Charged Aerosol Detection for Liquid Chromatography and Related Separation Techniques E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 866

Veröffentlichungsjahr: 2017

Beschreibung

The first book devoted exclusively to a highly popular, relatively new detection technique Charged Aerosol Detection for Liquid Chromatography and Related Separation Techniques presents a comprehensive review of CAD theory, describes its advantages and limitations, and offers extremely well-informed recommendations for its practical use. Using numerous real-world examples based on contributors' professional experiences, it provides priceless insights into the actual and potential applications of CAD across a wide range of industries. Charged aerosol detection can be combined with a variety of separation techniques and in numerous configurations. While it has been widely adapted for an array of industrial and research applications with great success, it is still a relatively new technique, and its fundamental performance characteristics are not yet fully understood. This book is intended as a tool for scientists seeking to identify the most effective and efficient uses of charged aerosol detection for a given application. Moving naturally from basic to advanced topics, the author relates fundamental principles, practical uses, and applications across a range of industrial settings, including pharmaceuticals, petrochemicals, biotech, and more. * Offers timely, authoritative coverage of the theory, experimental techniques, and end-user applications of charged aerosol detection * Includes contributions from experts from various fields of applications who explore CAD's advantages over traditional HPLC techniques, as well its limitations * Provides a current theoretical and practical understanding of CAD, derived from authorities on aerosol technology and separation sciences * Features numerous real-world examples that help relate fundamental properties and general operational variables of CAD to its performance in a variety of conditions Charged Aerosol Detection for Liquid Chromatography and Related Separation Techniques is a valuable resource for scientists who use chromatographic techniques in academic research and across an array of industrial settings, including the biopharmaceutical, biotechnology, biofuel, chemical, environmental, and food and beverage industries, among others.

Leseprobe

Table of Contents

Cover

Title Page

List of Contributors

Preface

Acknowledgment

Section 1: Fundamentals of Charged Aerosol Detection

1 Principles of Charged Aerosol Detection

1.1 Summary

1.2 History and Introduction to the Technology

1.3 Charged Aerosol Detection Process

1.4 CAD Response Model

1.5 Performance Characteristics

References

2 Charged Aerosol Detection

2.1 Introduction

2.2 CAD History and Background

2.3 Application Areas

2.4 Conclusions

Acknowledgements

References

3 Practical Use of CAD

3.1 Summary

3.2 Introduction

3.3 Factors Influencing CAD Performance

3.4 System Configurations

3.5 Method Transfer

3.6 Calibration and Sensitivity Limits

References

4 Aerosol‐Based Detectors in Liquid Chromatography

4.1 Summary

4.2 Introduction

4.3 Universal Detection Methods

4.4 Factors Affecting the Response in Charged Aerosol Detection

4.5 Gradient Compensation

4.6 Response Models

4.7 Green Chemistry

4.8 Temperature Gradient Separations

4.9 Supercritical CO2 Separations

4.10 Capillary Separations

4.11 Global Analysis and Multidimensional Separations

4.12 Conclusions

References

Section 2: Charged Aerosol Detection of Specific Analyte Classes

5 Lipid Analysis with the Corona CAD

5.1 Introduction

5.2 Principles of Chromatographic Separation of Lipids

5.3 Application: Strategy of Lipid Separation

5.4 Literature Review: Early Use of Corona CAD in Lipid Analysis

5.5 Calibration Strategies

References

6 Inorganic and Organic Ions

6.1 Introduction

6.2 Technical Considerations

6.3 Applications

6.4 Concluding Remarks

References

7 Determination of Carbohydrates Using Liquid Chromatography with Charged Aerosol Detection

7.1 Summary

7.2 Liquid Chromatography of Carbohydrates

7.3 Charged Aerosol Detection

7.4 Why LC‐CAD for Carbohydrate Analysis?

7.5 Early Applications of CAD to Carbohydrate Analysis

7.6 Additional Applications of CAD to Carbohydrate Analysis

References

8 Polymers and Surfactants

8.1 Summary

8.2 Introduction

8.3 Polymer Analysis

8.4 Polyethylene Glycol

8.5 Surfactants

References

9 Application of Charged Aerosol Detection in Traditional Herbal Medicines

9.1 Summary

9.2 Introduction

9.3 Factors that Affect the Sensitivity of CAD

9.4 Application of CAD in Quality Analysis of Traditional Herbal Medicines

9.5 Conclusion

References

Section 3: Industrial Applications of Charged Aerosol Detection

10 Charged Aerosol Detection in Pharmaceutical Analysis

10.1 Summary

10.2 Introduction

10.3 Analytical Method Development

10.4 Analytical Method Validation

10.5 CAD in Analytical Method Transfer

10.6 CAD in Formulation Development and Ion Analysis

10.7 Carbohydrate Analysis by CAD

10.8 CAD in Stability Analyses

10.9 Conclusion

References

11 Impurity Control in Topiramate with High Performance Liquid Chromatography

11.1 Summary

11.2 Introduction

11.3 Material and Methods

11.4 Results and Discussion

11.5 Conclusion

Acknowledgment

References

12 Applying Charged Aerosol Detection to Aminoglycosides

12.1 Introduction

12.2 Development and Validation of an RP‐HPLC Method for Gentamicin Using Charged Aerosol Detection

12.3 Application of Strategy to Netilmicin Sulfate

12.4 Conclusion

Acknowledgments

References

13 Determination of Quaternary Ammonium Muscle Relaxants with Their Impurities in Pharmaceutical Preparations by LC‐CAD

13.1 Summary

13.2 Introduction

13.3 Experimental

13.4 Results and Discussion

13.5 Conclusion

Acknowledgments

References

14 Charged Aerosol Detection of Scale Inhibiting Polymers in Oilfield Chemistry Applications

14.1 Summary

14.2 Background to Scale Inhibition in Oilfields

14.3 Historical Methods of Analysis

14.4 Charged Aerosol Detection for Polymeric Scale Inhibitors

14.5 Conclusions and Further Work

References

15 Applications of Charged Aerosol Detection for Characterization of Industrial Polymers

15.1 Introduction

15.2 Liquid Chromatography of Polymers

15.3 Solvents

15.4 Quantitative Detection of Polymer Molecules

15.5 Size Exclusion Chromatography and Charged Aerosol Detection

15.6 Gradient Polymer Elution Chromatography and CAD

15.7 Liquid Chromatography Combined with UV, CAD, and MS Detection

15.8 Typical Examples of Industrial Applications Using LC‐MS‐CAD

15.9 Epilogue

Acknowledgments

References

Index

End User License Agreement

List of Tables

Chapter 01

Table 1.1 Estimated number of molecules for a given particle diameter.

Table 1.2 Parameters and property values for N‐T model.

Table 1.3 Dried particle size dependency on solute concentration and density.

Table 1.4 Ion trap parameters.

Table 1.5 Parameters and results for the rate of residue particle formation for water and acetonitrile.

Chapter 02

Table 2.1 Comparison of CAD performance to other detectors for different applications/markets.

Table 2.2 Charged aerosol detector development.

Table 2.3 Review publications of the operating principles and performance of CAD and other nebulizer‐based detectors.

Table 2.4 An overview of CAD publications relevant to carbohydrates.

Table 2.5 An overview of CAD publications relevant to lipids.

Table 2.6 An overview of CAD publications relevant to natural products/supplements.

Table 2.7 An overview of CAD publications relevant to pharmaceutical and biopharmaceutical industries.

Table 2.8 Miscellaneous CAD publications.

Chapter 03

Table 3.1 Main differences between first‐ and second‐generation CAD instruments.

Table 3.2 Volatile additives and typical concentrations.

Chapter 04

Table 4.1 Selected physical and chemical properties of acetonitrile and alternatives.

Table 4.2 Critical properties of various solvents.

Chapter 05

Table 5.1 Examples of lipid structures from various classes.

Table 5.2 Lipid class composition of various plants (wt% of total lipids) [32].

Table 5.3 Lipid classes of rat tissues (wt% of total lipids) [32].

Table 5.4 Number of TAG isomers according to the number (

N

) of FA residues.

Table 5.5 Practical example of the number of TAG to be separated in a vegetable oil.

Table 5.6 Coefficients of Equation 5.17 for the calibrations shown in Figure 5.4.

Chapter 08

Table 8.1 Percentage of the dimer in seven lots of PEG reagents determined by RI, ELSD, and CAD.

Table 8.2 Polydispersity data of seven lots of PEG reagents determined by CAD and ELSD with TurboSEC software.

Table 8.3

M

w

,

M

n

, and molecular mass distributions (

M

w

/

M

n

) of CRM PEG 1000 determined by CAD and ELSD compared to the certified values.

Table 8.4 Regression analysis with the two models used.

Chapter 09

Table 9.1 LODs and LOQs for seven saponins in Radix et Rhizoma Notoginseng by UV, ELSD, and CAD.

Table 9.2 Comparison of sensitivity of ELSD and CAD by QI, PA, ratio of Sc, and Se parameters.

Table 9.3 Regression data, LODs, and LOQs of seven ginsenosides for the three different detectors.

Table 9.4 Comparison of CAD and ELSD for the determination of astragalosides.

Chapter 10

Table 10.1 Physical characteristics vs. CAD response.

Chapter 11

Table 11.1 Parameters of the linear regression before and after log–log transformation of analyte concentration and peak area for both ELSD and CAD are shown.

Table 11.2 Correction factors (CFs) and accuracy expressed as % recovery of the spiked impurities.

Chapter 12

Table 12.1 Gradient program of the gentamicin method.

Table 12.2 Recovery of gentamicin C1a from triplicate preparations.

Table 12.3 Reproducibility and precision of gentamicin C1a and sisomicin.

Table 12.4 The relative retentions (RRs) of gentamicin and related substances under selected HPLC parameter robustness conditions.

Table 12.5 Gradient program of the netilmicin method.

Table 12.6 The resolution of netilmicin and related substances in the specificity mixture under selected HPLC parameter robustness conditions.

Chapter 13

Table 13.1 Observed

m

/

z

values and chromatographic (LC‐CAD) parameters for atracurium and its impurities.

Table 13.2 Linear and exponential fit equations for calibration curves for atracurium, cisatracurium, and laudanosine determined by LC‐CAD.

Table 13.3 Limits of detection (LOD) and limits of quantitation (LOQ) for atracurium, cisatracurium, and laudanosine determined by LC‐CAD.

Table 13.4 Summarized validation parameters and determination of content for pancuronium and its impurities in PANCURONIUM

injection

2 mg mL

−1

by LC‐CAD.

Table 13.5 Precision and accuracy for atracurium, cisatracurium, and laudanosine determined by LC‐CAD.

Table 13.6 Determination of active substances and impurities in pharmaceutical preparations by LC‐CAD.

Chapter 14

Table 14.1 Precision of injection.

Table 14.2 Assay accuracy and precision.

Table 14.3 Assay ruggedness.

Table 14.4 Assay ruggedness 2.

Table 14.5 Limit of detection/quantification.

Table 14.6 Analysis of routine sample.

Chapter 15

Table 15.1 Overview of used solvents/eluents for LC polymers applications.

Table 15.2 Peak areas of different polymer samples obtained with UV, CAD, and dRI detection.

Table 15.3 Average molecular weights of copolymers determined by CAD and dRI.

Table 15.4 Gradient conditions in GPEC mode.

List of Illustrations

Chapter 01

Figure 1.1 CAD involves pneumatic nebulization of liquid column eluent, aerosol conditioning within a spray chamber, solvent evaporation, diffusion charging of resultant aerosol residue within a mixing chamber using an opposing ion jet formed via corona discharge, removal of excess ions and high mobility charged particles in an ion trap, and measurement of aggregate charge of aerosol particles with a filter/electrometer.

Figure 1.2 (a) Cross‐flow nebulizer + impactor. (b) Concentric nebulizer and spray chamber.

Figure 1.3 Particle size distributions measured using an SMPS from the outlet of the evaporation tubes of the Corona Veo (left) and the Corona ultra RS (right) for continuous 1.0 mL/min flow of 1.0 µg/mL theophylline (THEO) in 20% v/v aqueous CH

3

OH.

Figure 1.4 Water droplet lifetimes as a function of droplet size for 0, 50, and 100% relative humidity at 293°K (20°C) [4].

Figure 1.5 Response curves obtained with different CAD instrument designs. Under the same chromatographic conditions (isocratic 20% CH

3

OH, 1.0 mL/min), the Corona ultra RS response for caffeine (more volatile) is lower and exhibits a higher power law exponent (Sections 1.4 and 1.5) than the less volatile theophylline. VCAD response curves are similar for both analytes even at higher Te (35°C) than UCAD (presumed < ~20°C due to cooling from nebulization). This is attributed to the larger overall particles measured with the VCAD design.

Figure 1.6 Particle size distributions measured using an SMPS from the outlet of the evaporation tubes of the Corona Veo for constant 0.6 mL/min flow of water (left) and CH

3

CN (right).

Figure 1.7 CAD is depicted for an isocratic separation where three discrete eluent volumes (baseline and two points within a single analyte solute band) are projected to the outlet of a column and traced through the main steps of the process. Particle cartoons are magnified to show impurity or analyte + impurity components. Shaded areas of each aerosol residue distribution reflect the relative proportion of

d

 < ~9 nm, which have a higher aerosol charging power law exponent, thereby influencing the overall power law exponent of CAD response.

Figure 1.8 Distribution of dry residue particle diameters for v/v concentration = 10

−6

. (a) Mobile phase = water; CMD = 3.6 µm; GSD = 1.85;

D

cut

 5.0 µm, (b) mobile phase = acetonitrile; CMD = 2.7 µm; GSD = 1.85;

D

cut

 = 11 µm.

Figure 1.9 Mean number of electronic charge units per particle versus particle diameter in nanometers.

Figure 1.10 Ion trap transmission for singly charged particles.

Figure 1.11 CAD detector signal as a function of concentration. Mobile phase: (a) water; (b) CH

3

CN.

Figure 1.12 Broadening of the signal peak. The column retention time is 2 min and the peak standard deviation

σ

is 2 s. The mobile phase is acetonitrile and the analyte has an assumed density of 1.4 g/cm

3

.

Figure 1.13 Peak area is shown in nanocoulombs for injected mass in the range of 0.5 ng to 200 µg. Mobile phase: acetonitrile; analyte bulk density: 1.4 g/cm

3

.

Figure 1.14 (a) ELSD (SEDEX 90LT) peak area versus

m

inj

theophylline—7.8–10,000 ng (left); 7.8–1,000 ng (right). Mobile phase 20% CH

3

OH; 1.0 mL/min; 20 × 4.0 mm, 3 µm C18 column. Signal units depend on attenuation setting and thus not specified. (b) VCAD peak area versus

m

inj

theophylline—0.2–10,000 ng (left); 0.2–1,000 ng (right). Conditions as in (a). Note: VCAD signal output is pA

1.5

(see

power function

discussed in Section 1.5.1).

Figure 1.15 Power law exponent

b

versus

m

inj

obtained under the same conditions (see Figure 1.14) with two CAD designs and ELSD. Note: Veo signal output is pA

1.5

(see

power function

discussed in Section 1.5.1).

Figure 1.16 Comparison of Corona ultra RS and Corona Veo near the limit of detection for theophylline (Theo) and caffeine (Caff);

m

inj

 = 0.5 ng each (left);

m

inj

 = 2.0 ng (right).

Figure 1.17 Log

10

(area) versus log

10

(

m

inj

) obtained under the same conditions (see Figure 1.14) with a Corona Veo (pA

1.5

) and with ELSD.

Figure 1.18 Power law exponent

b

versus

m

inj

for VCAD with different power function settings (pA

1.5

and pA

2.0

; Exponent of 2.0 = 1.5 × 1.33).

Figure 1.19 Ratio of peak full width at half maximum (FWHM) for “raw” current predicted by the CAD response model versus a “true” Gaussian concentration distribution over a range of

m

inj

and for different power functions.

Figure 1.20 Peak width (FWHM) obtained with a UV detector in series with CAD (power function of pA

1.5

) over a

m

inj

range of 0.5–10,000 ng theophylline showing gradual broadening of CAD peak area due to nonlinear response. Broadening of both signals at highest

m

inj

is attributed to column overload. Conditions as in Figure 1.14a.

Figure 1.21 Chromatograms for the same

m

inj

(5 ng) of chlorogenic acid (CA) and 4‐hydroxyphenylacetic acid (4HPAC) showing approximate mass flow versus time‐dependent response of CAD with lower noise and thus higher signal‐to‐noise ratio at the lower flow rate. Top trace: flow rate—0.8 mL/min, 3.0 mm i.d. × 50 mm L, 2.2 µm C18 column; bottom trace: flow rate—0.4 mL/min, 2.1 mm i.d. × 50 mm L, 2.2 µm C18 column.

Figure 1.22 Baseline current and noise as a function of the v/v concentration of theophylline (THEO) intentionally added to the mobile phase. Conditions as in Figure 1.14a.

Figure 1.23 Peak area response obtained with CAD (top) and CNLSD (Quant, NQAD; bottom) from flow injection analysis of 1.0 µg of each analyte dissolved in mobile phase (50% aqueous CH

3

OH, v/v). Flow rate = 1.0 mL/min. Evaporation temperature: CAD ambient; NQAD 35°C.

Chapter 02

Figure 2.1 Evolution of charged aerosol detectors.

Figure 2.2 Separation of simple carbohydrates by ion‐exchange chromatography (CarboPac PA20 Column—Thermo Scientific Dionex) using a sodium hydroxide gradient. Carbohydrates were measured by CAD following mobile phase desalting by a CMD 300 (Carbohydrate Membrane Desalter 300) with an RFC‐10 Reagent‐Free™ Controller (Thermo Scientific Dionex).

Figure 2.3 Separation of dahlia inulins by ion‐exchange chromatography (CarboPac PA100 Column—Thermo Scientific Dionex) using sodium acetate–sodium hydroxide gradient. Carbohydrates were measured by CAD following mobile phase desalting by a CMD 300 (Carbohydrate Membrane Desalter 300), with a RFC‐10 Reagent‐Free™ Controller (Thermo Scientific Dionex).

Figure 2.4 Analyte response factors change during conventional gradient elution due to altered nebulization efficiency. Inverse gradient compensation ensures that the mobile phase composition entering the nebulizer is consistent, so that analyte response factors are similar independent of elution time.

Chapter 03

Figure 3.1 CAD involves pneumatic nebulization of liquid column eluent, removal of larger droplets within a spray chamber, solvent evaporation, diffusion charging of aerosol residue within a mixing chamber by an opposing ion jet formed via corona discharge, removal of excess ions and high mobility charged particles in an ion trap, and measurement of aggregate charge of aerosol particles with a filter/electrometer.

Figure 3.2 Baseline noise observed with older CAD design (Corona® ultra, top trace) attributed to unstable nebulization at low liquid flow rates and/or solvents with low viscosity and surface tension. Bottom trace (Corona Veo). Conditions: 0.30 mL/min, isocratic 59 : 40 : 1, acetonitrile: ethyl acetate: 0.1 M ammonium acetate buffer, pH 5.5 for 1 min; then 27 min linear gradient to and 10 min hold at 19 : 80 : 1, acetonitrile: ethyl acetate: 0.1 M ammonium acetate buffer, pH 5.5.

Figure 3.3 Use of CAD with higher pH eluents. 40 ng erythromycin; polymer‐encapsulated C18 column, 5.0 µm, 4.6 × 150 mm; 30% 10 mM ammonium carbonate, pH 9.0; 70% acetonitrile. Isocratic 0.8 mL/min at 70% B; Corona Veo RS

T

e

 = 75°C.

Figure 3.4 Inverse gradient compensation configuration, which includes two identical columns in order to match the volumes of both flow paths.

Figure 3.5 Peak area response for low and high amounts of theophylline as a function of

T

e

.

Figure 3.6 Residual plots showing % amount deviation (error) from regression curve versus mass injected using either linear fit to log (area) versus log (mass) for data acquired with PF = 1.0 (left) or linear fit to area versus mass for data acquired with a PF = 1.4.

Chapter 04

Figure 4.1 Schematic diagram of the Corona CAD.

Figure 4.2 Response of 18 nonvolatile analytes of varied physicochemical properties on the Corona CAD. A mobile phase containing 50% acetonitrile and 50% formic acid (0.1% in Milli‐Q) was used, and analyte concentration was kept constant at 0.01 mg/mL (25 μL injection).

Figure 4.3 ELSD response when manipulating the (a) nebulizer temperature, (b) evaporator temperature, and (c) carrier gas flow‐rate settings available on the detector and keeping all other conditions constant.

Figure 4.4 Effect of mobile phase flow rate on the response of a Corona CAD.

Figure 4.5 The response for four nonvolatile analytes on the Corona CAD detector with changing eluent composition (increasing the percentage of acetonitrile).

Figure 4.6 The three‐dimensional relationship found for nonvolatile analytes on the Corona CAD relating the detector response, analyte concentration, and composition of the mobile phase.

Figure 4.7 A two‐dimensional separation showing approximately 150 peptide components obtained from a tryptic digest of reduced porcine thyroglobulin.

Figure 4.8 A 2D separation of human urine components using SCX–SAX columns in the first dimension and monolithic ODS columns in the second dimension. Both dimensions were gradient eluted.

Chapter 05

Figure 5.1 Solubility parameters of stationary phases, lipid solutes, and mobile phase solvents.

Figure 5.2 Snyder’s and Teas’ solvent selectivity triangles.

Figure 5.3 TAG retention on two complementary RP stationary phases.

Figure 5.4 Examples of calibration curves obtained with Corona CAD and ELSD.

Figure 5.5 Experimental correlation between log

A

and

b

.

Figure 5.6 TAG calibration curve (peak area vs. sample size) with Corona CAD (range 2.5–250 ppm).

Chapter 06

Figure 6.1 Column chemistry of Acclaim Trinity P1—a reversed‐phase/anion‐exchange/cation‐exchange trimodal mixed‐mode column based on Nanopolymer Silica Hybrid (NSH) technology. (a) Overview of a silica particle coated with nanopolymer beads; (b) enlarged view of the silica surface coated with negatively charged nanopolymer beads; (c) inner‐pore area consists of reversed‐phase and weak anion‐exchange functionalities and outer surface provides strong cation‐exchange interaction.

Figure 6.2 Separation of pharmaceutical counterions using a reversed‐phase/anion‐exchange/cation‐exchange trimodal mixed‐mode column. Column, Acclaim Trinity P1, 3.0 × 100‐mm format; mobile phase, acetonitrile/ammonium acetate buffer, pH 5 (20 mM total concentration) (60 : 40, v/v); flow rate, 0.5 mL/min; injection volume, 2 μL; temperature, 30°C; and detection, Corona ultra (gain = 100 pA; filter = med; nebulizer temperature = 30°C). Sample: 0.05–0.1 mg/mL. Peaks: (1) Choline or

N,N,N

‐trimethylethanolammonium; (2) tromethamine or tris(hydroxymethyl)aminomethane; (3) sodium; (4) potassium; (5) meglumine or

N

‐methyl glucamine; (6) mesylate or methanesulfonate; (7) nitrate; (8) chloride; (9) bromide; and (10) iodide.

Figure 6.3 Simultaneous separation of a hydrophobic acidic drug and its counterion—naproxen sodium salt.

Figure 6.4 Sensitivity comparison of CAD and ELSD for Na

+

and Cl

−

.

Figure 6.5 Chromatogram of the separation of 25 common pharmaceutical ions. Column, Acclaim Trinity P1, 3.0 × 50‐mm format; mobile phase, A, acetonitrile; B, 200 mM ammonium format (pH 4.0); C, DI water; gradient elution; flow rate, 0.5 mL/min; injection volume, 5 μL; temperature, 35°C; and detection, Corona CAD. Peaks: (1) lactate, (2) procaine, (3) choline, (4) tromethamine, (5) sodium, (6) potassium, (7) meglumine, (8) mesylate, (9) gluconate, (10) maleate, (11) nitrate, (12) chloride, (13) bromide,(14) besylate, (15) succinate, (16) tosylate, (17) phosphate, (18) malate, (19) zinc, (20) magnesium, (21) fumarate, (22) tartrate, (23) citrate, (24) calcium, and (25) sulfate.

Figure 6.6 Chromatograms of the separation of active pharmaceutical ingredients and their counterions. Column, Acclaim Trinity P1, 3.0 × 50‐mm format; mobile phase, A, acetonitrile; B, 200 mM ammonium format (pH 4.0); C, DI water; gradient elution; flow rate, 0.5 mL/min; injection volume, 5 μL; temperature, 35°C; and detection, Corona CAD. Samples: adenine hydrochloride (top), naproxen sodium (middle), and compound X fumarate salt (bottom).

Chapter 07

Figure 7.1 Sugar content of rice leaves over diurnal cycle. The top chromatogram corresponds to the time point on the left side of the graphs on the right. Sugars were separated on an AsahiPak NH2P‐50G 4E (6 × 250 mm column) at 35°C and 1.0 mL/min. The mobile phase was 85% acetonitrile. The horizontal bars at the top of the graphs on the right side of the figure indicate transitions between light (white) and darkness (black). The sugar content is expressed in milligrams per fresh weight grams of leaves. Each point and vertical bar indicates the average and standard deviation of three measurements.

Figure 7.2 Estimated by CAD purity of a glucosidase inhibitor (6‐

O

‐desulfated kotalanol). The separation used a Unison UK‐C18 (4 × 250 mm) with formic acid/water/acetonitrile mobile phase (0.1/100/0.2) flowing at 0.5 mL/min at a column temperature of 30°C. The inhibitor and the four impurity peaks were detected by CAD.

Figure 7.3 Chromatography of a cycloamylose sample. The sample was separated on a Cadenza C18 column (4.6 × 500 mm) with a gradient of 3.5–6% methanol over 900 min. The flow rate was 0.5 mL/min and the column temperature was 35°C. The cycloamyloses with DP from 21 to 80 were detected by CAD.

Figure 7.4 Chromatography of

O

‐linked oligosaccharides from bovine submaxillary mucin. The oligosaccharides were separated on a Hypercarb PGC column (4.6 × 150 mm) with a water/acetonitrile/trifluoroacetic acid mobile phase with 90% of the flow going to the CAD and 10% going to a mass spectrometer for oligosaccharide identification. Identified structures are shown on the figure. Peaks marked with * depict proposed structures based on online accurate mass determination and literature [36].

Chapter 08

Figure 8.1 Chromatograms of the same lot of PEG 32 kDa reagent by three universal detectors: (a) RI, (b) ELSD, (c) CAD.

Figure 8.2 Changes in CAD and ELSD peak area as a function of the degree of polymerization

n

for the equimass mixture of uniform PEGs (

n

 = 6, 8, 10, 12, 18, 21, 25, 30, and 42) at the concentration of 10 mg/mL (), 1 mg/mL (), and 0.4 mg/mL () for CAD and 10 mg/mL (), 1 mg/mL (), and 0.4 mg/mL () for ELSD.

Figure 8.3 Comparison between the mass fractions of CRM PEG 1000 detected by CAD and ELSD.

Figure 8.4 Chromatograms of stressed protein solution containing 1 mg/mL protein and 40 µg/mL Polysorbate 80. Chromatograms: protein solution (A), protein solution containing deamidated protein (B), protein solution containing reduced form (C), and protein solution containing two major oxidized protein form (D). Peaks: 1, 2: major peaks of Polysorbate 80 origin; 3, 4: oxidized protein; 5: protein (native); and 6: reduced protein form. Chromatographic conditions: Poroshell 300 SB‐C18 column packed with 5 µm shell particles (75 mm × 2.1 mm), mobile phase: acetonitrile–methanol–water–trifluoroacetic acid gradient elution (50–100% B, in 6 min), flow: 0.65/min, column temperature: 20°C, injection volume: 5 μL, sample temperature 4°C, detection: charged aerosol detection, range: 50 pA, nebulizing temperature: 30°C, gas (nitrogen) pressure: 37–39 psi.

Chapter 09

Figure 9.1 Typical HPLC chromatograms of mixed standards with CAD (a) and methanol extracts of Radix et Rhizoma Notoginseng with the detectors of CAD (b), UV (c), and ELSD (d). Compounds 1–7 are notoginsenoside R

1

; ginsenosides Rg

1

, Re, Rb

1

, Rg

2

, Rh

1

, and Rd, respectively.

Figure 9.2 CAD, UV, and ELSD response of seven investigated saponins (10 μL of 0.048 g/mL sample solution was injected).

Figure 9.3 Representative LC chromatograms of

P. ginseng

. (a) Representative LC chromatograms of the mixed standards solutions of seven compounds of CAD signal, (b) corresponding ELSD signal, (c) corresponding UV signal, (d) representative LC chromatograms of the samples of

P. ginseng

of CAD signal. Peaks: 1, Ginsenoside Rg

1

; 2, Ginsenoside Re; 3, Ginsenoside Rb

1

; 4, Ginsenoside Rc; 5, Ginsenoside Rb

2

; 6, Ginsenoside Rb

3

; 7, Ginsenoside.

Chapter 10

Figure 10.1 HILIC–CAD analysis of API and counterion in a single run. (a) Penicillin G and potassium. Conditions: HPLC: Thermo Scientific™ Dionex™ UltiMate™ 3000 RSLC. Column: Thermo Scientific™ Acclaim™ Trinity P2, 3 µm, 3 × 50 mm. Col. Temp: 30°C; Flow Rate: 0.5 mL/min. Inj. Volume: 1 μL. Eluent A: Acetonitrile, Eluent B: Water, Eluent C: 100 mM ammonium formate, pH 3.65. Isocratic: 25 : 50 : 25 (v/v) A : B : C. Detector: Thermo Scientific™ Dionex™ Corona™ Veo™ RS; Sample: Potassium Penicillin G, 100 ng/μL in DI water. (b) Metformin and chloride. HPLC: Thermo Scientific™ Dionex™ UltiMate 3000 RSLC. Column: Thermo Scientific™ Acclaim™ Trinity P2, 3 µm, 3 × 50 mm. Col. Temp: 30°C. Flow Rate: 0.5 mL/min. Inj. Volume: 1 μL. Eluent A: Acetonitrile, Eluent B: 100 mM ammonium formate, pH 3.65. Isocratic: 20 : 80 (v/v) A : B. Detector: Thermo Scientific™ Dionex™ Corona™ Veo™ RS, Evap. Temp: 55°C. Sample: Metformin hydrogen chloride, 100 ng/μL in DI water.

Figure 10.2 API, counterions, and trace impurities in a single analysis. Figure shows the measurement of chloride impurity at 0–0.3% of API. Conditions: HPLC: Thermo Scientific™ Dionex™ UltiMate™ 3000 RSLC. Column: Thermo Scientific™ Acclaim™ Trinity P1, 3 µm, 3 × 50 mm. Col. Temp: 30°C; Flow Rate: 0.8 mL/min. Inj. Volume: 5 μL. Eluent A: Acetonitrile, Eluent B: 200 mM Ammonium acetate pH 4.00. Isocratic: 75 : 25 (v/v) A : B. Detector: Thermo Scientific™ Dionex™ Corona™ Veo™ RS, Evap. Temp: 60°C. Sample: Diclofenac sodium 1 mg/mL in DI water.

Figure 10.3 Simultaneous analysis of anions and cations using HILIC–CAD. Conditions: A Sequant ZIC®‐pHILIC 5 mm, 4.6 × 150 mm column (The Nest Group, Southborough, MA) operated at 30°C was used. Gradient conditions: 20–70% B over 26 min; Mobile Phase A: 15% 100 mM Ammonium Acetate pH 4.68, 5% Methanol, 20% IPA, 60% Acetonitrile; mobile phase B: 50% 30 mM Ammonium Acetate pH 4.68, 5% Methanol, 20% IPA, 25% Acetonitrile, at a flow rate of 0.5 mL/min and a 10 μL injection.

Figure 10.4 Simultaneous analysis of 25 pharmaceutical counterions using HILIC–CAD. HPLC: Thermo Scientific™ Dionex™ UltiMate™ 3000 RSLC. Column: Thermo Scientific™ Acclaim™ Trinity P1, 3 µm, 3 × 50 mm. Col. Temp: 35°C; Flow Rate: 0.5 mL/min. Inj. Volume: 5 μL. Eluent A: water, Eluent B: Acetonitrile, Eluent C: 200 mM Ammonium formate pH 4.00. Gradient—see Figure. Detector: Thermo Scientific™ Dionex™ Corona™ Veo™ RS, Evap. Temp: 50°C. 1, Lactate; 2, procaine; 3, choline; 4, tromethamine; 5, sodium; 6, potassium; 7, meglumine; 8, mesylate; 9, glucoronate; 10, maleate; 11, nitrate; 12, chloride; 13, bromide; 14, besylate; 15, succinate; 16, tosylate; 17, phosphate; 18, malate; 19, zinc; 20, magnesium; 21, fumarate; 22, tartrate; 23, citrate; 24, calcium; 25, sulfate.

Figure 10.5 CAD–HILIC analyses of organic acids conditions: Column: SeQuant ZIC®‐pHILIC, 5 m, 4.6 × 150 mm (The Nest Group, Southborough, MA) operated at 30°C was used with an isocratic mobile phase of 70 : 30 ACN/200 mM ammonium acetate, pH 6.7 operated at a flow rate of 1.0 mL/min and a 10‐μL injection.

Figure 10.6 Analysis of fermentation broth sugars by HPLC/CAD. Conditions: A Shodex Asahipak NH2P‐50 4.6 × 250 mm 5 µm column operated at 35°C was used with an isocratic mobile phase of 25/75 water/ACN and a flow Rate: 1.0 mL/min. Samples are 10 μL injections of 10 µg/mL each in 30/70 water/ACN.

Figure 10.7 Analysis of adrenocorticotropic hormone fragment 4–10 peptide standard by UV/PDA and CAD. Conditions: A C

18

4.6 × 250 mm 5 µm column was used at ambient temperature with a mobile phase of 0.1% TFA in water (A) and ACN (B). The gradient consisted of a 5‐min hold at 100% A, then a linear gradient from 0 to 40% B over 20 min at a flow rate of 0.6 mL/min; 10 μL of a 1 mg/mL sample concentration in mobile phase A was injected.

Figure 10.8 Analysis of free, underivatized amino acids that compose the adrenocorticotropic hormone fragment 4–10 peptide by HPLC/UV/PDA detection and CAD (see Figure 10.7 for conditions).

Chapter 11

Figure 11.1 Structural formulae of topiramate and its impurities derived from the synthetic route [16]. Impurity A is commercially available and can thus be either starting material or intermediate.

Figure 11.2 Example chromatogram of the test solution of topiramate (

c

 = 5 mg/mL) spiked with each impurity at concentrations of 0.1%. Chromatographic conditions are described in Section 11.3.2. Detection: Corona ultra RS (nebulizer temperature: 35°C). Impurity A was not detectable at this concentration. The retention time derived from the experiments for sensitivity enhancement with the Corona ultra RS, cf. Figure 11.3.

Figure 11.3 Influence of post‐column addition of acetonitrile on the response of impurity A (

c

 = 2.6 mg/mL). Detection: Corona ultra RS (nebulizer temperature: 35°C). The chromatographic conditions are described in Section 11.3.2.

Figure 11.4 Three consecutive injections of a solution of topiramate (

c

 = 20 mg/mL) from the same vial are shown using the ELSD. The content of impurity C, as the only impurity found in this batch, was below the LOQ. All other peaks are nonreproducible ghost peaks. The chromatographic conditions are described in Section 11.3.2.

Chapter 12

Figure 12.1 Chemical structures and selected properties of gentamicin C1, C1a, C2, C2a, and C2b, deoxystreptamine, garamine, and sisomicin.

Figure 12.2 Representative chromatograms of (A) gentamicin reference standard and (B) standard mixture containing gentamicin, deoxystreptamine, garamine, and sisomicin.

Figure 12.3 Representative chromatograms of (A) diluent, (B) pharmaceutical cream placebo, and (C) pharmaceutical cream sample.

Figure 12.4 Compound name, structure, identity, and properties of netilmicin and its related substances.

Figure 12.5 Representative chromatograms of (A) diluent, (B) deoxystreptamine, (C) netilmicin, (D)

N

‐ethyl garamine, and (E) sisomicin.

Figure 12.6 Representative chromatograms of stress study of netilmicin (a) netilmicin, (b) netilmicin heat stress, and (c) netilmicin acid stress (HCl).

Figure 12.7 Representative chromatogram of a specificity mixture containing the ethyl derivatives of sisomicin (reaction mixture of sisomicin reductive alkylation) and

N

‐ethyl garamine sulfate.

Chapter 13

Figure 13.1 Degradation pathways of atracurium.

Figure 13.2 Degradation pathways of mivacurium.

Figure 13.3 Mass chromatograms (ESI in positive mode) recorded for peak identification from the solutions containing 10 µg mL

−1

of cisatracurium (a) and 10 µg mL

−1

of atracurium (b); injection volume 1 μL.

Figure 13.4 Mass chromatogram (ESI in positive mode) recorded for peak identification from the solutions containing 10 µg mL

−1

of Mivacron; injection volume 1 μL.

Figure 13.5 LC‐CAD chromatogram recorded from the solutions containing 100 µg mL

−1

pancuronium and 3 µg mL

−1

of dacuronium and vecuronium in 0.1% TFA (a) and from the solution containing 800 µg mL

−1

pancuronium from pharmaceutical preparation in 0.1% TFA (b).

Figure 13.6 LC‐CAD chromatograms recorded from the solution containing 100 µg mL

−1

of: cisatracurium (1), atracurium (2), SZ1677 (3), vecuronium (4), pipecuronium (5), pancuronium (6), and rocuronium (7) and the mixture of seven muscle relaxants (8) in 01% TFA.

Figure 13.7 LC‐CAD chromatograms recorded from pharmaceutical preparations containing ~100 µg mL

−1

of atracurium from Tracrium (a), cisatracurium from Nimbex (b), and mivacurium from Mivacron (c) in 0.1% FA (black) and in 20% methanol (grey); injection volume 10 μL.

Chapter 14

Figure 14.1 Example scale inhibitor return curve.

Figure 14.2 Polyphosphino polycarboxylic acid (PPCA).

Figure 14.3 Polyvinyl sulfonate (PVS).

Figure 14.4 Sulphonated polyacrylic acid copolymer (VS‐Co).

Figure 14.5 Polyacrylic acid (PAA).

Figure 14.6 Typical Hyamine 1622 calibration curve.

Figure 14.7 Typical mass spectrum of a polymeric scale inhibitor.

Figure 14.8 Typical low level linearity curve.

Figure 14.9 Typical high level linearity curve.

Figure 14.10 Showing the polymeric scale inhibitor peak at approximately 9 min.

Chapter 15

Figure 15.1 Isocratic retention modes. The retention behavior of three different polystyrene SEC standards (

M

A

 > 

M

B

 > 

M

C

) at three different conditions on analytical silica C18 column is shown: (left) eluent tetrahydrofuran, (middle) tetrahydrofuran/water 87 : 13 (at 30°C), and (right) eluent tetrahydrofuran/water 75 : 25 [11].

Figure 15.2 Comparison of CAD, dRI, and UV signals of homopolymers, a pS (white) standard (

M

w

 ~ 5 k/mol) and a pMMA (grey) standard (

M

w

 ~ 5 k/mol) at similar concentration.

Figure 15.3 Chromatograms of copolymers using CAD, UV, and dRI. The UV and dRI clearly show dependency on sample composition.

Figure 15.4 Concentration dependency of different copolymers. Different concentrations are used 0.01, 0.1, 1.0, and 10 mg/mL. Samples are analyzed using CAD, UV, and dRI detectors.

Figure 15.5 Overlay of chromatograms (left) and molar mass distributions (MMD) (right) of the pS‐co‐pMMA 50 : 50 copolymer for the different detectors.

Figure 15.6 Overlay of chromatograms of various copolymers and homopolymers of styrene and MMA obtained under gradient conditions shown in Table 15.4.

Figure 15.7 Overlay of CAD‐GPEC chromatograms of the copolymers and corresponding homopolymers. The copolymers clearly show a broader distribution, resulting in a larger peak area due to the nonlinear concentration dependency of CAD.

Figure 15.8 Concentration dependency on peak area of homopolymers and copolymers of the UV (254 nm) and CAD.

Figure 15.9 Blueprint of LC‐MS system. The system consists of an ESI‐TOF‐MS combined with two gradient pumps, two isocratic pumps, one single column oven, a PDA, and a CAD.

Figure 15.10 Overlay of CAD, UV (220 and 254 nm), and MS signals of a multifunctional acrylate obtained with SEC.

Figure 15.11 Overlay of gradient LC chromatograms of an intermediate product of TIC MS and CAD. Gradient as described in Table 15.4 is used.

Figure 15.12 SEC‐MS‐UV‐CAD chromatograms of oligomeric resin.

Guide

Cover

Table of Contents

Begin Reading

Pages

iii

iv

v

xvii

xviii

xix

xx

xxi

xxii

xxiii

xxv

1

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

67

68

69

70

71

72

73

75

76

77

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

221

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

327

328

329

330

331

332

333

334

335

336

337

338

339

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

501

502

503

504

505

506

507

508

509

510

Charged Aerosol Detection for Liquid Chromatography and Related Separation Techniques

Edited by Paul H. Gamache

Thermo Fisher Scientific, Chelmsford, MA, USA

This edition first published 2017© 2017 John Wiley & Sons, Inc

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Paul H. Gamache to be identified as the editor of the editorial material in this work has been asserted in accordance with law.

Registered OfficeJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

Editorial Office111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of WarrantyIn view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data

Names: Gamache, Paul H., 1957‐Title: Charged aerosol detection for liquid chromatography and related separation techniques / edited by Paul H. Gamache, Thermo Fisher Scientific, USA.Description: First edition. | Hoboken, NJ : John Wiley & Sons, Inc., 2017. | Includes bibliographical references and index.Identifiers: LCCN 2017007285 (print) | LCCN 2017008536 (ebook) | ISBN 9780470937785 (cloth) | ISBN 9781119390695 (pdf) | ISBN 9781119390701 (epub)Subjects: LCSH: Liquid chromatography. | Atmospheric aerosols. | Aerosols. | Electrostatic precipitation. | Separation (Technology)Classification: LCC QD79.C454 C43 2017 (print) | LCC QD79.C454 (ebook) | DDC 543/.84–dc23LC record available at https://lccn.loc.gov/2017007285

Cover design by Wiley

I would like to dedicate this book to my family. To Dad whose courage, integrity, and selflessness makes him a perfect example of America’s greatest generation. To Mom for her undying devotion to her family and her great cooking. To my brother, Dan, and his family for their love and support. My deepest gratitude goes to my wife, Anne, and children, Chelsea and Michael. I am so proud to see the accomplishments and growth of both Chelsea and Michael. Finally, to Anne, the love of my life and one of the most genuine and unselfish people that I have ever met.

List of Contributors

Ian N. AcworthThermo Fisher ScientificChelmsford, MAUSA

Stefan AlmelingEuropean Directorate for the Quality of Medicines & HealthCare (EDQM)StrasbourgFrance

Amber AwadDominion DiagnosticsNorth Kingstown, RIUSA

Bruce BaileyThermo Fisher ScientificChelmsford, MAUSA

Agata BlazewiczPharmaceutical Chemistry DepartmentNational Medicines InstituteWarsawPoland

Ton BrooijmansDSM Coating ResinsWaalwijkThe Netherlands

Sophie BrossardEuropean Directorate for the Quality of Medicines & HealthCare (EDQM)StrasbourgFrance

Pierre ChaminadeLip(Sys)2 Lipids, Analytical and Biological Systems, Chimie Analytique PharmaceutiqueUniversité Paris‐Sud, Université Paris‐SaclayChâtenay‐MalabryFrance

Hitesh P. ChokshiRoche Innovation CenterNew York, NYUSA

Paul CoolsDSM Coating ResinsWaalwijkThe NetherlandsCurrent address: The Dow Chemical CompanyFreeport, TXUSA

Chris CraftsThermo Fisher ScientificChelmsford, MAUSA

Greg W. DicinoskiAustralian Centre for Research on Separation Science (ACROSS), School of Chemistry, Faculty of Science, Engineering and TechnologyUniversity of TasmaniaHobartTasmania, Australia

Mark EmanueleWestford, MAUSA

Zbigniew FijalekPharmaceutical Chemistry DepartmentNational Medicines InstituteandWarsaw Medical UniversityWarsawPoland

Paul H. GamacheThermo Fisher ScientificChelmsford, MAUSA

Paul R. HaddadAustralian Centre for Research on Separation Science (ACROSS), School of Chemistry, Faculty of Science, Engineering and TechnologyUniversity of TasmaniaHobartTasmania, Australia

Sylvie HéronLip(Sys)2 Lipids, Analytical and Biological Systems, LETIAMUniversité Paris‐Sud, Université Paris‐Saclay, IUT d’OrsayOrsayFrance

Ulrike HolzgrabeInstitute of Pharmacy and Food ChemistryUniversity of WuerzburgWuerzburgGermany

Joseph P. HutchinsonAustralian Centre for Research on Separation Science (ACROSS), School of Chemistry, Faculty of Science, Engineering and TechnologyUniversity of TasmaniaHobartTasmania, Australia

David IlkoInstitute of Pharmacy and Food ChemistryUniversity of WuerzburgWuerzburgGermany

Yong JiangState Key Laboratory of Natural and Biomimetic DrugsPeking UniversityBeijingChina

Arul JosephGilead Sciences, Inc.Foster City, CAUSA

Stanley L. KaufmanRetired from TSI Inc.Shoreview, MNUSA

Shinichi KitamuraGraduate School of Life and Environmental SciencesOsaka Prefecture UniversityOsakaJapan

William KopaciewiczThermo Fisher ScientificChelmsford, MAUSA

Dawen KouGenentech Inc.South San Francisco, CAUSA

Lijuan LiangBeijing Friendship HospitalCapital Medical UniversityandState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina

Danielle LibongLip(Sys)2 Lipids, Analytical and Biological Systems, Chimie Analytique PharmaceutiqueUniversité Paris‐Sud, Université Paris‐SaclayChâtenay‐MalabryFrance

Xiaodong LiuThermo Fisher ScientificSunnyvale, CAUSA

Gerald ManiusRetired from Hoffmann‐La Roche Inc.Nutley, NJUSA

Robert C. NeugebauerEuropean Directorate for the Quality of Medicines & HealthCare (EDQM)StrasbourgFrance

Marc PlanteThermo Fisher ScientificChelmsford, MAUSA

Christopher A. PohlThermo Fisher ScientificSunnyvale, CAUSA

Magdalena PoplawskaWarsaw Medical UniversityWarsawPoland

Jeffrey S. RohrerThermo Fisher ScientificSunnyvale, CAUSA

Abu RustumMerck & Co Inc.Summit, NJUSA

Katarzyna SarnaPharmaceutical Chemistry DepartmentNational Medicines InstituteWarsawPoland

Michael SwartzAnalytical DevelopmentValidation ScienceUxbridge, MAUSA

Alain TchaplaLip(Sys)2 Lipids, Analytical and Biological Systems, LETIAMUniversité Paris‐Sud, Université Paris‐Saclay, IUT d’OrsayOrsayFrance

David ThomasThermo Fisher ScientificChelmsford, MAUSA

Alan K. ThompsonNalco Champion, an Ecolab CompanyAberdeenUK

Hung TianNovartisEast Hanover, NJUSA

Pengfei TuState Key Laboratory of Natural and Biomimetic DrugsPeking UniversityBeijingChina

Michael TürckMerck KGaADarmstadtGermany

Malgorzata Warowna‐GrzeskiewiczPharmaceutical Chemistry DepartmentNational Medicines InstituteandWarsaw Medical UniversityWarsawPoland

Ke ZhangGenentech Inc.South San Francisco, CAUSA

Preface

It has been approximately 12 years since charged aerosol detection (CAD) first became widely available for use with liquid‐phase separations. CAD is perhaps best described as a universal detection technique for quantitative analysis. In addition to characteristics such as dynamic range, ease of use, reproducibility, and sensitivity, such techniques are often chosen for the broadness of their detection scope and their ability to provide analyte‐independent sensitivity (i.e., signal/amount). Since its introduction, CAD has been widely adopted as evidenced by over 250 peer‐reviewed articles that encompass several fields of application. These descriptions and the pioneering work of Dr. Stan Kaufman, Dr. Roy Dixon, and others have led to an increased understanding of the theory and practice of CAD. The capabilities of CAD technology have also continued to evolve along with those of relevant separation technologies. In many respects, CAD is still a new technique and its fundamental performance characteristics are not fully understood. The primary objectives of this book are therefore to further elucidate CAD theory, to objectively describe its advantages and limitations, and to provide detailed recommendations for its practical use.

This is the first book devoted to the topic of CAD and is intended to be a primary resource for analytical chemists in a variety of disciplines. The book was developed in collaboration with many scientists with expertise in research and/or application of separation science, detection, and aerosol measurement. CAD can be coupled with a range of separation techniques in numerous configurations where many variables can influence its performance. The practical value of CAD also depends on its intended use and the alternative tools available for a given application. The key aspects of this book are therefore to relate fundamental properties and operational variables of CAD to its performance in a variety of conditions and to provide expert insight and different perspectives on use of CAD to address specific analytical problems.

This book is arranged into three main sections each having several chapters. Each chapter was written by a different contributor or group and begins with a summary and/or table of contents to provide a quick view of the information provided.

Section 1 includes a theoretical description (Chapter 1), a review of current literature (Chapter 2), a guidance for practical use (Chapter 3), and an overview of universal detection in the context of comprehensive multicomponent analysis. Chapter 1 provides significant new insight to CAD theory making use of aerosol size distribution measurements to develop a detailed semiempirical model that describes its response. This chapter mainly focuses on newer CAD instrument designs, the evolution of which is further described in Chapter 2. Chapter 3 is complementary to Chapter 1 and provides detailed practical recommendations, which include method transfer approaches from older to newer instrument models. This chapter also provides basic requirements for configuring multi‐detector systems often used to extend both the quantitative and qualitative information obtained from each sample. The role of universal detectors in multi‐detector configurations together with emerging trends in separation science is then further elucidated in Chapter 4. This includes discussion of capillary and ultrahigh pressure liquid chromatography, supercritical and subcritical fluid chromatography, and multidimensional separations to address more complex analyses and to minimize the environmental impact of laboratory testing. Many of these techniques are further exemplified throughout the later sections of the book.

Section 2 consists of separate chapters each focusing on specific analyte classes: lipids (Chapter 5), ions (Chapter 6), carbohydrates (Chapter 7), polymers and surfactants (Chapter 8), and diverse, putatively bioactive species in herbal medicines (Chapter 9). For each analyte class, an informative review is provided on theory and experimental approaches to both separate and quantify species present in different sample matrices and in different areas of application (e.g., biosciences, chemical, food and beverage, natural products, and pharmaceutical). Examples are used to illustrate the advantages and limitations of CAD compared with alternative techniques. Taken together these chapters include descriptions of the use of CAD with many different separation techniques including those discussed in Chapter 4 along with normal‐phase liquid chromatography (NPLC), size exclusion chromatography (SEC), hydrophilic interaction chromatography (HILIC), and mixed‐mode techniques that, for example, combine ion exchange with either reversed‐phase or HILIC retention mechanisms.

Section 3 describes the use of CAD to address specific analytical problems. Since CAD is widely used within pharmaceutical and biopharmaceutical industries, this section includes an overview of its use during various stages of drug development and informative guidance on method development, validation, and transfer in regulated environments (Chapter 10). This is followed by examples describing development and validation of pharmaceutical methods for analyses involving aminoglycoside antibiotics (Chapter 12), quaternary ammonium muscle relaxants (Chapter 13), and the anticonvulsant topiramate (Chapter 11). Each of these applications pose significant challenges in terms of both separation and detection, and their descriptions provide valuable insight to routine use of CAD in a regulated environment. The final two chapters discuss very interesting fields of application outside of the pharmaceutical industry. Chapter 14 describes analysis of scale‐inhibiting polymers in oil‐field chemistry applications and includes thorough validation of a gel permeation chromatography (GPC) method with CAD for routine analysis of residual levels in oilfield brine samples. Lastly, Chapter 15 describes various approaches to characterize industrial synthetic polymers ranging from smaller oligomers to larger and more complex formulations. In addition to SEC, this chapter includes discussion of liquid chromatography at critical conditions (LCCC) and gradient polymer elution chromatography (GPEC) to analyze complex polymer compositions. This includes examples of the combined use of CAD with complementary devices to address the very difficult task of characterizing raw material, intermediate compositions, and end products. Chapters 14 and 15 along with Chapter 8, which describes analysis of polymers and surfactants primarily as pharmaceutical excipients and reagents, are particularly recommended to readers interested in use of CAD in polymer analysis.

Throughout development of this book, significant advances have continued to be made in both CAD and relevant separation technologies. In addition to the chapters within Sections 2 and 3, it is recommended that the reader also refer to Chapters 1–3 for updated information on a given topic. Chapter 2 provides a comprehensive literature review, which includes more recent examples of the applications of CAD, while Chapters 1 and 3 describe specific technology advancements and their implications for practical use. It is sincerely hoped that this book will provide a valuable resource to all workers in the field of liquid‐phase separations and will help to stimulate further research in all aspects of universal detection.

Acknowledgment

This book would not have been possible without the significant efforts and expertise of more than 45 contributors. I am very grateful to Stan Kaufman for the many fruitful discussions and for his brilliant input and time devoted to Chapter 1. The early and continued success of CAD is largely due to the devotion and talents of my colleagues from ESA Inc., several of whom are contributors to the book and many others who have greatly supported this effort. I would especially like to acknowledge and thank Ian Acworth, Bob Kwiatkowski, Ryan McCarthy, Nick Santiago, and John Waraska for their essential contributions.

Section 1Fundamentals of Charged Aerosol Detection

1Principles of Charged Aerosol Detection

Paul H. Gamache1 and Stanley L. Kaufman2

1 Thermo Fisher Scientific, Chelmsford, MA, USA

2 Retired from TSI Inc., Shoreview, MN, USA

CHAPTER MENU

1.1 Summary

1.2 History and Introduction to the Technology

1.3 Charged Aerosol Detection Process

1.4 CAD Response Model

1.5 Performance Characteristics

References

1.1 Summary

This chapter provides a brief history and detailed overview of charged aerosol detection (CAD) and a semiempirical model describing its response and expected performance under various analytical conditions. CAD and other evaporative aerosol detectors involve the same successive steps of primary spray droplet formation from an eluent stream, conditioning by inertial impaction to remove droplets too large to evaporate during passage through the instrument, and evaporation of remaining droplets to form residue particles each comprised of nonvolatile background impurities and any nonvolatile analyte present. Detection of the residue particles produces the detector signal. In CAD, the aerosol is given a charge dependent on the particle size, and the total charge carried by the aerosol is measured as a current; in ELSD, the aerosol is detected by its light scattering properties. Both detection methods produce a response that is approximately mass‐flow dependent. The analyte dry bulk density affects the residue particle size for a given eluting mass, which has a minor effect on the mass sensitivity of both detector types. Other analyte properties in particular optical properties (e.g., refractive index (RI)) for the ELSD likewise affect the sensitivity. Detection selectivity for evaporative aerosol detectors is based on differences in vaporization of components within an eluent. Accordingly, these techniques are expected to have very similar detection scope, eluent requirements, and solvent dependency of response. The unique characteristics of CAD are due to the aerosol measurement technique, which includes diffusion charging of residue particles and detection of the current due to deposition of particles with their charge in an aerosol‐electrometer filter. Aerosol charging by diffusion mechanisms is well known to have only a minor dependence on particle material (i.e., analyte properties), which is the basis for uniform response capabilities of CAD. Like ELSD, CAD response (e.g., peak area vs. mass injected (minj)) can be described by a power law function with a variable exponent b. Linear response, never perfectly achieved by either methods, would correspond to b = 1. For both techniques, the exponent b is at its maximum at the lowest minj and decreases with increasing minj. This is attributed to smaller residue particles that have a higher power law exponent (β1) of response and are more prevalent with low minj and the low concentration that occurs near the edges of any peak. For ELSD this corresponds to Rayleigh light scattering for particle diameters (d) typically < 50 nm where β1 = 6, while for CAD corresponds to aerosol charging of d < ~9 nm where β1 ~ 2.25. For CAD, the lower d transition and smaller β1 (closer to 1) underlies the widely observed lower detection limits, wider dynamic range, and less complex response curve than ELSD. Newer CAD designs produce an even smaller relative proportion of residue particles of d < ~9 nm, thus further simplifying the response curve, enabling lower sensitivity limits and a wider quasi‐linear response range.

1.2 History and Introduction to the Technology

The technique that is now most commonly called charged aerosol detection (CAD) was first described in 2001 by Kaufman at TSI Inc. in a provisional patent application that ultimately led to US patent 6,568,24 [1]. This device was termed an evaporative electrical detector (EED) and was based on coupling liquid chromatography (LC) and other separation techniques with TSI’s well‐established electrical aerosol measurement (EAM) technology [2]. Around the same time, Dixon and Peterson at California State University were pursuing a similar avenue of innovation with a laboratory‐built device that coupled LC with an earlier generation of TSI’s EAM instruments. Dixon and Peterson described their device, termed aerosol charge detector (ACD), in the Journal of Analytical Chemistry in 2002 [3]. In both instances, the primary objective was to exploit the advantages, well described in aerosol science literature [4], of EAM over direct light scattering for measuring the very small (i.e., low nm diameter range) particles typically produced by LC detectors. The approach was therefore mainly geared toward addressing some of the limitations of evaporative light scattering detection (ELSD), which at the time had been used for LC detection for about 20 years. Subsequent collaboration between TSI and ESA Biosciences, Inc. led to the introduction of the first commercial instrument, the Corona® CAD®, in 2005 [5]. While there are some differences among these early EAM‐based LC devices and with newer commercial instruments, the basic detection process remains the same. Therefore, Kaufman’s patent disclosure and Dixon’s article are acknowledged as the primary theoretical descriptions of CAD.

Since its commercial introduction in 2005, CAD has been widely adopted for a broad range of chromatographic applications. CAD and other aerosol techniques, including ELSD and condensation nucleation light scattering detection (CNLSD) [6], are described as “universal” since response depends primarily on aerosol particle size and number concentration (e.g., number of particles/cubic centimeter of gas) rather than individual analyte properties. These “common property” measurement characteristics provide significant advantages over other devices whose detection scope (viz., range of chemicals for which a useful response can be obtained) and sensitivity (viz