unifying chromatography to meet business needsapproach: parameter interactions •...

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© 2007 T. L. Chester

Unifying Chromatography to Meet Business Needs

Thomas L. Chester7122 Larchwood DriveCincinnati, OH 45241

[email protected]


Acknowledgments:Procter & GambleClaudia Smith David InnisDavid Pinkston Rosemary HentschelDoug Raynie Brian HaynesTom Delaney Don BowlingGrover Owens Lisa BurkesJianjun Li Rebecca CunninghamChris Ott Steve TeremiDimitra Simmons Chris GamskyJim Ziegler Jason CoymLynn Cole Steve Page

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History and overall perspective

• Chromatography is 100+ years old• Partition chromatography, 1941 (Martin & Synge)• GC, 1952 (Martin & James)• HPLC, ~1970• New possibilities require us to think differently


Unification:

• Don’t be limited in practice by boundaries that don’t really exist.

• 3 unification topics:1. Chromatography from the mobile phase perspective2. Performance expectations3. Parameter interactions, optimization, and opportunities.

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1. The Mobile Phase Perspective

SolidLiquid

Vapor

TemperatureTc

Pc

TriplePoint

CriticalPoint

SolidLiquid

Vapor

TemperatureTc

Pc

TriplePoint

Supercritical Fluid

Region

a) b)

Pre

ssur

eSupercritical Fluid according to IUPAC and ASTM


Fluids…

YesYes, variable, and it depends on compression

Supercritical, etc.

YesNoGases

NoYesLiquids

CompressibleSolvating

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1. The Mobile Phase Perspective

SolidLiquid

Vapor

TemperatureTc

Pc

TriplePoint

CriticalPoint

SolidLiquid

Vapor

TemperatureTc

Pc

TriplePoint

Supercritical Fluid

Region

a) b)

Pre

ssur

eSupercritical Fluid according to IUPAC and ASTM


Temperature

systempressurelimit

systemtemperature

limit

C)

solidliquid

vapor

one-phaseregion available

for chromatography

5


Adding a second component requires another dimension in

the phase diagram:

T

P

100% a

100% b

a is the more volatile component


The critical mixture curve is the locus of mixture critical points:

T

P100% b

100% a

Type I Binary Mixture

6


systempressurelimit

systemtemperature

limit

Temperature

a

b


systempressurelimit

systemtemperature

limit

Temperature

a

Supercritical FluidChromatography range

OT-SFCrange (with one-component mobilephase)

systempressurelimit

systemtemperature

limit

Temperature

a

Unnamed range

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systempressurelimit

systemtemperature

limit

Temperature

a

Subcritical FluidChromatography range system

pressurelimit

systemtemperature

limit

(Extended) Enhanced-Fluidity Liquid Chroma-tography range

Temperature

a


Unified Chromatographyfrom the mobile phase perspective

systempressurelimit

systemtemperature

limit

Temperature

a

One-phaseregion available

for chromatography

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Temperature

systempressurelimit

systemtemperature

limit

C)

solidliquid

vapor

one-phaseregion available

for chromatography


systempressurelimit

systemtemperature

limit

Temperature

a

b

GC range

Conventional LC and GC are limiting cases of Unified Chromatography

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Unified Chromatography

systempressurelimit

systemtemperature

limit

Temperature

a

One-phaseregion available

for chromatography


Why?

• Save money• Reduce expense• Reduce time to market

• 10,000 20-min analyses require 139 days• Reducing to 3-min analyses requires 21 days

and saves 118 days• For a $100M/year product:

$32M in sales$3M in earnings

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How can we save time?

Plate generation takes time, so

• Improve selectivity to reduce the required plate number

• Generate plates faster• Increase the diffusion rate• Decrease critical dimensions


Evidence from elsewhere…

See Gerd Vanhoenacker, Pat Sandra, J. Sep. Science 2006, 29, 1822-1835 for an example of the influences of temperature change on selectivity and diffusion.

They took a 12-min separation that was inadequate, and resolved everything in about 2 min by increasing the temperature and the flow rate. These changes improved the selectivity (to reduce the plates required) and increased the rate at which plates are produced.

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Unified techniques offer…

• Different, potentially better selectivity• Faster plates per unit time


2. Performance expectations:• Unified techniques should provide the same

figures of merit as HPLC, only faster. • Injection and detection interfacing must be

changed.

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Let’s use SFC as an example to look at a few problems

• Complaint: “Peak area precision is not as good in SFC as in HPLC even when both use the same injector.”

• Experiment: Gilson SF-3 with model 234 autosampler• Specification: RSD=0.5%• HPLC (n=10): RSD=0.38%• SFC (n=10): RSD=7% (same autosampler protocol)


The loop is pressurized while in the Inject position. CO2 is

liquid at ambient temperature.

from the pump or mixerto the column inlet

A. Inject position

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When switched to Load, the loop is vented. CO2 is gaseous

at ambient temperature.

from the pump or mixer

to the column inlet

B. Load position



• Complaint: “Peak area precision is not as good in SFC as in HPLC even when both use the same injector.”

• Experiment: Gilson 234 autosampler• Specification: RSD=0.5%• HPLC (n=10): RSD=0.38%• With our changes:

SFC (n=15): RSD=0.25% (worst case of 3 trials)

• RSD can be just as good in SFC

J. W. Coym and T. L. Chester, J. Sep. Sci. 2003, 26, 609–613.

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SFC calibration chart for 4-hydroxybenzyl isothiocyanate assay in mustard extract

R2 = 0.9988

0

2000

4000

6000

8000

10000

12000

14000

0 1 2 3 4 5 6mg/mL

Pea

k ar

ea



• Complaint: “Signal/Noise ratios are worse in SFC than in HPLC”

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Possibilities…

• Signal—two common oversights:• Analytical SFCs are sometimes furnished with ‘prep’ UV

detector cells (0.5-mm path).• Peaks with the same temporal width will produce smaller

signals in concentration detectors (like UV) in a system with faster flow (like SFC).

• Noise…


HPLC

Mobile phase strength is “constant” from the injector to the outlet.

Pressure is “constant” and fairly low in the detector.

Pump A,Weaksolvent(Flow-controlled)

Pump B,Modifier(Flow-controlled)

ColumnOven

Detector

12

Injector

16


Unified—SFC

A back pressure regulator is required to prevent the MP from expanding, weakening and separating.

Flow-through detectors must be operated at system pressure, not atmospheric pressure.

Pump A,Main Fluid(Flow-controlled)

Pump B,Modifier/ Additives(Flow-controlled) Column

Oven

High-PressureDetector

Back-PressureRegulator

12

3

Injector


Possibilities…• Signal:• Analytical SFCs are sometimes furnished with ‘prep’

detector cells (0.5-mm path).• Peaks with the same temporal width will produce smaller

signals in concentration detectors (like UV) in a system with faster flow (like SFC).

• Noise:• Pressure fluctuations in the BPR and detector

• Switching BPR—a possible noise source• PID BPR—highly unlikely

• Oscillations in the detector cell or in coupled components

• Refractive index of compressible fluids is ~100x more sensitive to pressure and temperature changes than non-compressible fluids.

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RI of CO2 at 40°C

See Sun, Y.; Shekunov, B. Y.; York, P. Chem. Eng. Commun. 190, p. 1-14 (2003). CO2 changes it RI value from about 1.04 as a low-density supercritical fluid to about 1.9 as a high-density supercritical fluid. Water changes only 1% from 10 to 90°C and is virtually flat with pressure.


Unified—SFC

Pump A,Main Fluid(Flow-controlled)

Pump B,Modifier/ Additives(Flow-controlled) Column

Oven

High-PressureDetector

Back-PressureRegulator

Injector

temperatureis changing

If RI is not uniform, then eddies and convection currents willrandomly refract light in the detector and create noise.

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Solution?

• Stabilize the temperature (and the pressure) in the detector—make sure the RI is uniform.


Don’t settle for any S/N compromise in SFC or any unified

technique

Find the problem and fix it.

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3. Parameter Interactions, Optimization, and Opportunities…

• In the workplace, we really don’t understand the parameter interactions.

• We’ll use HPLC as an example…


The Need for Modeling and a Multivariate Approach: parameter interactions

• Resolution-controlling parameters (Purnell):•Efficiency (5)•Selectivity (5)•Retention factors (5)

But these are not directly adjustable…

• Performance measures:•Resolution•Analysis time•Pressure and Flow limits•(Accuracy, precision, etc.)

( )( )R

N k

kS =−

+41

12

2

αα

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The Need for Modeling and a Multivariate Approach: parameter interactions

• Resolution-controlling parameters (Purnell):•Efficiency (5)•Selectivity (5)•Retention factors (5)

But these are not directly adjustable…

• Independently adjustable parameters:• Column length, L• Column diameter, dc• Particle size, dp• Flow rate, F• Stationary phase• Mobile phase modifier• pH• Temperature• Modifier concentration, %B

• Performance measures:•Resolution•Analysis time•Pressure and Flow limits•(Accuracy, precision, etc.)

• Adjustable HPLC parameters are highly interrelated

• Univariate optimization will not work


Leveraging the complexity

• Modeling and virtual experimentation• Multivariate approach to optimization

• Combine particle size, column dimensions, and pressure and flow constraints with all other adjustable efficiency parameters to maximize the overall performance.

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Examine the complexity of a simple reversed-phase separation

0 4 8 12 16 min

0.00

0.10

0.20

0.30

0.40

uracil

butylparaben

propranolol

naphthalene

acenaphthene

amitriptyline

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Vary only F and %B, L = 5 and 10 cm,Rs � 2.0, P � 10-MPa

0

5

10

15

20

25

30

0 5 10 15 20

Column length (cm)

Ana

lysi

s tim

e (m

in)

best solution, excess Rs,pressure-limited

best solution, meets Rs,not pressure-limited


Solutions for 5- and 10-cm,Rs = 2.0, 10 MPa limit

0

5

10

15

20

25

30

0 5 10 15 20

Column length (cm)

Ana

lysi

s tim

e (m

in)

23


Solutions for 5- and 10-cm

notpressure

limited

pressure limited at:10 MPa

20 MPa

30 MPa

0

5

10

15

20

25

30

0 5 10 15 20

Column length (cm)

Ana

lysi

s tim

e (m

in)

a


But at 7.5 cm…

notpressure

limited


20 MPa

30 MPa

0

5

10

15

20

25

30

0 5 10 15 20

Column length (cm)

Ana

lysi

s tim

e (m

in)

a

24


At 30 MPa, 65% savings

notpressure

limited


20 MPa

30 MPa

0

5

10

15

20

25

30

0 5 10 15 20

Column length (cm)

Ana

lysi

s tim

e (m

in)

a

The best column length depends on the pressure limit…and on many other parameters.


Loci of optimal analysis times as a function of the pressure limit

0

10

20

30

40

50

60

70

80

90

0 5 10 15 20 25 30

Pressure limit, MPa

Ana

lysi

s tim

e, m

in

pressure-limited

not pressure-limited

L =25 cm

L =20 cm

L =15 cm

pressure-limitedpressure-limited

not pressure-limited

1500 psi

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The point of this is to show

• …how complex the interdependencies are, even when only three variables are considered

• And that counter-intuitive changes in parameter values are often required to go faster


4 cold remedy actives—Best analysis time vs. column length, 2200 psi

limit

Minimum time for Rs=4.0

0

5

10

15

20

25

30

0 5 10 15 20 25 30

Column length, cm

Tim

e, m

in Standard HPLC, 5umLow-vol HPLC, 5umStandard HPLC, 3umLow-vol HPLC, 3um

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Same problem, higher resolution

Minimum time for Rs=4.5

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30

Column length, cm

time,

min Standard HPLC, 5um

Low-vol HPLC, 5umStandard HPLC, 3umLow-vol HPLC, 3um


Another approach: maximum resolution with a10-min time limit and 2200 psi max

Best Rs in 10 minutes

0

1

2

3

4

5

6

0 5 10 15 20 25 30

Column length, cm

Rs

Standard HPLC, 5umLow-vol HPLC, 5umStandard HPLC, 3umLow-vol HPLC, 3um

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Optimization Considerations Around Particle Size

• Consider 3.5 µµµµm vs. 1.7 µµµµm particles• Efficiency: Hmin becomes half• N/L doubles, or L is half for the same N• uopt doubles• tR is quarter—75% time savings• pressure goes up by 4x• extracolumn volumes become deadly• data acquisition rates must be faster


Example reversed-phase separation

0 4 8 12 16 min

0.00

0.10

0.20

0.30

0.40

uracil

butylparaben

propranolol

naphthalene

acenaphthene

amitriptyline

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Same problem considering dp, %B, F and L; 0.001-mL extra-column volume; Rs � 2.0

µµµµ

constraints:1 mL/min, 103 MPa

2 mL/min,62 MPa

5 mL/min,41 MPa

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6

Particle size ( m)

Ana

lysi

s ti

me

(min

) (15,000 psi)

(6,000 psi)

(9,000 psi)


• But, these are sparse, isocratic chromatograms. • What about gradients with maximum peak capacity?

• Consider maximizing the peak capacity within a set time limit, a set retention range, and a maximum pressure—set the time, set the range, set the pressure limit, and minimize the peak width.

Peaks don’t behave like we thought!

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Consider the peak width vs. particle size.Analysis time and retention range are fixed.

No pressure limit.

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (r

elat

ive

scal

e)

L


Consider the peak width vs. particle sizeAnalysis time and retention range are fixed

No pressure limit

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (r

elat

ive

scal

e)

L

2L

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Consider the peak width vs. particle sizeAnalysis time and retention range are fixed

No pressure limit

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (r

elat

ive

scal

e)

L

2L

3L


If we start with large particles and decrease, we will eventually hit the pressure limit

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (re

lativ

e sc

ale)

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0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (re

lativ

e sc

ale)



0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (re

lativ

e sc

ale)

Locus of pressure-limitedsolutions at pressure = P

L and dp both increasing toreduce w at pressure = P

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0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (re

lativ

e sc

ale)

P2P

3P

These are analytical solutions (that is, algebraic) with lots of simplifying assumptions.


Assumptions will eventually break down—Likely outcome: an optimal dp and L combo

for a given problem and pressure limit

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5

particle size (µm)

peak

wid

th (re

lativ

e sc

ale)

Locus of pressure-limitedsolutions at pressure = P

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Preliminary numeric solutions:

Peak width vs. dpfixed 60-min gradient time and fixed solute range

0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5 6

dp, µµµµm

peak

wid

th, m

in L2L3L5LP limit:225 barP limit: 620 barP limit: 1000 bar


Optimization Conclusions• No parameter, not even particle size, if

considered alone will optimize the separation.• All the adjustable parameters must be

considered in concert.• Savings can be big.• Results can be surprising.• Ultra-small particles will be most valuable for

problems requiring lots of plates or lots of peak capacity.

• Assay methods involving a few peaks of interest will usually run faster with larger particles.

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Our Current Picture of Unification:

1. Expand our choices of fluids, and add both temperature and pressure to our control parameters to achieve higher selectivity and faster plate generation for specific problems.

2. Use all adjustable parameters (including temperature and pressure) in a multivariateoptimization to deliver business needs in minimum time.


Additional references for the MASSEP presentation, October 18, 2007:Unified Chromatography49. Chromatography from the Mobile Phase Perspective, T. L. Chester, Anal. Chem. 69, 165A-

169A, (1997).55. Unified Chromatography, J. F. Parcher and T. L. Chester, Eds., ACS Symposium Series,

American Chemical Society, Washington, D.C., U. S. A. (2000).SFC injection and detector interfacing53. Pressure-Regulating-Fluid Interface and Phase Behavior Considerations in the Coupling of

Packed-Column Supercritical Fluid Chromatographs with Low-Pressure Detectors, T. L. Chester and J. D. Pinkston, J. Chromatogr. A, 807, 265-273 (1998).

64. Improving Injection Precision in Packed-Column Supercritical Fluid Chromatography, J. W. Coym and T. L. Chester, Journal of Separation Science 26, 609-613 (2003).

50. Injection Techniques in SFC, T. Greibrokk and T. L. Chester, Analusis 27, 672-680 (1999).71. Supercritical Fluid Chromatography Instrumentation, T. L. Chester and J. D. Pinkston,

appearing in "Ewing’s Analytical Instrumentation Handbook, 3rd Edition" J. Cazes, Editor, Marcel Dekker, New York (2005).

Modeling and multivariate optimization66. Business-Objective-Directed, Constraint-Based Multivariate Optimization of HPLC

Operational Parameters, T. L. Chester, Journal of Chromatography A, 1016 (2003) 181–193.69. A Virtual-Modeling and Multivariate-Optimization Examination of HPLC Parameter

Interactions and Opportunities for Saving Analysis Time, T. L. Chester and S. O. Teremi, Journal of Chromatography A, 1096 (2005) 16–27.

70. Business-Needs-Driven, Constraint-Based HPLC Optimization, T. L. Chester, American Laboratory, 37:24 (2005), 25-28.

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