda Brown Zimei Bu Grigor Bantchev Zuhal & Savas Kucukyavuz--METU Seth Fraden —Brandeis Nancy Thompson —Chapel Hill

I cannot tell you the coolest part of this, but postdoc Grigor Bantchev found a trick that is definitely a treat!

“Too much dancing and not nearly enough prancing!”

Can probe diffusion actually

do

something?

C. Montgomery Burns, “The Simpsons”

Matrix Fluorescence Photobleaching Recovery for Macromolecular Characterization Garrett Doucet, Rongjuan Cong, David Neau, Others Louisiana State University Funding: NSF, NIH, Dow

Blue input light Green Detected Light Fluorescent Sample Fluorescence & Photobleaching

Blue input light Green Detected Light Slowly Recovers Fluorescent Sample With Fluorescence Hole in Middle Recovery of Fluorescence

Modulation FPR Device

Lanni & Ware, Rev. Sci. Instrum. 1982 SCOPE I F 5-10% bleach depth c PMT PA TA/PVD * D X  * DM S * OBJ RR * ARGON ION LASER AOM M M * = computer link

Cue The Movie

Dextran Diffusion in Hydroxy propylcellulose, a probe diffusion study: the more HPC, the more nonlinearity in semilog plots.

Hmmm….

Bu & Russo, Macromolecules, 27, 1187 (1994)

Can FPR be used for MWD characterization?

Questions bearing on this • Need: are new analytical methods needed in a GPC/AFFF multidetector world?

• Ease of labeling the analyte?

• How hard to calibrate?

• Worth the price of setup?

• Miniaturization?

GPC

•Solvent flow carries molecules from left to right; big ones come out first while small ones get caught in the pores.

•Non-size mechanisms of separation complicate regular GPC, are much less of a problem for multidetector methods, but they correspondingly more complicated.

They were young when GPC was.

Small Subset of GPC Spare Parts

To say nothing of unions, adapters, ferrules, tubing (low pressure and high pressure), filters and their internal parts, frits, degassers, injector spare parts, solvent inlet manifold parts, columns, pre-columns, pressure transducers, sapphire plunger, and on it goes…

Other SEC Deficiencies

• 0.05 M salt at 11 am, 0.1 M phosphate pH 6.5 at 1 pm?

• 45 o C at 8 am and 80 o C at noon? • Non-size exclusion mechanisms: binding.

• Big, bulky and slow (typically 30 minutes/sample).

• Temperature/harsh solvents no fun.

• You learn nothing fundamental by calibrating. • For straight GPC, what you measure is not what you calibrated. Good for qualitative work, otherwise problematic.

Must we separate ‘em to size ‘em?

Your local constabulary probably doesn’t think so.

Atlanta, GA I-85N at Shallowford Rd.

A Saturday at 4 pm

Molecular Weight Distribution by

g

(

DLS/Inverse Laplace Transform--B.Chu, C. Wu, &c.

t

)  

G

(  ) exp(  

t

)

d

 Where:  = q 2 D  R h G(  ) ~ cMP(qR g ) = XR g q 2 kT/(6 h R h ) g(t) G(  ) ILT CALIBRATE log 10 D log 10 t   1/2 MAP c  q 2 D log 10 M M

Hot Ben Chu / Chi Wu Example

Macromolecules

, 21, 397-402 (1988) MWD of PTFE Special solvents at ~330 o C Problems: •Only “works” because MWD is broad •Poor resolution.

•Low M part goofy. •Some assumptions required.

log

Matrix Diffusion/Inverse Laplace Transformation Goal: Increase magnitude of



—this will improve resolution

.

10 D D Solution:   1/2  Difficult in DLS because matrix scatters, except special cases.

 Difficult anyway: dust-free matrix not fun!

 Still nothing you can do about visibility of small scatterers  DOSY not much better D Matrix:    log Stretching  10 M  Replace DLS with FPR.

 Selectivity supplied by dye.

 Matrix = same polymer as analyzed, except no label.

 No compatibility problems.

 G(  ) ~ c (sidechain labeling)  G(  ) ~ n (end-labeling)

Sample

The Plan to Measure M Using FPR

Collect Data Using FPR

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

10 -3 10 -2



/ s -1 10 -1 10 0

Convert to Molar Mass by Mapping onto Calibration Plot

4 3 6 5 2 1 0 0 200 400 t / s 600 800 1000

Analyze Using ILT

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

10 3 10 4

M

/ Da 10 5

Labeling is Often Easy H O OH O OH OH OH n

Dextran M = 2 Million Da as the matrix at different concentrations in 5 mM NaN 3 solution

H O OH O OH OH O O

Pullulan

OH OH O O OH O OH OH O OH n OH OH COOH Cl

Pullulans of different M labeled with 5-DTAF as probes

N N NH N Cl

5-(4,6-dichlorotriazinyl)amino fluorescein

Matrix FPR for Pullulan (a polysaccharide)

10 1 0.1

0.01

10 4 NaN 3 (aq) solution (  = 0.537 ± 0.035) 5% Matrix solution (  = 0.822 ± 0.018) 10% Matrix solution (  = 0.907 ± 0.038) 15% Matrix solution (  = 0.922 ± 0.037) 10 5

M

Probe Diffusion: Polymer physics 10 5 10 4 0.1

1

D app

/ 10 -7 cm 2 s -1 10 Calibration: polymer analysis

GPC vs. FPR for a Nontrivial Case

1.0

0.9

0.4

0.3

0.2

0.1

0.8

0.7

0.6

0.5

0.0

10 4

M

/ g mol -1 10 5 PL Aquagel 40A & 50A 12 10 8 6 4 2 0 10 4 10 5 M User-chosen CONTIN 25% Matrix  only ~1 20,000 & 70,000 Dextran

How Good COULD it Be? Simulation of FPR Results for



= 2

(Most Desirable Situation) 6 4 2 0 -2 0 -4 -6 -8 -10 -12 -10 y = -2.0009x + 2.3045

2 4 6 8 y = -0.4998x + 1.1518

4 5 3 2 log M 1 -8 -6 log D -4 -2 0 0

What could we separate from 10K, according to



= 2 simulations?

Shazamm!

10000

M

Detected 100000 20000 40000 57000 80000 113000 160000

M

S im ul ate d

1.0

0.8

0.6

0.4

0.2

0.0

1000

Using an HPC Matrix

Pullulan, 8% HPC Solution, M=12,200 and 48,000 CONTIN Exponential Exponential 10000 M 100000 1000000  Indicates targeted M.

MFPR Conclusions

We are entitled to expect something better than GPC.

For some situations, MFPR could really work. What is good about GPC (straight GPC) is the simple concept; Matrix FPR keeps that—just replaces V e with D . We haven’t yet addressed two questions --Is it worth setting this up?

--Miniaturization/Automation?

Macromolecules for The Demented

and methods for their study Help from Keunok Yu, Jirun Sun, Bethany Lyles, George Newkome and LSU’s Alz-Hammer’s Research Team Krispy Kreme Donut Day, September 2003 Supported by National Institutes of Health-AG, NSF-DMR and NSF-IGERT • How Alzheimer’s happens • Attempts to prevent or reverse it • Characterization challenges • Alzheimer’s model systems with materials implications

Positron emission tomography Age: 20 -- 80 Normal -- 80 AD Postmortem Coronal Sections Normal Alzheimer’s PET images courtesy of the Alzheimer's Disease Education and Referral Center/National Institute on Aging; Postmortem images courtesy of Edward C. Klatt, Florida State University College of Medicine

APP = Amyloid Precursor Protein http://www.bmb.leeds.ac.uk/staff/nmh/amy.html

APP = the larger, lighter pink one •Transmembrane protein •Normal function not known •Educated guesses May help stem cells develop identity Or help relocate cells to final location May “mature” cells into structural type May protect brain cells from injury Synaptic action Copper homeostasis •Anyway, you need it.

•Normal “clipping” of APP by a “secretase” enzyme (in red, and also assumed to be a transmembrane protein) is shown.

•There are several secretases, also associated proteins, and they seem to mutate easily: there is a genetic link.

•It is not exactly clear why things go awry with advanced age.

Amyloid hypothesis: fibrils or protofibrils cause cell death, possibly as the body’s own defenses tries to clear such “foreign” matter.

Peter Lansbury Group http://focus.hms.harvard.edu/1998/June4_1998/neuro.html

Competing hypothesis: channel formation disrupts Ca +2 metabolism

Two FPR Contrast Decay Modes are Often Observed: Fast = small; Slow = large.

1 pH 2.7

pH 6.9

pH 11 0.1

0.01

1E-3 1 10 100 t/s 1000

Doing More Experiments Faster with Less Precious Amyloid: Dialysis FPR

Pump Exchange Fluid Cover slip Sample PTFE spacer Dialysis membrane O-ring

Reversing Amyloid Aggregation…by pH

FPR Study: Reversibility of  -Amyloid Aggregation 100  M 5-CF  -Amyloid 1-40 +  -Amyloid 1-40 pH 11 dialysis against 50mM PB pH 7.4

1E-6 1E-7 dialysis against 50mM PB pH 2.7

1E-8 0 200 400 600 800 1000 1200 1400 1600 1800 Time/min

Diffusion from in situ FPR of 5-carboxyfluorescein-A unlabeled 75% A



1-40



1-40 mM phosphate (pH 2.7) and 50 mM phosphate (pH 7.4).

(25% mixed with ) starting at pH 11, then alternately dialyzed between 50

Probe diffusion works at fundamental and practical levels.

Happy Halloween!

M = 10,000 and 20,000 2.0

1.5

1.0

0.5

CONTIN 2 Exponential 0.0

1000 10000 M M = 10,000 and 57,000 100000 2.0

1.5

CONTIN 2 Exponential 1.0

0.5

0.0

1000 10000 M 100000 Examples of Separation Results from Simulation Data 2.0

1.5

1.0

0.5

0.0

1000 M = 10,000 and 160,000 10000 M 100000 CONTIN 2 Exponential 1000000  Indicates targeted M.

25 20 15 10 5 0 1000 30 40 35 45

Matrix FPR Chromatogram

Pullulan, 5%w/w Dextran Matrix, 50/50 mix of 380K and 11.8K

CONTIN Analysis Exponential Analysis Exponential Analysis

Sure this is easy. Also easy for GPC.

But not for DLS or DOSY!

10000 100000 1000000

M

 Indicates targeted

M

.

Making the M vs.

D calibration is fast & easy } 6 fractions from analytical scale GPC Enough for 100’s of FPR runs in ½ hour

M

w /

M

n ’s as now as good as anionically polymerized, patchy standards.

Cong, Turksen & Russo

Macromolecules 37(12),

4731-4735 (2004)

“Cleanup on Aisle 1”

Millipore Centricon Device Millipore Centricon - http://www.millipore.com/userguides.nsf/docs/p99259 Pre-poured gel filtration columns are also very useful.

Analytical scale GPC itself is a great way to clean up unreacted dye.

Why is the cup half empty?

R

g ~

M

(0.158 ± 0.002) 10

R

g ~

M

(0.410 ± 0.005) 1 10 5

M

10 6

10

Half empty, continued

10 4

M

/ g mol -1 10 5 10 1 1 0.1

w

dextran ( ● ), and pullulan probes ( ○ ). 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

0.0

0.1

0.2

0.3

R

h / x 0.4

0.5

0.6

0.7

Pullulan (destran similar)

No wonder the cup is half empty— no plateau modulus!

100 10 1 0.1

0.01

1 10  / Hz 100

Correlations—suggests soft-sphere like behavior from branching of matrix.

50 45 40 35 30 25 20 15 10 5 0 0.0

0.1

0.2

0.3

0.4

0.5

q

2 / nm -2 0.6

0.7

0.8

0.9

1.0