Exploration of High Temperature Ethanol/Water Mobile Phases As A

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2016 Exploration of High Temperature Ethanol/ Water Mobile Phases as a Green Alternative to Traditional Reversed Phase High Performance Liquid Chromatography Phillip Bradford Ogden

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COLLEGE OF ARTS AND SCIENCES

EXPLORATION OF HIGH TEMPERATURE ETHANOL/WATER MOBILE PHASES AS A

GREEN ALTERNATIVE TO TRADITIONAL REVERSED PHASE HIGH PERFORMANCE

LIQUID CHROMATOGRAPHY

PHILLIP BRADFORD OGDEN

A Dissertation submitted to the Department of Chemistry and Biochemistry in partial fulﬁllment of the requirements for the degree of Doctor of Philosophy

2016

John G. Dorsey Professor Directing Dissertation

Shridhar K. Sathe University Representative

Michael Roper Committee Member

Albert E. Stiegman Committee Member

The Graduate School has veriﬁed and approved the above-named committee members, and certiﬁes that the dissertation has been approved in accordance with university requirements.

ii This work is dedicated to my family. To my brother Christopher Ogden, whose guidance and patience has been a bedrock for me to grow and succeed in all of my endeavors. And to my parents, Cindy and Carroll Ogden whose wisdom and advice has steered me around unforeseen obstacles in my path. Finally, this work is dedicated to my brother Paul-Michael Ogden watching from above. May Paul-Michael rest in peace for he is most certainly missed in this life.

iii TABLE OF CONTENTS

ListofTables...... vi ListofFigures ...... viii List of Abbreviations ...... xi Abstract...... xii

1 INTRODUCTION 1 1.1 Reversed Phase High Performance Liquid Chromatography ...... 3 1.1.1 BasicConcepts...... 3 1.1.2 EﬃciencyandthevanDeemterCurve ...... 4 1.1.3 Environmental Implications ...... 7 1.2 High Temperature Chromatography ...... 9 1.2.1 BasicConcepts...... 9 1.2.2 Subcritical Water Chromatography ...... 13 1.2.3 DrawbackstoSCWC...... 15 1.3 Ethanol as a Reversed Phase Organic Modiﬁer ...... 19 1.4 ResearchGoals ...... 25

2 SOLVENT STRENGTH COMPARISON 26 2.1 Introduction...... 26 2.1.1 Solvent Strength Parameter ...... 26 2.1.2 CurrentWork...... 28 2.2 Experimental ...... 29 2.2.1 Reagents ...... 29 2.2.2 Equipment ...... 29 2.2.3 Procedures ...... 30 2.3 ResultsandDiscussion...... 30 2.3.1 Solvent Strength Comparison ...... 30 2.3.2 Temperature Dependence of the S Parameter ...... 36 2.4 Conclusions...... 40

3 CHARACTERIZATION OF THE RETENTION MECHANISM IN HIGH TEM- PERATURE ETHANOL/WATER MOBILE PHASES FOR REVERSED PHASE HIGH PERFORMANCE LIQUID CHROMATOGRAPHY 43 3.1 Introduction...... 43 3.1.1 van’t Hoﬀ Analysis ...... 43 3.1.2 Linear Solvation Energy Relationships ...... 45 3.1.3 CurrentWork...... 48 3.2 Experimental ...... 48 3.2.1 Reagents ...... 48 3.2.2 Equipment ...... 49

iv 3.2.3 Procedures ...... 50 3.3 ResultsandDiscussion...... 50 3.3.1 van’t Hoﬀ Analysis ...... 50 3.3.2 Linear Solvation Energy Relationships ...... 56 3.4 Conclusions...... 66

4 ESTIMATION OF THE OCTANOL/WATER PARTITION COEFFICIENT 73 4.1 Introduction...... 73 4.1.1 Lipophilicity ...... 73 4.1.2 CurrentWork...... 75 4.2 Experimental ...... 75 4.2.1 Reagents ...... 75 4.2.2 Equipment ...... 77 4.2.3 Procedures ...... 77 4.3 ResultsandDiscussion...... 78 4.3.1 Log P Estimation ...... 78 4.3.2 Linear Solvation Energy Relationship Comparison ...... 85 4.4 Conclusions...... 91

5 SUMMARY AND CONCLUSIONS 97

Bibliography ...... 103 BiographicalSketch ...... 114

v LIST OF TABLES

1.1 Viscosity comparison of several hydro-organic mixtures of ethanol, methanol, and acetonitrile at two temperatures. Values were estimated from Figures 1.2, 1.3, and 1.4, which are from reference [86]. All values are in units of micropascals...... 24

2.1 The analytes used for solvent strength examination...... 29

2.2 Comparison of the Organic and Thermal Solvent Strength Parameters for the Ethanol Modiﬁer...... 32

2.3 Comparison of the Organic Solvent Strength Parameters for various modiﬁers. . . . . 33

2.4 Comparison of the Enthalpy of Transfer for Toluene between Subcritical Water and Ethanol/Water mobile phases...... 36

2.5 Solvent strength parameters for toluene, bromobenzene, acetophenone, benzophenone, para-chlorophenol, and para-nitrotoluene with ethanol/water mobile phases at eight temperatures between 50◦C and 80◦C along with the R2 values from the Snyder- Soczewinskiequation...... 37

2.6 The free energy term for solvent strength for toluene, bromobenzene, acetophenone, benzophenone, para-chlorophenol, and para-nitrotoluene with ethanol/water mobile phases at eight temperatures between 50◦C and 80◦C. All RTS values are in units of J/mol...... 41

3.1 The analytes used for the linear solvation energy relationship analyses...... 49

3.2 The van’t Hoﬀ analyses of ACN/H2O, MeOH/H2O, and EtOH/H2O mobile phases on an Agilent Stablebond C-18 column. The ∆G◦ and T∆S◦ are calculated at 50◦C. Entropies are in units of J/mol*K. All other terms are in units of kJ/mol...... 57

3.3 The analytes and log k’ values (average of 3 measurements) for LSER analysis of several mobile phases at 30◦C. A dash (-) indicates insuﬃcient retention for analysis. An asterisk (*) indicates retention was too long for accurate determination of the retention...... 59

3.4 The analytes and log k’ values (average of 3 measurements) for LSER analysis of ethanol/water mobile phases at several temperatures. An asterisk (*) indicates retention was too long for accurate determination of the retention time...... 60

3.5 Comparison of the Linear Solvation Energy Relationship system coeﬃcients...... 72

4.1 Analytes used in this work are noted with their octanol/water partition coeﬃcients andtheirLSERparameters...... 76

vi ′ 2 4.2 Extrapolated log kw values with R values and the estimated log P values for both acetonitrile and methanol as organic modiﬁer at 30◦C...... 79

4.3 The slope, intercept, R2 values, and standard deviation of the residuals for log P versus log k’w based on Equation 1.1. The average of the square residuals was less than 3x10−15 for all data sets...... 80

′ 4.4 Extrapolated log kw values for ethanol as organic modiﬁer at four temperatures with their R2 values...... 83

2 ′ 4.5 The slope, intercept, and R values are listed for the temperature ﬁtting of the log kw ′ values based on ethanol/water log kw values from Table 4.3. Log k’w values extrapolated to 30◦C and their estimated log P values are also listed...... 84

4.6 LSER comparison of the log k’w values based on retention data for acetonitrile, methanol, and ethanol organic modiﬁers...... 86

4.7 The cos θ values are provided for comparison of the methanol, acetonitrile, and ethanol ′ extrapolated log kw systems and the octanol/water partitioning system log P . The cos θ values are identical irrespective of the application of the Collander equation. . . 89

4.8 LSER comparison of the literature log P versus log P values estimated using methanol, acetonitrile, and ethanol organic modiﬁers...... 89

vii LIST OF FIGURES

1.1 Curve of plate height versus linear velocity at several temperatures taken from reference[28]...... 11

1.2 The experimentally determined viscosities of the binary mixture acetonitrile(1) - water(2)fromreference[86]...... 20

1.3 The experimentally determined viscosities of the binary mixture methanol(1) - water(2)fromreference[86]...... 21

1.4 The experimentally determined viscosities of the binary mixture ethanol(1) - water(2) fromreference[86]...... 22

2.1 Comparison of (a) the thermal solvent strength curve for the 10% EtOH/H2O mobile phase and (b) the solvent strength curve for ethanol/water mobile phases at a temperature of 50◦C...... 31

2.2 This curve relates the solvent strength parameter (S) of toluene to the temperature (T ) of ethanol/water mobile phases...... 34

2.3 Comparison of the eﬀect of organic content on the retention of toluene for ethanol/water mobile phases at several temperatures, acetonitrile/water mobile phases at 30◦C, and methanol/water mobile phases at 30◦C. Included for reference is the retention of toluene for subcritical water mobile phases at 100◦C and 160◦C...... 35

2.4 The solvent parameter S versus temperature for (a) toluene, (b) bromobenzene, and (c)acetophenone...... 38

2.5 The solvent parameter S versus temperature for (a) benzophenone, (b) para-chlorophenol, and (c) para-nitrotoluene...... 39

◦ 3.1 The methylene selectivity curve for the 40% EtOH/H2O mobile phase at 60 C. Ho- molog number corresponds to the number of methylene groups on alkylbenzene analytes. 51

3.2 The van’t Hoff selectivity plots for methylene are presented for the 50% and 30% ACN/H2O mobile phases, the 60% and 40% MeOH/H2O mobile phases, and 40% and 30% EtOH/H2O mobile phases. For methanol/water and acetonitrile/water mobile phases, the temperature range for the van’t Hoff analysis was 30◦C - 50◦C. For the ethanol/water mobile phases, the temperature range for the van’t Hoff analysis was 50◦C - 80◦C. A temperature axis is added for additional clarity...... 52

3.3 Histogram of the thermodynamics of transfer for methylene between a Stablebond C-18 stationary phase and one of several mobile phases: 50% or 30% ACN/H2O; 60% or 40% MeOH/H2O; 40% or 30% EtOH/H2O. For methanol/water and acetonitrile/water mobile phases, the temperature range was 30◦ - 50◦C. For the ethanol/water

viii mobile phases, the temperature range was 50◦ - 80◦C. The thermodynamic values are presentedinTable3.2...... 54

3.4 Variation of LSER system constants for ethanol/water mobile phases with varying organic content...... 61

3.5 Variation of LSER system constants for ethanol/water mobile phases with varying temperature...... 62

3.6 Linear solvation energy relationship system values for the 50% ACN/H2O, 60% MeOH/H2O, ◦ and 40% EtOH/H2O at 80 C. The standard error of each coeﬃcient is represented by verticalerrorbars...... 64

3.7 Linear solvation energy relationship system values for the 30% ACN/H2O, 40% MeOH/H2O, ◦ and 20% EtOH/H2O at 80 C. The standard error of each coeﬃcient is represented by verticalerrorbars...... 65

3.8 Predicted versus experimental log k’ values for ambient acetonitrile/water LSER sys- temsdescribedinTable3.5...... 68

3.9 Predicted versus experimental log k’ values for ambient alcohol/water LSER systems describedinTable3.5...... 69

3.10 Predicted versus experimental log k’ values for ethanol/water LSER systems described inTable3.5...... 70

3.11 Predicted versus experimental log k’ values for ethanol/water LSER systems described inTable3.5...... 71

4.1 Collander plots based on the acetonitrile, methanol, and ethanol modifiers at 30◦C. ′ ◦ The ethanol-based log kw values were extrapolated to 30 C from higher temperatures. 81 ′ 4.2 Histogram comparing the LSER system constants for the log kw RPLC systems extrapolated from methanol/water, acetonitrile/water, and ethanol/water mobile phases. The coefficients for the octanol/water partition system is also provided. The standard error of each coefficient is represented by vertical error bars. The order of the sys- ′ tems presented is the octanol/water partitioning system and the log kw extrapolated systems based on the methanol, acetonitrile, and ethanol modifiers...... 87

4.3 Values for each coeﬃcient in the extrapolated methanol, acetonitrile, and ethanol systems after application of the Collander equation and the octanol/water partition system. The standard error of each coeﬃcient is represented by vertical error bars. Order of the systems is octanol/water partition, acetonitrile estimated log P , methanol estimated log P , and ethanol estimated log P ...... 90

′ ◦ 4.4 Predicted versus experimental log k’ values for the log kw system at 30 C that was extrapolated from acetonitrile/water, methanol/water, or ethanol/water mobile phases. LSER coeﬃcients are described in Table 4.6...... 94

ix 4.5 Predicted versus experimental log P values for the estimated log P system at 30◦C that was extrapolated from acetonitrile/water, methanol/water, or ethanol/water mobile phases. LSER coeﬃcients are described in Table 4.8...... 95

4.6 Predicted versus experimental log k’ values for the octanol/water partition system at 25◦CdescribedinTable4.8...... 96

x LIST OF ABBREVIATIONS

ACN acetonitrile BDS base deactivated silica DDE dichlorodiphenyldichloroethylene DDT dichlorodiphenyltrichloroethane EtOH ethanol H2O water HPLC High Performance Liquid Chromatography LSER Linear Solvation Energy Relationship MeOH methanol ODS octadecyl sulfate PBD polybutadiene PEEK polyether ether ketone PS-DVB polystyrene-divinylbenzene RP reversedphase RPLC Reversed Phase Liquid Chromatography SCWC Subcritical Water Chromatography UV-Vis ultraviolet-visible

xi ABSTRACT

Reversed phase liquid chromatography is a common analytical technique employed in a variety of fields as a core component in many developmental and validation methods. Tradition- ally, this technique uses acetonitrile/water or methanol/water mixtures as the mobile phase to elute analytes of interest via differential partitioning to and from an embedded stationary phase. These organic modifiers, however, are generally toxic and flammable. This toxicity and volatility necessitates additional care in use and disposal compared to greener solvents. In particular, the more commonly employed acetonitrile is moderately toxic and produces significantly more toxic substances upon decomposition (nominally carbon monoxide and hydrogen cyanide), which further complicates its disposal. In addition, while methanol is less toxic, this modifier is more volatile and requires significantly greater concentrations for similar eluotrophic strength. Ethanol, on the other hand, provides a greener alternative due to its low toxicity to both humans and the environment. Furthermore, the eluotrophic strength of ethanol is comparable to the acetonitrile modifier, thus requiring smaller organic concentrations than methanol. Ethanol’s boiling point is also significantly greater than methanol’s boiling point, thereby reducing the flammability risk versus methanol. Finally, moderate increases in temperature are found to significantly reduce the several times greater viscosity of ethanol/water mixtures versus traditional mobile phases. The application of higher temperatures also provides a secondary benefit by increasing the elution strength of the mobile phase, thereby reducing run times and, more importantly, ‘organic waste’. In this work, high temperature ethanol/water mixtures were explored in-depth as a green alternative to traditional hydro-organic mobile phases. Solvent strength comparisons revealed that high temperature ethanol/water mixtures have a broad range of eluting power that exceeds 60% ambient acetonitrile/water mobile phases without requiring greater than 50% ethanol content. In addition, retention was demonstratively less sensitive to changes in temperature than to changes in organic content. From an examination of the effect of these changes on the retention mechanism by linear solvation energy relationships, it was determined that temperature altered retention more by affecting the relative difference in the hydrogen-bond acidity while ethanol content largely changed retention by modifying mobile phase cavity formation effects.

xii From a van’t Hoff analysis of the thermodynamics of transfer for a methylene substituent, the structures of the mobile phase was revealed to differ considerably. In particular, it was determined that the greater elution strength of the ethanol modifier is a result of a reduction in the favorable change in entropy by transfer of the analyte out of the mobile phase. This observation implied that a mobile phase cavity around a non-polar analyte is more stable when employing ethanol as the modifier. Finally, high temperature ethanol/water mobile phases were examined in the chromatographic approximation of pure water retention (log k’w) and subsequent estimation of the octanol/water partition coefficient (log P ) via the Collander equation. Linear solvation energy relationships were employed to compare the log k’w extrapolated systems based on high temperature ethanol/water, ambient acetonitrile/water, and ambient methanol/water mobile phases. Based on the comparisons of the three organic modifiers, high temperature ethanol/water mobile phases were observed to provide the best estimation of log P . This conclusion is based on a high Collander correlation of 0.978 and a near unity cos θ value of 0.998 between the LSER coefficient vectors of ethanol/water log k’w and octanol/water log P systems.

xiii CHAPTER 1

INTRODUCTION

The elements that are the foundational components of stellar bodies, planetary surfaces, and life itself rarely manifest themselves as pure substances in the universe. Even combinations of these elements as molecules of varying sizes are almost always found in highly complex mixtures. The separation of compounds and elements into purer forms has been the focus of experimentation and analysis since before written history. The earliest known evidence of separation methods by humans dates back to 6500 BC in the prehistoric village of Çatalhöyük in Turkey, wherein cast iron beads were found [1]. Over the millenia, processes for separation and purification were discovered, improved upon, and replaced with more efficient methods. As with most areas of human civilization, the introduction of the scientific method and the foundation of a broad scientific community combined with the proliferation of knowledge gained therefrom has brought forth significant advances in separation and purification research. No single area has drawn as much focus for separation science as the purification and identification of organic compounds. This process is of such significant interest to human society because an enhanced understanding of a wide variety of organic molecules is required to properly explore the chemistry of life. These separations, however, are complicated by (a) the complexity of their mixtures, (b) the often trace quantities of organic analytes, and (c) the similarity in size and shape of target compounds with other organic molecules. Scientific experimentation has resulted in the development of numerous methods and techniques for the analysis and separation of these molecules. Among these separation techniques, chromatography and more specifically, reversed phase high performance liquid chromatography has been one of the most widely implemented separation techniques in analytical analyses. In fact, it is the most common method employed for chemical separations throughout academic, industrial, and governmental labs. In fact, the market size for all chromatographic instruments within North

1 America was estimated to reach over $7 billion by the end of 2015, with an expected yearly market share of nearly $9.2 billion by the year 2020 [2]. The foundation of chromatography is often attributed to the Russian botanist Mikhail Tswett who separated plant pigments in the early 1900’s using a technique that is known today as liquid-adsorption column chromatography. The simplicity of this initial technique, paired with its ability to rapidly separate a complex mixture into individual pigments, has resulted in it being commonly selected as an introductory chemistry experiment for students from middle school to freshman college. From a modern perspective, early chromatography would appear to be a promising method for further development and study. However, it was largely ignored and/or rejected until the 1930s. The reasons for the rejection of chromatography for decades is difficult to know definitively, but based on a detailed review of various primary sources, Johnathon Livengood [3] reasoned that chromatography lacked the necessary theoretical and practical support of that period. The major criticisms from Tswett’s contemporaries focused on the lack of theoretical justi- fication combined with results that conflicted with the canonical theories of that era. In addition, there was no practical precedent for that method nor any connection to contemporanous practices. The situation in the 1930s, however, was significantly different. The common use of spectroscopy in chemistry coupled with the development of adsorption theories provided the scientific groundwork necessary for the “rebirth of chromatography.” [3] By the 1940s, modern liquid chromatography would begin to form with the introduction of liquid-liquid column chromatography by Martin and Synge. Their technique is now referred to as partition liquid chromatography [4, 5]. This foundational work combined chromatography and countercurrent solvent extraction for the separation of amino acids among other organic molecules. More importantly, their study included the theory behind how the chromatographic method works. By the time Martin and Synge received the Nobel Prize in 1952, partition chromatography had split into several well-known and potent techniques that comprise paper chromatography, reversed phase liquid chromatography, and gas chromatography [6]. The simplicity, efficacy, and adaptability of partition chromatography have resulted in its widespread implementation and, subsequently, produced a significant impact across chemical, biological, and biomedical fields.

2 1.1 Reversed Phase High Performance Liquid Chromatography 1.1.1 Basic Concepts

Partition chromatography is a process wherein a chemical mixture is carried by a mobile phase through a column containing a stationary phase and separated into its components by the time-based differential partitioning of the solutes between the two phases. To add further clarifi- cation; in all chromatographic separations, the sample is dissolved in a mobile phase, which may be a gas, a liquid, or a supercritical fluid. This mobile phase is then forced through an immiscible stationary phase, which is fixed in place within a column on a solid surface. For traditional HPLC, a high pressure pump (400 to 800 bar max rating) propels the mobile phase (a mixture of water and a polar organic modifier - usually acetonitrile or methanol) through a manual sample injection valve or autoinjector device wherein the sample mixture is injected into the flow path and then subsequently travels into the HPLC column as an analyte plug. The stationary phase consists of a porous bed with non-polar ligands packed within a stainless steel column from 5 cm to 25 cm in length while inner diameters are usually between 2 mm and 5 mm. The most common commercially available packed beds consist of silica particles coated with ligands containing either 8 or 18 carbons. The column is usually surrounded by a heated water sleeve or a conduction heating oven. The analytes will generally elute in order from strongest to weakest polarity. It should be noted that the shape, size, and hydrogen-bonding basicity of an analyte contributes significantly to analyte retention. The analytes are then detected via an UV-Vis spectrometer or an Electrospray Ionization Mass Spectrometer. The partitioning process of an analyte is examined by the thermodynamic constant K - the partition coefficient. This constant is defined in Equation 1.1 where Cs and Cm represent the molar concentration of the solute in the stationary phase and the mobile phase, respectively.

C K = s (1.1) Cm A more practical measure of the equilibrium is obtained via the retention factor, which is deﬁned as the ratio of the quantity of analyte in the stationary phase versus quantity of analyte in the mobile phase. This term is related to the equilibrium constant shown in Equation 1.2 where

Vs and Vm are the stationary and mobile phase volumes, respectively, and θ is the volume phase ratio for the column.

3 ′ V k = s = Kθ (1.2) Vm Since the volume is related to time relative to the ﬂow rate, Equation 1.2 can be rearranged to give the ratio shown in Equation 1.3 where tr is the analyte’s retention time and t0 is the void time (usually measured for each run via an unretained analyte).

′ t − t0 k = K r (1.3) t0

1.1.2 Eﬃciency and the van Deemter Curve

When an analyte is injected into the flow path, it starts as a liquid plug that is narrow in longitudinal width. Over the course of its progress through the column, the plug will broaden based on partition processes both within and between the mobile and stationary phases. The efficiency for a column is a measure of a column’s relative ability to produce narrow analyte peaks. This efficiency is directly measured by the parameter N known as the number of theoretical plates.

Theoretical plate count is experimentally defined in Equation 1.4 where σt is the standard deviation of the peak and is measured in the same units as the retention time resulting in a dimensionless plate count. Plate count can also be defined in relation to the width at half-height (W0.5) as shown in Equation 1.4. As Equation 1.4 demonstrates, higher plate counts indicate narrower and more efficient peaks. Equation 1.4 also reveals that more efficient columns are ones that produce longer retention times without significantly greater peak widths.

t t N =( r )2 = 5.54( r )2 (1.4) σt W0.5 One major caveat to Equation 1.4 is that it assumes chromatographic peaks are symmet- rically Gaussian. Other and more accurate methods can be employed; including measurement of statistical moments [5] and the Foley-Dorsey equation, which treats chromatographic peaks as ex- ponentially modiﬁed Gaussians [7]. The Foley-Dorsey equation is presented in Equation 1.5 where

W0.1 is the peak width at 10% height and B/A represents the peak asymmetry with B deﬁned as the distance between the peak maximum and right-hand peak edge at 10% height.

2 tr W0.1 N = 41.7 B (1.5) 1.25 + A

4 Equation 1.5 is most effective for tailing peaks (accurate to within 2% for asymmetries measured between 1.0 and 2.76). As presented, plate count represents the square ratio of an analyte’s retention to the time-based width of an analyte peak. Consequently, this means a more efficient column is one that retains an analyte longer with minimal band broadening of the analyte plug. One limitation of plate count is its specificity to column length (L) and particle diameter

(dp). Two measures of column efficiency that account for these differences in geometry are theoretical plate height (H ) and reduced plate height (h). Theoretical plate height is defined as the ratio of column length to plate count. Reduced plate height is the ratio of plate height to particle diameter. Theoretical plate height (H ) is operationally defined as the ratio of the column length to its plate count. In chromatography, the sample injected into the system gradually separates into plugs of the component analytes due to differential partitioning. As each plug of analyte progresses through the column, it broadens thereby resulting in a time-based distribution for elution of the analyte molecules. The band broadening process is best described using rate theory developed by J.C. Giddings in the late 1950s to the early 1960s [8–10]. This theory characterizes band broadening as the total variance (σ2) with component band broadening processes being additive as shown in Equation 1.6. The three most significant intra-column processes that cause this broadening are referred to as (1) longitudinal diffusion, (2) resistance to mass transfer, and (3) Eddy diffusion.

2 2 2 2 σT = σ1 + σ2 + σ3 + ... (1.6)

Longitudinal diffusion is the diffusion of analyte molecules in a liquid mobile phase. Using Einstein’s equation for molecular diffusion, Equation 1.7 [11] can be derived to determine the contributing factors from longitudinal diffusion toward band broadening. In Equation 1.7, HLD is defined as the contribution of longitudinal diffusion to plate height, DM is the diffusion coefficient of the solute in the mobile phase, v is the linear velocity of the mobile phase, L is the length of the column, and γ is the obstruction factor due to restriction of the mobile phase flow by the solid stationary phase particles. Values for the obstruction factor are usually 0.6-0.7 for packed columns and 1 for open tubular columns. Additionally, longitudinal diffusion produces extra-column band broadening of the analyte plug as it progresses from the injector to the column and from the column

5 to the detector. As a result, total variance should be subtracted by the extra-column variance due to this additional diﬀusion.

σ2 2γD H = LD = M (1.7) LD L v The second band broadening process is more complicated, since it comprises both a mobile phase and a stationary phase component. As an analyte passes through the column, the analyte continually transfers between the mobile and stationary phases. This process is, however, not instantaneous due to the time required for the analyte molecules to diﬀuse through the mobile phase to enter the stationary phase. This distribution results in some molecules close to the stationary phase entering sooner than other molecules. During this time, molecules in the mobile phase are swept along the column and away from those that diﬀused into the stationary phase. Increasing the velocity of the mobile phase will thereby produce a higher dispersion of analyte molecules and thus the analyte peak will be broadened. This process is referred to as resistance to mass transfer in the mobile phase. The plate height contribution from this band broadening process is proportional to the ratio shown in Equation 1.8 where dp is the particle diameter.

2 dpv Hm ∝ (1.8) DM Dispersion due to resistance to mass transfer in the stationary phase is exactly analogous to that in the mobile phase. Those molecules close to the interface of the two phases will leave the stationary phase and enter the mobile phase sooner than those that diffused further into the stationary phase. Thus, those molecules that quickly enter the mobile phase due to proximity to the interface will be swept away from those molecules that had diffused deeper into the stationary phase. The plate height contribution from this band broadening process is proportional to the ratio shown in Equation 1.9 where k’ is the retention factor, Ds is the diffusion coefficient of the solute in the stationary phase, and df is the diffusion distance in the stationary phase. When the stationary phase is totally porous, df is equal to dp.

′ 2 k df v Hs ∝ ′ 2 (1.9) (1 + k ) Ds Eddy diﬀusion, the third intra-column band broadening process is due to the distribution of paths that solute molecules can take while traveling in the mobile phase through the column. The

6 packed bed in the column has numerous pathways through which the mobile phase and analyte molecules can travel. Some paths will be shorter or longer depending on the size of the molecule and the packing of the bed. This range of paths will result in a broader time-based distribution of molecules. Unlike the other two processes, Eddy diffusion is an insolvable problem as it is not possible to accurately approximate the molecular diffusion of every analyte molecule through a random and irregularly packed bed unless the packed bed is specifically and carefully etched. This band broadening process is independent of the mobile phase velocity, as speed does not factor into the paths in which an analyte molecule will traverse through the packed bed. The van Deemter equation for plate height is the combination of the above processes as presented in Equation 1.10 where A is the Eddy diffusion term, B/v is the longitudinal diffusion term, and Cv is the combination of the mobile phase and stationary phase resistance to mass transfer terms. When the flow velocity is increased, the Cv term increases and the B/v term decreases. This results in an asymptotic curve with a minima where the plate height is at its shortest, the efficiency at its largest, and the linear velocity at its most optimal value.

H = A + B/v + Cv (1.10)

1.1.3 Environmental Implications

Throughout history, the growth of human population densities has resulted in societies facing problems from pollution as a byproduct of human and industrial waste. Even in antiquity, mining operations were responsible for widespread pollution as evidenced by the detection of significant lead contamination within Greenland’s ice sheets that date from periods of heavy mining operations by the Greek and Roman civilizations [12]. While the impact of human pollution has been significant in the past, the industrial revolution dramatically magnified both its scale and severity. By the mid-20th century, long-term negative effects were becoming apparent with acid rain deteriorating forests, chlorofluorocarbons depleting the ozone layer, and widespread bioaccumulation of numerous chemical substances resulting in negative impacts on the health of many species, including humans. Since that time, many governments have been involved in the creation and enforcement of numerous regulatory measures that were designed with the express purpose of safeguarding the environment. These regulations have underscored the need for proper containment and disposal of various hazardous chemicals. The field of study known as “Green Chemistry” evolved in order

7 to mitigate the impact of harmful substances on the environment and reduce the industrial costs associated with their disposal. Green Chemistry was established primarily to focus on the development of chemical processes and earth-friendly products that would reduce pollution by limiting the use of hazardous substances and minimizing the consumption of natural resources. A major objective of this field is the implementation of green solvents for various applications. The solvents that have received the most attention are carbon dioxide, ionic liquids, and water. However, a survey of solvent related publications in the Journal of Green Chemistry [13] revealed that most efforts in this field have been concentrated on the replacement of hazardous solvents with green solvent substitutes as it respects the areas of synthesis, reaction media, and biomass processing. While a reduction in the use of hazardous solvents in chemical synthesis does lessen the overall negative impact on the environment, it only affects a small fraction of the total volume of hazardous solvents currently used in society. Chromatographic applications are a major consumer of hazardous solvents, which are utilized for a variety of far-reaching purposes. These purposes include research activities as well as routine medical, industrial, food, and environmental analyses. Liquid Chromatographic systems, themselves, are responsible for the generation of liquid hazardous waste that exceeds a liter per day [14]. Over the course of the past decade, interest in Green Analytical Chemistry has intensified with the publication of several key books in the field [15–17]. In addition, several major Analytical Chemistry journals have devoted entire issues to this research area [18–20]. In particular, the field of Green Chromatography has largely focused on the replacement of existing hazardous solvents or the reduction of solvent usage [21]. One of the greenest solvents that has received the most attention by green chemists is pure water. In reversed phase liquid chromatography, water is traditionally considered a weak eluent. The percentage of water is increased to reduce the elution strength of stronger non-polar organic solvents. The two most common solvents are acetonitrile and methanol with acetonitrile being the modifier of choice for HPLC in many industrial and research applications. The most common commercial source for acetonitrile is generated as a byproduct of acrylonitrile production, thus the availability of acetonitrile is dependent upon the production needs for acrylonitrile. As acetonitrile is widely used in the industry, shortages of acetonitrile are possible such as the acetonitrile shortage in 2008 [22]. In addition, acetonitrile is a toxic solvent that can cause cyanosis, cardiac arrest, and

8 respiratory distress if ingested or absorbed through the skin in significant quantities. It is also a mild irritant to the eyes, nose, and/or throat [23]. As a result, acetonitrile waste is considered an EPA hazardous waste requiring specific procedures for proper disposal due to the production of hydrogen cyanide and carbon monoxide during combustion. At first glance, increasing the water content would appear to reduce the concentration of organic waste produced; more importantly however, highly aqueous mobile phases carry significant consequences related to the chromatographic mechanism. More specifically, weaker mobile phases result in greater retention volumes requiring higher (less efficient) flow rates and/or increased retention times. In addition, the analyte plug is significantly broadened resulting in decreased resolution and efficiency. Gradient elution is a possible solution to this issue as it increases organic content over time, which improves the resolution of all analytes in the mixture while also reducing the run time. Gradient elution with large differences in the starting and ending composition, however, can result in shifting baseline, changes in analyte elution order with different flow rates, and/or increased re-equilibration time [24]. Nevertheless, the addition of a re-equilibration volume of mobile phase mitigates any potential reduction in total organic waste from using gradient elution. Therefore, other methods must be explored in order for chromatography to become a greener technique. As part of this effort, this dissertation will examine the combination of temperature and the green solvent ethanol.

1.2 High Temperature Chromatography 1.2.1 Basic Concepts

High temperature liquid chromatography generally refers to liquid chromatography performed at temperatures greater than ambient temperature, which is usually capped at 200◦C. The appeal of higher temperatures comes from the faster kinetics for analyte partitioning and a reduction in mobile phase viscosity resulting in lower system backpressure. In addition, moderately high temperatures (35◦C to 60◦C) can generally be employed without major changes to the chromatographic system with only the addition of a temperature regulating apparatus. Beyond 60◦C, various features of the system must be adjusted to allow for prolonged experimentation including the thermal stability of both the stationary phase and the system’s tubing, limits of the heating apparatus, and evaporation of the mobile phase. Above the boiling points of mobile phase mix-

9 tures, additional backpressure can be simply added via a backpressure regulator to prevent mobile phase evaporation within the column. Application of temperature will require stainless steel for most of an instrument’s tubing when very high temperatures (above 100◦C) are used. According to John Batts of IDEX Health & Science [25], the reason for this requirement is that the alternative PEEK tubing and fittings are not recommended for conditions that exceed 100◦C. This restriction is necessary to minimize long-term degradation of the PEEK material. Finally, according to the Phenomenex HPLC Column Protection Guide [26], the traditional silica RPLC column generally has a recommended thermal limit of 60◦C. Another common consideration in high temperature liquid chromatography is analyte stability. Decomposing analytes generate significant problems in liquid chromatography (LC) by reducing the concentration of the analytes of interest and adding numerous unknown peaks to account for in a separation. Fortunately, analyte decomposition is rarely observed as many complex analytes are thermally stable in both high temperature liquid chromatography and gas chromatography [24]. It is reasonable to surmise that this stability is a result of the limited time during which an analyte experiences these high temperatures. As previously shown in Section 1.1.2, flow rates higher than optimum have reduced efficiency due to an increase in the C term. The C term, however, is inversely proportional to the diffusion coefficient such that an increase in the diffusion coefficient will decrease the impact of the C term and increase the optimum flow rate. As a result, separations performed at higher temperatures will be more efficient as long as the flow rate is not changed and remains less than the optimum flow rate for that temperature. One means of empirically estimating the diffusion coefficient is the Wilke-Chang equation

[27] presented in Equation 1.11. The terms for this equation are defined as follows; Dm is the diffusion coefficient in cm2/s, η is the solvent viscosity in cP, M is the solvent’s molecular weight, 3 T is the absolute temperature in Kelvin, and Va is the solute molar volume (cm /mol). The final parameter, φ, is a dimensionless “association factor” for the solvent that accounts for hydrogen- bonding with values ranging from 1.0 for non-polar solvents to approximately 2.6 for water.

1/2 −8 (φM) T Dm = 7.4x10 0.6 (1.11) ηVa

10 An increase in diffusion is therefore achieved at higher temperatures both directly and via a decrease in the viscosity. Thus, the main benefit for elevated temperatures in HPLC is the ability to perform separations at faster flow rates while maintaining a similar level of efficiency. This theoretical conclusion is shown experimentally in Figure 1.1 with a comparison of representative van Deemter plots at several temperatures from work done by Vanhoenacker and Sandra [28]. Another detail shown in Figure 1.1 is that the maximum plate count/minimum plate height is independent of analysis temperature assuming adequate mobile phase preheating. As such, increases in temperature do not improve maximum column efficiency but merely increase the linear velocity at which the optimal condition is found. In addition, the negative impact from the C term on greater than optimal flow rates is reduced by this lower diffusion. However, it should be pointed out that for velocities greater than optimal, an increase in temperature will result in an increase in efficiency at that velocity as long as that velocity remains greater than the optimal velocity for the van Deemter curve at that temperature. This conclusion has been proven both theoretically [29] and experimentally [30].

Figure 1.1: Curve of plate height versus linear velocity at several temperatures taken from reference [28].

There have been studies performed by Yang et al. [31, 32] for a variety of columns that present results that conflict with this conclusion. In one of their studies, efficiency was found to increase or remain unchanged for part of the temperature range before decreasing at higher temperatures while a second study generally observed decreasing efficiency across the entire range

11 of temperatures. This efficiency loss implies that band broadening is influenced by other factors at higher temperatures. One possible explanation provided by Guiochon [33] is that radial temperature gradients are forming within the column. As the mobile phase travels through the column, heat will flow between the heated stationary phase to the cooler mobile phase until thermal equilibrium is reached at some length of the column. The time or length of column required is dependent upon multiple aspects of the system: (1) the heat conductivities of the two phases, (2) the diameter of the column, (3) the flow rate of the pump, and (4) the difference in temperature between the two phases. As a result of these differences, thermal gradients form throughout the column both within the packed bed and along the flow path. A significant factor in the band broadening process is the distribution of molecular velocities of the analyte molecules. Given the temperature dependence of the molecular velocity and the diffusion rate, the heterogeneity in temperature from the aforementioned gradients will result in a broader analyte plug. The analyte molecules will therefore experience a range of increasing temperatures across the column rather than a single homogeneous temperature for the entire retention process. A reduction in efficiency with higher temperatures is consequently expected as the temperature begins to exceed the ability of the heating system to adequately preheat the mobile phase before it enters the column. This situation leads to the question of the impact of ‘oven choice’ on mobile phase heating. In a comparison of the effect of preheating coil lengths, Fields et al. [34] found that a 15 cm long coil with air convection heating resulted in distorted or split peaks for steroids separated at 160◦C at greater than 1 mL/min flow rates while a 150 cm long coil generated symmetrical peaks at the same conditions. To overcome this problem, Teutenberg et al. [35] demonstrated that tightly-clamped, pre-heating capillaries within a block-heating oven provide efficient peak shapes with temperatures as high as 185◦C at a 5 mL/min flow rate. Another extra column effect on efficiency and retention is viscous heat dissipation, which occurs when a liquid is pumped through a packed bed at high pressure. Gritti and Guiochon [36] demonstrated this effect when the retention factor of phenol decreased with increasing flow rate from 0.025 mL/min to 4.9 mL/min at ambient conditions. They also found the dissipation to decrease at higher temperatures. This effect was predicted and then verified in an early work by Poppe and Kraak [37] who determined the solute band would become distorted with higher flow rates at room temperature.

12 Finally, temperature programming is often cited as an advantage with high temperature liquid chromatographic methods since it would seemingly provide similar retention controls as solvent programming without the requirement to change solvents or use a gradient pump. As explored by Synder [38], this technique is only marginally better than isocratic elution (a run performed at a single mobile phase composition) and significantly inferior versus solvent programming (a run performed with gradually changing mobile phase composition). One of the biggest limits to implementing temperature programming is the potential for heterogeneity within the column and between stationary and mobile phases. Even with adequate preheating to minimize differences between the set temperature and temperature of the mobile phase, the relatively poor thermal conductivity of silica (<0.20 W/m/K [39]) and polymeric (<0.21 W/m/K for PS-DVB [40]) stationary phases versus stainless steel (33.6 W/m/K [41]) will result in temperature gradients between the stainless steel column and the innermost portions of the packed silica or polymer bed. As a result, most contributions involving temperature programming with LC have used capillary or micro bore columns [42]. A review by Teutenberg [42] noted that the argument of implementing temperature programming for its ability to improve separation efficiency is not a practical consideration. This conclusion is based on the success of modern solvent gradient elution methods to provide band compression and high peak capacity with the caveat that temperature programming has the potential to improve “special hyphenation techniques,” such as screening for biomarkers in complex matrices. In fact, Teutenberg later points out that temperature programming will not improve separation efficiency versus solvent programming with the exception that concomitant use of both could optimize selectivity. It was determined that temperature programming is not considered a viable technique with high temperature liquid chromatography using packed beds.

1.2.2 Subcritical Water Chromatography

One area where high temperatures have been employed to provide greener chromatography is with heating pure water to subcritical temperatures (usually deﬁned 80◦C to 300◦C) while under moderate pressures of less than 100 bar. This application of temperature is found to decrease the polarity of liquid water to values comparable to the hydro-organic solvent mixtures commonly used in RPLC [43]. As a result of this temperature dependence, pure water mobile phases at subcritical temperatures can be used in place of organic modiﬁer mixtures with changes in temperature altering

13 the mobile phase polarity. This technique is known as Subcritical Water Chromatography. Since polarity is changed by altering the temperature, however, SCWC is limited to isocratic elution due to the issues regarding temperature programming as previously mentioned in Section 1.2.1. When water is heated under pressure to subcritical temperatures, there are significant changes to its polarity. The polarity is often expressed as the dielectric constant. As the temperature is increased, the dielectric constant of water decreases and is observed to mimic a range of organic [44] and hydro-organic solvents [45] at sufficiently high temperatures (100◦C to 225◦C). In addition, other physical and chemical properties of water are dramatically different between ambient conditions and higher temperatures/pressures. For instance, the dissociation constant of water is significantly changed by a rise in temperature. The dissociation constant of water at room temperature is 1.0 x 10−14. As the temperature is elevated to subcritical levels, this constant is observed to increase three-fold [46]. Due to this greater auto-ionization, subcritical water has been used as an acid/base catalyst in organic reactions [47]. Similarly, the viscosity and density of water has been shown to decrease by over 80% and by 0.5 g/mL, respectively [48]. These changes in the thermodynamic properties of water are usually attributed to the effect of temperature and pressure on water’s hydrogen-bond network. The self-associating nature of water is caused by the presence of four hydrogen-bonding sites on each molecule. For this reason, water is shown to be formed by clusters of self-associating molecules with varying numbers of hydrogen-bonds [49, 50]. As the temperature of water rises, some of the hydrogen-bonds are broken, and therefore, the hydrogen-bond network is also altered. This change in the number of hydrogen-bonds was observed through Raman scattering experiments that revealed a decrease in the mole fraction of these interactions from a value of 0.78 at ambient conditions to 0.60 at 100◦C [51]. Around the turn of the millenium, SCWC began to receive significant attention from researchers as a feasible green alternative. In several studies performed by Smith and Burgess [52, 53], the retention times of phenols on a polystyrene divinylbenzene column using SCWC were determined to decrease with increasing temperature. These results were validated in a similar study conducted by Miller and Hawthorne who separated homologous alkanols [54]. Their work indicated that SCWC can be applied to typical reversed phase separations of moderately polar analytes.

14 Importantly, the replacement of RPLC by SCWC would therefore seem to have the potential of significantly reducing production of hazardous waste across many applications. Furthermore, SCWC has been applied to a number of analyses that have traditionally been completed using RPLC. A comparison of these two techniques demonstrates that SCWC provides an improvement over RPLC in certain cases as detailed herein. Some of these analyses have included separations of pharmaceutical compounds [55–59] and natural product extracts [60, 61]. SCWC has also been used in environmental studies of polycyclic aromatic hydrocarbons [62], fungicides [63], and herbicides [64]. For example, studies have shown that buffered deuterated subcritical water can separate a set of model drugs in online LC-NMR and LC-NMR-MS systems [55, 56]. Additionally, deuterated subcritical water has been used in the analysis of water-soluble vitamins pyridoxine, riboflavin, and thiamine [65]. These three vitamins are known for their thermal instability and pH sensitivity. They were each analyzed under a different set of experimental conditions that included column type, pH, and temperature. At ambient conditions, the analysis of pyridoxine and riboflavin usually require a buffered mobile phase, thus necessitating additional preparation time. However, in subcritical water, no buffer was needed for their analysis. In the area of natural product analysis, isothermal conditions do not produce satisfactory results, so to improve the separation, a temperature gradient was successfully applied in a study by Saha etal. [60]. Although gingerols, which are the main components of ginger, usually undergo thermal decomposition in techniques that employ high temperatures, such as gas chromatography, no decomposition was observed during their study with subcritical water.

1.2.3 Drawbacks to SCWC

While SCWC appears to be a strong competitor to traditional ambient separations, there are several major drawbacks that limit its utility. The most signiﬁcant one is related to the higher temperatures required to achieve reasonable retention times. Silica-bonded columns commonly used in RPLC have limited stability and longevity at high temperatures especially under highly aqueous conditions. In addition, stainless steel is required for most of a SCWC’s instrument tubing due to high temperatures exceeding the temperature ceiling of the PEEK tube material [25]. In a study by Ian Wilson examining the separation of model drugs using SCWC on various columns, it was found that the silica-based C-18 BDS Hypersil only lasted two working days at temperatures ranging between 23◦C and 160◦C before signiﬁcant loss was observed in chromatographic

15 performance [58]. Since most applications of RPLC use silica for the stationary phase substrate, the brief lifetime of silica-bonded phases under subcritical water conditions would significantly limit research and analyses. In addition, application of SCWC with silica-bonded columns in an industrial setting would require an increased frequency of column replacement that would far exceed any cost benefits achieved with a pure water mobile phase. Several alternatives to silica-based phases have been in development to improve stationary phase stability at higher temperatures and pHs. Some of these alternatives include hybrid silica phases, zirconia-based columns, and carbon-based columns among others [24]. Hybrid silica stationary phases combine polymer and silica technologies to create porous sol- gel hybrid particles. The resulting particles are formed from organic (organosilane) and inorganic (tetraalkoxysilane) moieties on which to bond RP column packings. These stationary phases are superior to single moiety silica phases due to (1) reduced silanol acidity, (2) greater stability to alkaline mobile phases, and (3) improved mechanical strength [66]. In a study by Edge et al. [59], a variety of columns examined were observed to be relatively stable under harsher conditions such as ultra high pressures, low organic mobile phases, and highly acidic or basic pHs. Hybrid silica phases such as the methylsiloxane-based Xterra [59, 67] and ethylene-bridged hybrid silica phase Xbridge columns [68, 69] have been applied to subcritical water chromatography. In a study by Louden et al. [70], a C-8 Xterra column was determined to be stable up to 160◦C with 100% deuterated water. In a study by Wilson [58], a C-18 Xterra column produced excellent peak shape at 165◦C subcritical water in comparison to a BDS Hypersil under the same conditions for the separation of model drugs. The study also found that the BDS Hypersil only lasted two working days before losing significant chromatographic performance, whereas the Xterra column did not suffer this limitation over the course of his experiment. Similarly, in an article by Wyndham et al. [68], they stated that the Xbridge column was stable up to 200◦C for separations of anilines, alkylbenzenes, and phenylalkanols. Nevertheless, in a study by Nawrocki et al. [71], this stability was demonstrated to be significantly limited. In their work on the pH and thermal stability of various silica and metal oxide phases for HPLC and SCWC, Xterra columns demonstrated stable efficiency for only several hundred column volumes of exposure to a mobile phase of 30% acetonitrile in 50 mM potassium phosphate aqueous solution at 85◦C and pH 7. In addition, by 1800 column volumes, the efficiency had dropped by over 50%

16 in plate count. While the Xbridge column is shown to be more stable than the Xterra column; it was still observed that long periods of exposure to water mobile phases and high temperatures will slowly deteriorate the efficiency of the column [72]. As such, more involved and detailed SCWC studies employing these columns will require efficient experimental designs to avoid exceeding that column’s lifespan over the course of the study. As indicated by the studies referenced in this section, the stability of hybrid silica under high temperature is greater than that of pure silica phases under harsher conditions, but the loss in efficiency still occurs after prolonged exposure to subcritical water mobile phases at a faster rate than when employing traditional mobile phases. Harsher conditions are generally defined as those temperatures, pressures, and/or pH’s that stress the stationary phase and lead to increased degradation of the stationary phase. The zirconia-based columns use the metal oxide zirconia instead of silica for the reversed- phase substrate. Zirconia and other metal oxide substrates; specifically alumina and titania have a longer lifespan and are more stable at harsher temperatures and pH’s compared to silica-based phases [24]. The main reason for the higher stability of metal oxide phases over silica-bonded columns is the high solubility in water of amorphous silica used for HPLC columns, which ranges from 2x102 ppm at 50◦C to 9x102 ppm at 200◦C [73]. The literature regarding the preparation and use of these metal oxide phases has been thoroughly reviewed by Nawrocki et al. [71, 74, 75]. The generation of alkyl-bonded RP materials for metal oxide-based substrates, however, has been more involved and complicated due to a number of factors: (1) differences in acid-base thermochemistry and kinetics, (2) significantly reduced body of literature for metal oxide solution chemistry, (3) greater propensity of metal oxides to crystallize during sinterization - a process wherein the oxides are fired at a sufficiently high temperature to achieve vitrification without complete fusion of oxides, and (4) requirement of mobile phase additives due to the amphoteric properties of metal oxides [71]. Even with these complications, metal oxide columns have been produced commercially with a variety of ligand chemistries such as (1) polybutadiene, polystrene, or carbon-coated zirconia phases; (2) polybutadiene-coated or polysiloxane polymer alumina phases; or (3) Sachtopore polymer-coated titania phases. Zirconia columns are marketed by ZirChrom with a recommended thermal limit of 150◦C with multiple studies demonstrating stationary phase stability at even higher temperatures [34, 76–

17 78]. In a SCWC study by Wu et al. [77] using a PBD zirconia monolithic capillary column, the column was found to give reproducible retention times of phenol and 3,5-dimethylphenol for at least 200 hours at 150◦C and at 200 atm inlet pressure. They also noted that the column was stable at 260◦C and at 200 atm inlet pressure. In a different study by Fields et al. [34], a number of steroids were separated on zirconia PBD column at 160◦C with an elution order similar to that observed when using an ODS silica-bonded column at room temperature with 35% ACN/H2O mobile phase. In an additional study by Kephart and Dasgupta [78], ZirChrom PBD and ZirChrom- CARB packed capillary columns were used to separate phenols and alkylbenzenes with maximum operating temperatures of 300◦C and 370◦C, respectively. Finally, in a study by Dai et al. [79], ODS and zirconia PBD columns were compared in the separation of cationic drugs at pHs of 3 and 6. It was noted that the selectivities varied significantly between the two columns with ionic exchange contributions to retention dominating on the zirconia columns with greater retention and increased band-spacing in comparison to silica phases. This highly notable difference in the retention mechanism prevents direct comparability between traditional silica and subcritical water zirconia methods. Moreover, the added complexity and dissimilarity between the two stationary phases limits applicability of retention models to zirconia stationary phases. A further disincentive lies within the limited scale of the literature and study respecting retention on zirconia phases versus the traditional ODS stationary phase. As its respect the current literature, alumina and titania oxide columns have not been applied in HPLC separations as often as zirconia or hydrid silica columns. Furthermore, the kinetics, thermodynamics, and stability of these columns have not been thoroughly examined in the literature. In an earlier study by Nawrocki et al. [75], they determined the chemistry of the alumina surface is more similar to zirconia than silica, which indicates that an alumina column should exhibit both ion and ligand exchange chemistry. In addition, they observed similarities between titania and zirconia oxides with regard to changes in density, surface area, and pore volume as preparative conditions were altered. Carbon-based columns (notably ‘specially porous’ graphitised-carbon stationary phases) are produced at high temperature implying excellent thermal stability, and these carbon-based columns have successfully been used in SCWC [80]. However, these columns possess a highly active surface that is easily contaminated thereby resulting in asymmetric peak shapes [64]. In addition, while

18 the stationary phase has high temperature stability, column performance still deteriorates due to mechanical stresses from differences between the thermal expansion of the stationary phase and the stainless steel column, itself [80]. Another potential drawback with SCWC results from substantial differences in observed retention versus traditional hydro-organic mobile phases. In a study by Allmon and Dorsey [81] comparing the retention mechanism between subcritical water and ambient acetonitrile and methanol mobile phases, subcritical water was found to differ significantly from acetonitrile/water mobile phases; but was somewhat similar to methanol/water mobile phases. In particular, they noted that the greater dispersive interactions of the solute with the stationary phase could account for the more exothermic, enthalpic contributions observed with SCWC in comparison to traditional hydro-organic mobile phases. In addition to this observed difference, Allmon and Dorsey further determined in a separate study [82] that the shape selectivity of subcritical water is significantly reduced as compared to hydro-organic mobile phases implying a more disorganized stationary phase. Finally, they determined significant deviations in the hydrogen-bonding network prevents useful extrapolation to ambient conditions, which therefore limits its usability as an estimator of the octanol/water partition coefficient. In general, these studies [81, 82] indicate that subcritical water provides a complementary technique to traditional hydro-organic separations as opposed to a direct alternative.

1.3 Ethanol as a Reversed Phase Organic Modiﬁer

In reversed-phase liquid chromatography, acetonitrile is generally regarded as the best mobile phase modifier due to its lower viscosity and lower UV absorbance [83]. In addition, the pressure drop across the column is generally flat over the range of acetonitrile/water mixtures. As shown by the Darcy equation (Equation 1.12) [84, 85], there is a linear relationship between viscosity (η) and the pressure drop (∆P ) across the column. In Equation 1.12, K0 represents the specific column permeability, F is the flow rate, while L and r are the respective column length and inner radius, and dp denotes the particle diameter of the stationary phase. As a result of this linear relationship, the viscosity profile for ACN/H2O mixtures is presented in Figure 1.2, which indicates that the backpressure changes are gradual as the water content increases.

19 ηFL ∆P = 0 2 2 (1.12) K πr dp While acetonitrile is commonly employed in HPLC, it is far from an ideal modifier due to its high toxicity and significant procurement costs. The most common alternative organic modifier has been the cheaper and less toxic methanol.

Figure 1.2: The experimentally determined viscosities of the binary mixture acetonitrile(1) - water(2) from reference [86].

As shown in Figure 1.3, the viscosity of methanol/water ixtures increases rapidly with increasing water content. Additionally, when compared against acetonitrile/water mixtures, the viscoscity is almost twice as large indicating similiarly greater backpressures at identical organic content. A more significant disadvantage with methanol/water mobile phases is its weaker elution strength [83]. This limitation often requires higher concentrations of methanol and/or longer run times to complete similar separations, which may not necessarily be identical owing to the different selectivities between the two modifiers.

20 Ethanol is a green solvent with limited toxicity that is miscible with light aliphatic hydrocarbons. In addition, it is a stronger modifier versus subcritcal water or methanol/water mobile phases allowing for a larger range of elution strength. There are, however, some major factors that have limited its usage in chromatography. The first one is due to the heavy regulation of ethanol, which requires purchasing denatured alcohol resulting in higher ultraviolet cut-offs and their potentially interfering solution contaminants. The second issue concerns the greater viscosity of ethanol/water mixtures versus acetonitrile/water mixtures at ambient conditions, which is presented by comparing Figure 1.2 against Figure 1.4 [86].

Figure 1.3: The experimentally determined viscosities of the binary mixture methanol(1) - water(2) from reference [86].

In the United States and many European countries, alcoholic beverages and spirits intended for consumption are heavily regulated and taxed by their respective governments. In order to avoid taxes for large-scale solvent usage of ethanol in laboratory or industrial settings, simply means that ethanol used in a laboratory or industrial setting must be denatured to ensure it is unsuitable for human consumption. To accomplish this, various denaturants are employed including the addition

21 of significant quantities of methanol or the inclusion of toxic organic compounds such as benzene for specifically denatured alcohol or heptane for completely denatured alcohol [87]. The issue of denaturants has been largely mitigated by the variety of specifically denatured alcohols that employ denaturants, which specifically avoid interfering in the designed for application such as analytical separation or spectroscopy.

Figure 1.4: The experimentally determined viscosities of the binary mixture ethanol(1) - water(2) from reference [86].

The issue of viscosity is a greater concern as large backpressures stress instrument components while applying greater mechanical force on the stationary phase. These higher backpressures have been mitigated in part by the introduction of Ultra-High Pressure Liquid Chromatographic equipment, improvements in the mechanical stability of packed stationary phases, and the development of lower backpressure stationary phases. Even so, comparing the viscosity of ethanol/water mixtures (Figure 1.4) and acetonitrile/water mixtures (Figure 1.2) indicates that the backpressures for ethanol/water mobile phases will be two to three times greater than the corresponding acetonitrile/water mobile phase. In addition to the increased wear resulting from higher pressures, the

22 greater viscosity will negatively impact analyte diffusion. From Equation 1.11 provided in Section 1.2.1, it is demonstrated that higher viscosities decrease the diffusion of analytes within the mobile phase. This decrease will then negatively impact separation efficiency by increasing plate height for longitudinal diffusion and resistance to mass transfer terms as shown in Equation 1.7 and Equation 1.8 (Section 1.1.2), respectively. There is, however, a solution to this problem. As revealed in Figure 1.4, by increasing the temperature of an ethanol/water mixture from 25◦C to 50◦C, the mixture’s viscosity is reduced by a factor of two to three. The resulting viscosity is more comparable to ambient aqueous mixtures of methanol or acetonitrile as shown in Table 1.1. Ethanol/water mixtures at this or higher temperatures will, therefore, have backpressures allowing for a wider range of flow rates without pushing pressure limits beyond the maximum pressure tolerances of the system. In addition, efficiencies should improve significantly with larger diffusion coefficients thereby decreasing the plate heights at non-optimal conditions. A secondary benefit derived from the higher temperatures is the increase in the optimal velocity of the van Deemter curve that further encourages the use of higher flow rates. Another negative factor with ethanol is the absence of adequate studies that explore the observed differences of the modifier versus traditional hydro-organic mobile phases. There are, however, a few studies [88–92] that examine ethanol/water as a mobile phase. In one such work, factorial planning was employed by Ribeiro et al. [88, 89] to examine features of asymmetry, resolution, and efficiency of ethanol/water mobile phases. From their work, asymmetries were not observed to appreciably change with mobile phase condition while resolutions generally decreased with ethanol content. In examining efficiency, they found higher temperature ethanol/water mobile phases had similar efficiencies to methanol/water mobile phases with a slight reduction in efficiency when compared to acetonitrile/water mobile phases. In a method development study for statin analysis by Assassi et al. [92], it was determined that ethanol/water mobile phases at a temperature of 40◦C were not as efficient as acetonitrile/water mixtures. Specifically, ethanol/water mixtures were found to have a lower optimal flow rate and a higher C term. In a study by Miyabe et al. [91], the adsorption characteristics of a C-18 silica-bonded column was compared at ambient temperature 70% organic fraction mobile phases for the three modifiers of ethanol, methanol, and acetonitrile. In particular, they found that the

23 Table 1.1: Viscosity comparison of several hydro-organic mixtures of ethanol, methanol, and acetonitrile at two temperatures. Values were estimated from Figures 1.2, 1.3, and 1.4, which are from reference [86]. All values are in units of micropascals.

25◦C 50◦C 100 H2O 900 550 20/80 MeOH/H2O 1550 850 20/80 ACN/H2O 900 600 20/80 EtOH/H2O 2400 1150 60/40 MeOH/H2O 1150 N/A 60/40 ACN/H2O 500 350 60/40 EtOH/H2O 1900 1000

organic modifier had no impact on intraparticle diffusion within the stationary phase and that the mechanism of surface diffusion was similar between the three mobile phases with only magnitudes deviating. Despite the limitations presented, ethanol has been successfully applied in various studies [92–95] as a green chromatographic method. As an example, in a study by Salvador et al. [94], 18 ultraviolet filters used in comestic sunscreens were successfully separated with good resolution by employing an ethanol/aqueous buffer mobile phase at 45◦C and 0.5 mL/min flow rate. Addi- tionally, in a study by de Orsi et al. [95], high organic ethanol/water mobile phases with gradient elution was successfully adapted for the development of a green, routine method for detection of phthalates in nail cosmetics. In their study, they were able to use ethanol at a flow rate of 1.0 mL/min and at a temperature of 35◦C while still obtaining statisfactory resolution and recovery. In a different study by Destandau and Lesellier [93], ambient ethanol/water was applied to the separation of pesticides and sunscreen molecules on a monolithic silica-bonded column. The use of the macroporous column was shown to allow the utilization of ethanol/water mixtures at traditional flow rates with reasonable backpressures. In comparison to traditional mobile phases, Destandau and Lesellier found that the ethanol modifier provided an elution strength 10% to 20% higher than the methanol modifier while still providing identical or better resolution and efficiency. In the previously mentioned statin analysis by Assassi et al., the limitations in efficiency were not significant enough to prevent the separation of the three statins with “appropriate values of accuracy and precision” at performances compatible with pharmaceutical methods.

24 1.4 Research Goals

Recent advances in the field of HPLC has lead to greater availability of a variety of thermally- stable stationary phases. These advances in combination with the introduction of ultra-high pressure liquid chromatographic pumps has allowed researchers to employ alternative, usually greener, mobile phases. In particular, high temperature ethanol/water combines the kinetic advantages of high temperature liquid chromatography with the ‘greenness’ of ethanol as a modifier. To date, however, ethanol has primarily been applied to various methods without a thorough examination of the differences and similarities versus retention with respect to traditional hydro-organic phases. Importantly, few studies have employed ethanol at temperatures above 50◦C or examined the impact of temperature on retention with ethanol as the modifier. In order for ethanol to become a viable alternative, a closer examination of its retention will need to be performed at temperatures higher than 50◦C. The effect of both organic content and temperature on retention will be explored over the next two chapters. Additionally, extrapolation of pure water retention with HPLC is a common indirect method for the estimation of the lipophilicity parameter known as the octanol/water partition coefficient. It has been found that methanol provides a significantly better estimation of this parameter versus acetonitrile as the organic modifier. The similarities between methanol and ethanol imply that ethanol would be similarly effective at estimating this parameter while reducing solvent usage and producing less toxic waste [96]. In Chapter 4, estimation with high temperature ethanol/water will be compared versus methanol and acetonitrile based estimations and against log P values from the literature determined via direct methods. Overall, this dissertation thoroughly explores and successfully demonstrates the benefits of high temperature ethanol/water as a green alternative for reversed phase liquid chromatography.

25 CHAPTER 2

SOLVENT STRENGTH COMPARISON

2.1 Introduction

As noted in Chapter 1, several authors have employed ethanol as an organic modiﬁer. As it concerns the literature, little work has been done to thoroughly compare and constrast it to other hydro-organic mobile phases. The similiarities in structures and dipoles between methanol and ethanol imply that ethanol would behave similiarly to methanol as an organic modiﬁer in liquid chromatography. This assumption has not been proven and is seemingly contradicted by ethanol’s ability to adsorb more strongly to C-18 stationary phases [97] as well as its higher observed elution strength versus methanol [93]. This greater eluotropic strength can be thoroughly examined using the linear solvation strength relationship.

2.1.1 Solvent Strength Parameter

The linear solvent strength theory has been shown by Snyder et al. [98] to relate the retention of an analyte to the composition of a binary aqueous-organic mobile phase in a quasi- linear function. The most commonly employed equation derived for this relationship (the Snyder- Soczewinski equation) is presented in Equation 2.1 where k′ is the retention factor and φ is the organic fraction of the mobile phase. The extrapolated intercept log k’w represents the partitioning of a solute between a C-8 or C-18 stationary phase and a pure water mobile phase. The slope of this equation, the S parameter, depends on the strength of the organic modiﬁer.

′ ′ log k = −Sφ + log(kw) (2.1)

In the Valko et al. review [99], it is noted that Equation 2.1 can provide a reasonable ﬁt over a bounded range of φ; however, wider ranges of mobile phase composition tend to generate more concave plots especially with ionic analytes. This curvature is more commonly observed with acetonitrile/water mobile phases than with methanol/water mobile phases. Valko et al. also observed that Equation 2.1 provides a good approximation for methanol/water mobile phases across

26 broader ranges of organic composition than is usually determined with acetontrile/water mobile phases. While the function allows for a reasonable prediction of retention factors, the S parameter provides a means of comparing the relative strength of organic modifiers. This parameter contributes practical information that can be used as a starting point for conversion of methods from either acetonitrile or methanol modifiers to the ethanol modifier. Taking Equation 2.1 and solving for a pure modifier results in the S parameter becoming the logarithmic ratio of pure water to pure organic as presented in Equation 2.2.

′ ′ S = log(kw) − log(korg) (2.2)

Taking the relationship between equilibrium and retention as exhibited in Equation 1.2 from Section 1.1, the equilibrium can be substituted by the Gibbs free energy (∆G) to form Equation 2.3. In Equation 2.3, the Φ parameter is the phase ratio, which is the ratio of stationary phase volume to mobile phase volume within the column. RT represents as the gas constant times by the absolute temperature in Kelvin.

′ log(k )= log(Φ) − ∆G/2.3RT (2.3)

Equation 2.3 can be combined with Equation 2.2 for the pure water and pure organic conditions resulting in Equation 2.4. Equation 2.4 demonstrates that the solvent strength parameter is a free energy term for the transfer process of an analyte between the stationary and mobile phases. In particular, this term represents the relative difference between the transfer free energies for a pure aqueous mobile phase and that of a pure organic mobile phase. When comparing two modifiers, a larger S parameter means that the eluotropic strength of the first modifier is greater than the second. Thus, a lower volume percent of the first modifier is required to generate a similar degree of eluting power as that observed with the second modifier.

S = (∆Gorg − ∆Gw)/(2.3RT ) (2.4)

In the review by Valko et al., it was observed that the S parameter is independent of both the stationary phase structure and the bonded phase concentration [99]. This determination

27 supports the above theoretical understanding of the term being dependent only on the mobile phase component. However, Valko et al. [99] did observe a decrease in the S parameter with increasing stationary phase polarity. For example, significant differences were observed in the S parameter between a C-18 stationary phase and a cyano-bonded phase. This observation does not contradict the aforementioned theoretical understanding. A decrease in the polarity difference between the mobile phase and stationary phase would reduce the impact on retention of the polarity differences between the organic modifier and water. Thus, the difference in retention between a pure water mobile phase and a pure organic mobile phase would be reduced with a more polar stationary phase. Finally, Valko et al.[99] determined that S is largely dependent upon the analyte’s properties, specifically its hydrophobicity, molecular size, and proton donating sites. With respect to temperature, conflicting results have been found regarding the independence of the S parameter. Valko et al. [99] have observed that an increase in temperatures produces a small decrease in the S parameter. Unfortunately, they were unable to determine its significance or any obvious trends. In a multipart study by Zhu, Dolan, and Snyder [100], the S parameter was determined to not vary with respect to temperature for a variety of neutral analytes including homologous nitroalkanes and various neutral drugs. Furthermore, they measured an average ratio of high to low temperature S values to be 0.98 ± 0.06 using primarily acetonitrile as the organic modifier with a temperature interval of 20◦C to 40◦C. As temperature is a factor in this work, its impact on elution strength will need to be examined in detail. A linear relationship between retention and temperature, as shown in Equation 2.5, was proposed by Kiridena, Poole, and Koziol [101] at temperatures of less than 65◦C. With

Equation 2.5, a1 represents a temperature-based solvent strength parameter and the intercept (a0) represents a purely hypothetical log(k′) value at zero degrees Celsius. In addition to working with mobile phases at moderate temperatures, this equation has been successfully applied by Allmon and Dorsey [82] for subcritical water mobile phases.

′ log k = a1T + a0 (2.5)

2.1.2 Current Work

For the examination of solvent strength, Equation 2.1 and Equation 2.5 will be used to compare the solvent strength relationships for ethanol/water mobile phases against the solvent

28 strengths observed for acetonitrile/water, methanol/water, and subcritical water mobile phases. As part of this study, the impact of temperature on the solvent strength relationship will be explored for ethanol/water mobile phases.

2.2 Experimental 2.2.1 Reagents

Methanol (HPLC grade), acetonitrile (HPLC grade), and ethanol (absolute, 200 proof) were obtained from Sigma-Aldrich Chemical Company (St. Louis, MO, USA). Water used in this study was purified using a Barnstead (Debuque, Iowa, USA) Nanopure II purification system at an approximate resistance of 17 MΩ/cm and subsequently filtered through 0.45µm filter paper with vacuum filtration. Each mobile phase was degassed through vigorous helium sparging. For the initial study on solvent strength, a toluene sample was used with acetone as the void marker. For the second phase of this study, the analytes shown in Table 2.1 were employed with acetone as the void marker. All analytes were obtained from Sigma-Aldrich Chemical Company.

Table 2.1: The analytes used for solvent strength examination.

Toluene Bromobenzene Acetophenone Benzophenone p-Chlorophenol p-Nitrotoluene

2.2.2 Equipment

A Sigma-Aldrich (St. Louis, MO, USA) Supelco Discovery Zirconia-Carbon column, 50 mm x 4.6 mm with 5 µm stationary phase particles, was utilized for the ﬁrst phase of this study. According to the manufacturer, this column has a monomeric, non-endcapped stationary phase with a pore size of 300 A,˚ a surface area of 30 m2/g, and a carbon load of 1%. The chromatographic system used with the Zirconia column was a Shimadzu (Kyoto, Kyoto Prefecture, Japan) LC-10 system consisting of an SCL-10Avp controller, an SPD-10Avp UV-Vis detector, an SIL-10A autoinjector, and an LC-10ADvp pump. A Metalox 200-C High Temperature Column Oven (Anoka, MN, USA) was used to control the temperature of the column. A backpressure regulator was inserted between the oven and detector to prevent the boiling of the mobile phase.

29 An Agilent (Santa Clara, CA, USA) ZORBAX Stablebond C-18 column, 50 mm x 4.6 mm with 5 µm stationary phase particles, was used for the remaining aspects of this study. According to the manufacturer, this column has a monomeric, non-endcapped stationary phase with a pore size of 80 A,˚ a surface area of 180 m2/g, and a carbon load of 10%. The chromatographic system employed in this work consisted of a Waters (Milford, MA, USA) Model 501 HPLC pump, a Kratos Analytical (Manchester, UK) Spectroﬂow 757 UV-Vis Detector, a Sigma-Aldrich (St. Louis, MO, USA) Rheodyne Model 7125 6-port injection valve, and a Perkin Elmer (Waltham, MA, USA) Nelson 950A interface with TotalChrom Workstation v. 6.2.1 software. A glass column jacket and a Fisher Scientiﬁc (Waltham, MA, USA) Isotemp 9105 circulating water bath was used to maintain a constant column temperature.

2.2.3 Procedures

For the initial phase of this study, a toluene solution was injected onto a zirconia carbon column in triplicate with a flow rate of 1.00 mL/min, a detection wavelength of 254 nm, and an injection volume of 10 µL. Toluene was run with four different mobile phases: (1) methanol/water, (2) ethanol/water, (3) acetonitrile/water, and (4) subcritical water. For acetonitrile and methanol modifiers, the temperature was held at 30◦C and at least five percentages were chosen between 16.7% to 61.5% for each modifier. For subcritical water, seven temperatures were chosen between from 100◦C to 160◦C. For ethanol, the four percentages of 10%, 20%, 35%, and 50% were examined at six to nine temperatures ranging between 30◦C to 160◦C. For the second phase of this study, analytes in Table 2.1 were injected onto a Stablebond C-18 column in triplicate with a flow rate of 1.00 mL/min, a detection wavelength of 254 nm, and an injection volume of 20 µL. Each analyte was run with multiple EtOH/H2O mixtures ranging from 30% to 60% ethanol at multiple temperatures ranging from 50◦C to 80◦C. All data regressions were performed using the LINEST function from Microsoft Office 2007 Excel.

2.3 Results and Discussion 2.3.1 Solvent Strength Comparison

For the ﬁrst phase of this study, toluene was used to compare the relative solvent strengths for acetonitrile, methanol, ethanol, and subcritical water conditions on a zirconia carbon column.

30 The use of a zirconia column allows for both higher temperatures and a broader temperature range than is generally possible with silica stationary phases.

Figure 2.1: Comparison of (a) the thermal solvent strength curve for the 10% EtOH/H2O mobile phase and (b) the solvent strength curve for ethanol/water mobile phases at a temperature of 50◦C.

The ﬁrst feature explored was a comparison of the relative impact on elution strength between changing temperatures and changing mobile phase composition. As Figure 2.1(a) and Figure 2.1(b) demonstrate, changing ethanol content has a greater impact on the eluting power

31 Table 2.2: Comparison of the Organic and Thermal Solvent Strength Parameters for the Ethanol Modiﬁer.

Percent Ethanol 0.0 10.0 20.0 35.0 50.0 -a1 0.01076 0.01057 0.0103 0.0090 0.0080 Uncertainty 0.00007 0.00008 0.0003 0.0002 0.0002 R2 0.9997 0.9996 0.9953 0.9958 0.9965

Temperature (◦C) 30.0 50.0 70.0 90.0 100.0 S 0.034 0.0312 0.0304 0.0286 0.0277 Uncertainty 0.002 0.0003 0.0006 0.0007 0.0008 R2 0.9970 0.9999 0.9993 0.9987 0.9983

of the mobile phase with respect to the toluene analyte. A comparison of the slopes from these two curves reveals that each percentage increase in ethanol content has three times the eﬀect on retention than is observed with a degree Celsius increase in temperature.

Examining the solvent strength parameters S and a1 as more broadly presented in Table 2.2, it was observed that increases in elution strength through either more ethanol or higher temperatures reduced the sensitivity of toluene retention with respect to the other system parameter. Temperature, however, remains a much weaker mobile phase parameter compared to ethanol content even with highly aqueous mobile phases. In practical terms, temperature must be increased by at least 30◦C for the elution strength of the mobile phase to reach the strength that was observed with a 10% increase in ethanol content. This observation combined with the limitations of temperature programming in liquid chromatography, which is explained in Section 1.2.1, demonstrates that temperature programming is of limited utility in implementing gradient elution in liquid chromatography. Table 2.2 further demonstrates that the S parameter changes with temperature, which contradicts what was observed with acetonitrile/water mobile phases in the Zhu et al. study [100]. Taking the ratio of the S parameter between two temperatures (eg 50◦C and 90◦C) for ethanol/water mobile phases results in a value of 0.9189 ± 0.025, which is signiﬁcantly less than one. In the Zhu study, they observed an average of 0.98 ± 0.06 for ratios of a variety of analytes with temperature intervals of 20◦C to 40◦C. In addition to this deviation, there is a small but observable linear trend in the S parameter with respect to temperature as shown in Figure 2.2.

32 This work also examined the free energy term (R∗T ∗S) for the solvent strength parameter S, which was introduced in Equation 2.4. While the S parameter decreases with increasing temperature, the free energy term for S presented in Table 2.3 indicates an absence of a significant change with respect to temperature. Assuming the free energy term is truly constant with temperature, then the relative difference in the Gibbs free transfer energies for pure water and pure ethanol does not decrease in magnitude with the decreases in the Gibbs free energy for either pure water or pure ethanol at higher temperatures. Based on the Gibbs free energy equation, a constant ∆GS as shown in Equation 2.6 indicates that the enthalpy must also remain unchanged with respect to changing temperature. Entropy, however, must either vary with temperature or the difference between the entropies of transfer for pure ethanol and pure water must be either similar or small enough to result in insignificant changes with the ∆GS term.

∆GS1−∆GS2 = (∆Horg1−∆Horg2−∆Hw1+∆Hw2)−(T1∆Sorg1−T2∆Sorg2−T1∆Sw1+T2∆Sw2) = 0 (2.6) Figure 2.3 directly compares the elution strength of ethanol/water mobile phases at varying temperatures against that observed with acetonitrile and methanol modiﬁers at 30◦C. In particular, Table 2.3 illustrates that the S parameters for the ethanol modiﬁer are closer to acetonitrile implying that there is a similar impact on elution strength with respect to the changing organic content. In practical terms, this means that acetonitrile can be reasonably replaced with ethanol without requiring larger and/or faster organic gradient intervals with gradient elution separations. In addition, the increase in temperature provides more similar gradients.

Table 2.3: Comparison of the Organic Solvent Strength Parameters for various modiﬁers.

Mobile Phase ACN MeOH EtOH EtOH EtOH EtOH EtOH Temp. (◦C) 30.0 30.0 30.0 50.0 70.0 90.0 100.0 Percent (%) 16.7-60.0 16.7-61.5 20.0-50.0 10.0-50.0 10.0-50.0 10.0-50.0 10.0-50.0 S (%−1) 0.0328 0.0271 0.034 0.0312 0.0304 0.0286 0.0277 δS (%−1) 0.0004 0.0002 0.002 0.0003 0.0006 0.0007 0.0008 R2 0.9993 0.9968 0.9970 0.9999 0.9993 0.9987 0.9983 RTS (J/mol) 82.6 68.3 85.7 83.8 86.7 86.3 83.7 δRTS (J/mol) 1.0 0.5 5.0 0.8 1.7 2.1 2.6

33 As Figure 2.3 also presents, adjusting both temperature and percent ethanol allows for a broad range of eluting power without exceeding 50% organic content. The stronger eluting power of ethanol at elevated temperatures indicates that separations performed using methanol/water can be utilized with ethanol/water mobile phases at a signiﬁcantly lower organic content. Furthermore, the increase in temperature from 50◦C to 70◦C results in ethanol mobile phases going from slightly weaker than ambient acetonitrile mobile phases to moderately stronger for identical mobile phase content. More speciﬁcally, this increase in strength results in ethanol/water mobile phases having an elution strength at organic contents of approximately 3% to 6% higher than those observed with acetonitrile mobile phases.

Figure 2.2: This curve relates the solvent strength parameter (S) of toluene to the temperature (T ) of ethanol/water mobile phases.

Another factor of this substitution is the change in maximum pressure observed during a gradient run. This maximum pressure is directly related to the viscosity of the mobile phase mixture. As presented in Section 1.3, the greatest viscosity observed for acetonitrile/water mixtures at 25◦C was approximately 1000 micropascals. In comparison, ethanol/water mixtures at 50◦C were found to have a maximum viscosity of only 1200 micropascals. Though comparable, the pressure would be higher with a gradient run using ethanol as the modiﬁer. By increasing the temperature to 70◦C, the two modiﬁers not only become very similar in eluting strength, they also have less

34 Figure 2.3: Comparison of the effect of organic content on the retention of toluene for ethanol/water mobile phases at several temperatures, acetonitrile/water mobile phases at 30◦C, and methanol/water mobile phases at 30◦C. Included for reference is the retention of toluene for subcritical water mobile phases at 100◦C and 160◦C. dissimilar maximum pressures during their gradients. Thus, these similarities in eluting power, solvent strength parameters, and viscosities between moderately high temperature ethanol/water and ambient acetonitrile/water; thereby imply that 50◦C to 70◦C ethanol/water can directly replace acetonitrile/water mobile phases without requiring significant adjustments in organic content or composition gradient rates. One final observation from Figure 2.3 is the comparatively weak elution strength observed with subcritical water mobile phases in which a range of 100◦C to 160◦C corresponds to the relatively weak mobile phases of 17% to 38% acetonitrile/water and 37% to 55% methanol/water. This observation further demonstrates that subcritical water mobile phases are not a suitable replacement for hydro-organic mobile phases in separations of low polarity molecules due to the extensive run times that results from low eluting strength mobile phases.

35 In order to explore this observation in more detail, the enthalpies of transfer for toluene were calculated for subcritical water and for each percent of ethanol studied (Table 2.4). These enthalpies indicate that the partition mechanism of toluene is exothermic and thereby becomes less energetically favorable with increasing ethanol content. As with the solvent strength parameters, there is a large difference between the 50%, 35%, and 20% EtOH/H2O mobile phases with a negligible difference between 10% and 20% EtOH/H2O mobile phases. There is, however, a notable difference between the enthalpic contributions of subcritical water and 10% EtOH/H2O unlike what was observed with the solvent strength parameter. This difference is most likely the result of increased adsorption of ethanol and water to the stationary phase resulting in reduced dispersive interactions between toluene and the zirconia carbon stationary phase compared to subcritical water. This conclusion is supported by similar results observed by Allmon and Dorsey between subcritical water and methanol/water mobile phases [81, 82].

Table 2.4: Comparison of the Enthalpy of Transfer for Toluene between Subcritical Water and Ethanol/Water mobile phases.

Percent Ethanol (%) 0.0 10.0 20.0 35.0 50.0 ◦ ∆H Toluene (kJ/mol) -33.2 -27.2 -26.8 -23.1 -18.1

2.3.2 Temperature Dependence of the S Parameter

In order to more closely examine changes in the S parameter with temperature, multiple analytes were run across a large number of conditions on a silica C-18 column. The resulting S parameters and corresponding R2 values are presented in Table 2.5. Importantly, all of the R2 values were 0.99 or greater indicating linearity over the composition range studied. By comparing sequential temperatures in Table 2.5, it would appear that the S parameters do not diﬀer significantly. However, across the full but relatively smaller temperature range studied with the silica C-18 column, a signiﬁcant decreasing trend is still observed with the S parameter versus temperature. This trend is more clearly exhibited in Figure 2.4 and Figure 2.5, which presents the changes in S with temperature for the analytes studied.

36 Table 2.5: Solvent strength parameters for toluene, bromobenzene, acetophenone, benzophenone, para-chlorophenol, and para-nitrotoluene with ethanol/water mobile phases at eight temperatures between 50◦C and 80◦C along with the R2 values from the Snyder- Soczewinski equation.

Toluene Bromobenzene Acetophenone S Parameter R2 S Parameter R2 S Parameter R2 50.0◦C -0.0329 ± 0.0004 0.9990 -0.0287 ± 0.0004 0.9987 -0.0297 ± 0.0008 0.9970 55.0◦C -0.0317 ± 0.0007 0.9980 -0.0279 ± 0.0007 0.9971 -0.0292 ± 0.0008 0.9980 60.0◦C -0.0311 ± 0.0006 0.9979 -0.0271 ± 0.0006 0.9974 -0.0288 ± 0.0007 0.9974 64.0◦C -0.0306 ± 0.0007 0.9977 -0.0264 ± 0.0008 0.9962 -0.0281 ± 0.0010 0.9962 67.0◦C -0.0300 ± 0.0007 0.9965 -0.0260 ± 0.0006 0.9966 -0.0274 ± 0.0010 0.9945 70.0◦C -0.0298 ± 0.0007 0.9969 -0.0259 ± 0.0006 0.9964 -0.0273 ± 0.0011 0.9937 75.0◦C -0.0292 ± 0.0008 0.9966 -0.0255 ± 0.0007 0.9957 -0.0271 ± 0.0011 0.9933 80.0◦C -0.0289 ± 0.0007 0.9968 -0.0252 ± 0.0006 0.9963 -0.0262 ± 0.0010 0.9942 Benzophenone p-ChloroPhenol p-Nitrotoluene S Parameter R2 S Parameter R2 S Parameter R2 50.0◦C -0.0428 ± 0.0018 0.9921 -0.0336 ± 0.0005 0.9988 -0.0329 ± 0.0008 0.9973 55.0◦C -0.0411 ± 0.0020 0.9932 -0.0321 ± 0.0007 0.9980 -0.0321 ± 0.0009 0.9971 60.0◦C -0.0422 ± 0.0019 0.9920 -0.0317 ± 0.0006 0.9979 -0.0311 ± 0.0009 0.9958 64.0◦C -0.0412 ± 0.0022 0.9914 -0.0309 ± 0.0007 0.9979 -0.0306 ± 0.0010 0.9961 67.0◦C -0.0400 ± 0.0019 0.9907 -0.0301 ± 0.0007 0.9971 -0.0298 ± 0.0009 0.9954 70.0◦C -0.0396 ± 0.0020 0.9896 -0.0303 ± 0.0010 0.9957 -0.0303 ± 0.0012 0.9941 75.0◦C -0.0392 ± 0.0021 0.9892 -0.0297 ± 0.0009 0.9960 -0.0295 ± 0.0010 0.9955 80.0◦C -0.0381 ± 0.0018 0.9912 -0.0291 ± 0.0010 0.9955 -0.0290 ± 0.0010 0.9957

These results support the decreasing trend observed with toluene on the zirconia carbon column. Taken together, both of these studies provide strong evidence that the S parameter is a temperature dependent value for a variety of analytes, which is contrary to what is observed in the literature [100]. One possible explanation for this contradiction is the diﬀerence in organic modiﬁer being employed. The change in the S parameter is small with respect to changing temperature for ethanol/water mobile phases. If the partitioning process for acetonitrile/water mobile phases is less sensitive to changing temperature compared to ethanol/water mobile phases, then changes in the S parameter for acetonitrile/water mobile phases would not be observable greater than its uncertainty. Examining the relationship between S and temperature for toluene in Figure 2.4(a) seems to support the linear trend shown in Figure 2.2 for toluene on the zirconia carbon column. However,

37 Figure 2.4: The solvent parameter S versus temperature for (a) toluene, (b) bromobenzene, and (c) acetophenone.

38 Figure 2.5: The solvent parameter S versus temperature for (a) benzophenone, (b) para- chlorophenol, and (c) para-nitrotoluene.

39 the curves for the other five analytes (Figures 2.4(b), 2.4(c), and 2.5) present a less definitive linear relationship. While it is clear that the S parameter decreases with temperature, its linearity cannot be definitely concluded. This outcome is in part due to the uncertainty in the S parameters being nearly as large or larger than the observed change in S between each temperature studied. In addition, the thermal limits of the Stablebond C-18 column prevented studying a broader temperature range to mitigate this issue. As a result, it cannot be unequivocally stated that the S parameter changes linearly with temperature for all analytes.

One final detail examined with the silica C-18 study is the impact of temperature on ∆GS. The results presented in Table 2.6 are the RTS values based on the S parameters from Table 2.5. In general, Table 2.6 supports the conclusions evidenced by toluene on the zirconia carbon column, in that; RTS values do not deviate significantly with temperature. While there is a small deviation between the highest and lowest temperatures for some of the analytes, the RTS ratio of the 80◦C to 50◦C represented in the last row of Table 2.6 reveals this difference to be marginal with a near unity ratio.

2.4 Conclusions

In this chapter, the solvent strength of ethanol/water mobile phases was examined and subsequently compared to traditional hydro-organic mobile phases. Initially, the solvent strength of various mobile phases was examined on a zirconia carbon column. From a comparison of solvent strength curves for ethanol content and system temperature, retention was observed to be several times more sensitive per percent increase in ethanol content than per degree Celsius increase in temperature. The weaker impact on retention from temperature indicates that this parameter is of limited utility in implementing large adjustments to solvent strength. Therefore, even if temperature programming can be feasibly implemented with RPLC, the changes in temperature would need to be three times larger to afford the same solvent strength range provided by ethanol content gradients. The work within this chapter also demonstrated that the sensitivity of retention (represented by the S parameter) to ethanol content decreased with higher temperatures. Such changes in sensitivity have not been observed to be significant for acetonitrile/water mobile phases by other authors. This deviation indicates that the modifiers have different thermodynamic profiles, which

40 Table 2.6: The free energy term for solvent strength for toluene, bromobenzene, acetophenone, benzophenone, para-chlorophenol, and para-nitrotoluene with ethanol/water mobile phases at eight temperatures between 50◦C and 80◦C. All RTS values are in units of J/mol. 2.1 2.4 2.5 2.7 2.6 3.3 2.9 2.8 RTS 0.04 δ 88.5 87.5 86.2 85.7 84.3 86.5 85.5 85.2 0.96 p-Nitrotoluene RTS 1.4 2.0 1.8 2.0 2.0 2.8 2.7 2.9 RTS 0.04 δ 90.2 87.6 87.7 86.7 85.2 86.5 85.9 85.5 0.95 p-Chlorophenol RTS 4.9 5.5 5.3 6.2 5.5 5.8 5.9 5.3 RTS 0.06 δ 0.97 RTS Benzophenone 115.1 112.2 116.8 115.5 113.1 113.1 113.5 111.8 2.2 2.1 2.1 2.8 2.9 3.1 3.2 2.9 RTS 0.05 δ 79.7 79.8 79.6 78.7 77.4 77.9 78.5 76.9 0.99 Acetophenone RTS 1.1 2.0 1.6 2.3 1.7 1.8 2.2 1.8 RTS 0.03 δ 77.1 76.0 75.2 74.0 73.5 73.8 73.9 74.0 0.97 Bromobenzene RTS 1.1 2.0 1.6 1.9 2.1 1.9 2.2 2.0 RTS 0.03 δ Toluene 88.4 86.5 86.3 85.9 84.9 85.1 84.5 84.8 0.96 RTS C C C C C C C C ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ 50.0 55.0 60.0 64.0 67.0 70.0 75.0 80.0 RTS Ratio

41 complicates direct substitution by necessitating additional method development. This divergence between the modifiers, however, presents the possibility that the sensitivity of retention to ethanol content can be adjusted by altering the temperature of the system. In addition, the free energy term for the solvent strength of ethanol mobile phases was determined not to change significantly with temperature. This implies that the relative difference for the Gibbs free energy of transfer between pure water and pure ethanol mobile phases are not affected by temperature and thus the enthalpies of transfer for toluene with ethanol/water mobile phases are temperature independent. To confirm these results, similar examinations were performed with a Stablebond silica C-18 column using a broader analyte set. From these examinations, similar patterns were observed; however, a linear relationship between the S parameter and temperature could not be definitely confirmed due to the restrictions by the smaller temperature range of the Stablebond column. For the final component of this chapter, the eluting power of various mobile phases was compared and contrasted against each other. As part of this comparison, the green alternative mobile phase of subcritical water was demonstrated to be the weakest eluent studied. More specifically, ◦ a high temperature of 160 C corresponded approximately to the relatively weak 36% ACN/H2O mobile phase. On the other hand, ethanol/water mobile phases at temperatures of 50◦C to 120◦C exhibited a greater range of elutrophic strength. In particular, this range was stronger than 60%

ACN/H2O mobile phases without exceeding 50% in ethanol content. In addition, even though methanol and ethanol are similar alcohols, methanol was validated to be a significantly weaker modifier versus ethanol and acetonitrile. In conclusion, these comparisons establish that high temperature ethanol/water mobile phases provide similar or greater eluting power versus ambient acetonitrile/water while producing lower quantities of less toxic organic waste. Therefore, the benefits of high temperature ethanol modifiers comprise both its high solvent strength and its limited toxicity, which becomes important when considering a mobile phase for Green Chromatography.

42 CHAPTER 3

CHARACTERIZATION OF THE RETENTION MECHANISM IN HIGH TEMPERATURE ETHANOL/WATER MOBILE PHASES FOR REVERSED PHASE HIGH PERFORMANCE LIQUID CHROMATOGRAPHY

3.1 Introduction

As noted in Chapter 1, ethanol/water mobile phases are a rarely used organic modifier. As a result, differences to the retention mechanism versus ambient hydro-organic mobile phases in RPLC are unknown. In addition, Chapter 2 has demonstrated that the impact of high temperature on ethanol/water mobile phases differs from what is traditionally observed with hydro-organic mobile phases. Thus, the impact resulting from these changes on retention from temperature were also explored in this dissertation. Various analysis methods have been used to study retention. Most common among these methods are linear solvation energy relationships and van’t Hoff analyses, which were employed in this work to compare traditional hydro-organic mobile phases with high temperature ethanol/water mobile phases. The primary purpose of these analyses is the determination of the viability and applicability of substituting traditional hydro-organic mobile phases with the greener ethanol/water mobile phases.

3.1.1 van’t Hoﬀ Analysis

In chromatography, a van’t Hoﬀ analysis is applied to examine the thermodynamics of a stationary phase/mobile phase system. It focuses on the relationship between temperature and the change in equilibrium of a solute between the two phases. In Equation 3.1, the parameters are k’, the retention factor of a solute; ∆H◦, the standard partial molar enthalpy of transfer; ∆S◦, the standard partial molar entropy of transfer; T, the temperature of the system; and R, the gas constant.

43 ◦ ◦ ′ −∆H ∆S ln(k )= + + ln(Φ) (3.1) RT R The ﬁnal parameter of Φ is a consequence of the relationship between the equilibrium constant (K ) and the retention factor k’. The retention factor is related to the volume phase ratio as described in Equation 3.2.

′ Vs k =K = KΦ (3.2) Vm

Since Φ is the ratio of the stationary phase volume (Vs) to the mobile phase volume (Vm), this parameter becomes known as the phase ratio. This equation, however, will only produce a linear plot if there are no changes in enthalpy, entropy, and/or the phase ratio term with respect to temperature. The parameter known as the retention factor can also be deﬁned in terms of time as depicted in Equation 3.3 where t is a solute’s retention time and t0 is the void time of the system.

′ t − t0 k = r (3.3) t0 While the retention factor is easily calculated, the phase ratio term cannot simply be determined due to the complexity in measurement of the stationary phase volume. It can be reasonably assumed, however, that the phase ratio does not change significantly between similar compounds over a modest temperature range. Therefore, the difference between the natural logarithms of the retention factor between adjacent members in a series of compounds (e.g. toluene and ethylbenzene) will thereby remove the phase ratio term from the van’t Hoff equation. This difference or separation factor (α) is also known as the selectivity. The thermodynamics based on the selectivity can be determined from the line of a plot of ln(α) versus inverse temperature as provided in Equation 3.4 [102].

◦ ◦ ◦ ◦ −(∆H +1 − ∆H ) (∆S +1 − ∆S ) ln(α)= i i + i i (3.4) RT R In RPLC, the hydrophobic model has been considered the driving force for analyte retention; but several studies have contended that this explanation is insuﬃcient for all RPLC systems. In a study by Cole, Dorsey, and Dill [103], data was obtained that conﬁrmed the expected positive values for entropy for benzene when using a 95% 1-propanol/water mobile phase. However, entropies

44 with the 60% ACN/H2O mobile phase were determined to be negative [103]. Moreover, the 60%

ACN/H2O system did not exhibit the slope change in its van’t Hoﬀ plot that is indicative of the hydrophobic eﬀect. In a study conducted by Ranatunga and Carr [104], the enthalpy of retention was observed to be dominated by contributions from the stationary phase more than from the mobile phase when using 40% to 100% of either acetonitrile or methanol. In addition, it was concluded that entropy was not a dominant contributor to the retention process thus contradicting the hydrophobic model of retention.

3.1.2 Linear Solvation Energy Relationships

The LSER model is a particular subset of thermodynamic relationships known as linear free energy relationships [105]. The LSER model correlates the solute partitioning measure (SP) to the speciﬁc intermolecular interactions of solute molecules with two phases in a linear relationship. By conducting LSER studies, the interactions that govern the partitioning process can be examined and compared between systems. Equation 3.5 for the LSER model is presented by Abraham, and it is currently the most accepted, symbolic representation of LSERs [106]. The LSER model has been thoroughly detailed in a review by Vitha and Carr [105].

SP = c + eE + sS + aA + bB + vV (3.5)

Historically, the LSER model was developed to study the interaction ability of bulk solvents. Over time, it evolved to be applied to other complex chemical systems including as a means to examine solute behavior in various partitioning systems. For chromatographic systems, the measure of solute partitioning between the stationary and mobile phases is log k’. The remaining terms in Equation 3.5 represent a variety of chemical interactions between the solute and solvents. The capital letters of Equation 3.5 are solute input parameters representing the various properties of the solutes. The S parameter corresponds to an analyte’s polarizability and dipolarity, relative to the dipolar and polarizability interaction of cyclohexane. Parameter E represents the excess polarizability that is not included in the S parameter, primarily due to the presence of n and π electrons. Parameters A and B represent the hydrogen-bond acidity and hydrogen-bond basicity of the analyte, respectively. The last notable parameter, V, is based on the size and shape of the analyte.

45 Overall, the vV term accounts for the unfavorable (endoergic) process of formation of a cavity with sufficient size and shape to solvate an analyte within the stationary or mobile phase. More specifically, vV is a measure of the ease of cavity formation by the stationary phase versus the mobile phase. Included in this term are influences from dispersive interactions between the solute and each solvating phase. Finally, the LSER model contains a constant or intercept term c, which is independent of the solute. Each of the lower case letters represents the corresponding coefficients and reflects the differences between the stationary and mobile phases. Collectively, these coefficients are called system constants, and are a measure of the type and extent of interactions occurring in the chromatography column. More specifically, their magnitudes reflect the degree of difference in the solute interactions between the stationary and mobile phases, and thus the extent to which each interaction contributes to the retention of the solute. Polarizability is the capability of a molecule to interact with its neighbor molecules when there is an instantaneous disruption in the charge distribution of the molecule that results in a temporary dipole in that molecule and adjacent molecules. The polarizability has dimensions of volume and is proportional to the molecular size, which means that polarizability should linearly correlate with molecular volume in a homologous series [105]. The polarizability of a molecule can be calculated using the Equation 3.6 where MRs is the molar refraction of a solute and Vs is the McGowan molecular volume [106]. In addition, the McGowan volume is used directly as the size parameter V , though it is generally divided by 100 to bring the magnitude closer to that of the other solute parameters.

E = MRs − 2.83195Vs + 0.52553 (3.6)

The McGowan volume is determined by an additivity scheme as presented in Equation 3.7

[107]. More speciﬁcally, VS is the volume (in milliliters) of a mole of molecules that are not in motion. This volume is itself based on the summation of the McGowan atomic volumes Va reduced by a volume term based on the number of bonds (NB).

Vs = Σ(Va) − Σ(6.56NB) (3.7)

46 Each McGowan atomic volume is determined by a proportional relationship to the parachor

(Vp), which is represented by Equation 3.8. The parachor is deﬁned as the molecular volume of a substance when the surface tensions are equal. Parameters g and d1 represent the surface tension and density of the substance while the parameter dg represents the density of the element’s vapor at equilibrium with its liquid form.

∗ 1/4 Vp =(M g )/(d1 − dg) (3.8)

The dipolarity S parameter is based on the intermolecular interaction of the positive or negative end of a molecular dipole and the negative or positive end of another molecular dipole or induced molecular dipole. The solute parameter S is currently found in the same manner as calculated by the solvent parameter π*, which is calculated using Equation 3.9 where υ is the frequency of maximum absorption for a particular indicator dye in a specific solvent and is equal to υ0 + sπ∗ where υ0 is the frequency of maximum absorption in a solvent whose π* is defined as 0.000 (cyclohexane) and s is the solute dependent shift in υ computed by defining a second solvent as having π* equal to 1.000 (DMSO) [105].

∗ − π = ν υcyclohexane (3.9) υDMSO−υcyclohexane

The final two input parameters are focused on hydrogen-bonding interactions. The hydrogen- bond acidity and basicity parameters are measures of the hydrogen-bond donating and hydrogen- bond accepting ability of a solute, respectively. As these are measures of hydrogen-bonding, the solute must have atoms that can participate in hydrogen-bonding to have a value other than zero. Atoms that can participate in hydrogen-bonding are oxygen, nitrogen, and fluorine. The strong intermolecular force of hydrogen-bonding involves the attraction of the lone pair of a strongly electronegative atom to the partial positive charge of hydrogen that is covalently bonded to a strongly electronegative atom. These parameters are generally determined through a lengthy back- calculation wherein a correlation is established with simple solutes that contain only one hydrogen- bonding site to determine the coefficients of the fit. For those solutes with multiple sites, these parameters are then established through back-calculation to fit LSERs from various, well-defined systems [105].

47 For the calculation of the system coefficients, log k’ for a series of varying analytes are measured and a multi-parameter linear least-squares fit is performed, using Equation 3.5 as the model [105]. The values of these coefficients will reflect the differences in the properties between the stationary and mobile phases. The magnitude of each coefficient is indicative of the degree of a particular term’s difference between the phases. The signs of the coefficients determine whether there are favorable or unfavorable interactions of that input parameter with respect to retention for that solute. A positive result indicates that the represented chemical interactions are more favorable for the stationary phase while negative results favor the mobile phase. In addition, the polarizability, dipolarity, and hydrogen-bond acidity/basicity are favorable interactions, which means the interactions are stronger between the solutes and the stationary phase (if positive) or mobile phase (if negative). The volume term, however, is representative of the unfavorable process of cavity formation. A positive sign would therefore indicate that the cavity formation within the stationary phase is less unfavorable than cavity formation within the mobile phase.

3.1.3 Current Work

As previously noted, ethanol/water mobile phases have not been studied to determine how its retention mechanism diﬀers with traditional hydro-organic mobile phases. In order to character- ize this mechanism, the thermodynamics of methylene transfer are compared between traditional phases and high temperature ethanol/water by van’t Hoﬀ analyses using alkylbenzenes. In addition, the intermolecular forces involved in retention are examined and compared using linear solvation energy relationships.

3.2 Experimental 3.2.1 Reagents

Methanol (HPLC grade), acetonitrile (HPLC grade), and ethanol (absolute, 200 proof) were obtained from Sigma-Aldrich Chemical Company (St. Louis, MO, USA). Water used in this study was purified using a Barnstead (Debuque, Iowa, USA) Nanopure II purification system at an approximate resistance of 17 MΩ/cm and subsequently filtered through 0.45 µm filter paper with vacuum filtration. Each mobile phase was degassed through vigorous helium sparging. For the van’t Hoff phase of this study, the alkylbenzene series of benzene, toluene, ethylbenzene, propylbenzene, and butylbenzene were used with acetone as the void marker.

48 For the linear solvation energy relationship phase of this study, the analytes shown in Table 3.1 were employed with acetone as the void marker. Included in Table 3.1 are their LSER solute- dependent values [108, 109]. All analytes were obtained from Sigma-Aldrich Chemical Company.

Table 3.1: The analytes used for the linear solvation energy relationship analyses.

Analytes V S A B E Benzene 0.7176 0.511 0 0.144 0.608 Toluene 0.8573 0.499 0 0.139 0.606 Ethylbenzene 0.9982 0.499 0 0.139 0.613 p-Xylene 0.9982 0.494 0 0.16 0.615 Propylbenzene 1.1391 0.502 0 0.134 0.61 Butylbenzene 1.28 0.499 0 0.139 0.595 p-Dichlorobenzene 0.9612 0.75 0 0.02 0.825 Bromobenzene 0.8914 0.723 0 0.089 0.882 Anisole 0.916 0.768 0 0.311 0.712 Benzonitrile 0.8711 1.135 0 0.331 0.742 Methylbenzoate 1.0726 0.923 0 0.439 0.738 Biphenyl 1.3242 0.874 0 0.298 1.312 Acetophenone 1.0139 1.026 0 0.503 0.806 Benzophenone 1.4808 1.33 0 0.576 1.224 3-Phenylpropanol 1.1978 0.892 0.354 0.669 0.821 Benzyl alcohol 0.916 0.882 0.4 0.557 0.803 N-benzyl formamide 1.1137 1.8 0.4 0.63 0.99 Phenol 0.7751 0.759 0.716 0.319 0.769 p-Chlorophenol 0.8975 0.794 0.886 0.205 1.016 p-Nitrotoluene 1.0315 1.194 0 0.264 0.918 p-Nitrobenzylchloride 1.1539 1.34 0 0.4 1.08 Nitrobenzene 0.8906 1.138 0 0.269 0.846 Naphthalene 1.0854 0.906 0 0.193 1.24 Anthracene 1.4544 1.309 0 0.253 1.923

3.2.2 Equipment

An Agilent (Santa Clara, CA, USA) ZORBAX Stablebond C-18 column, 50 mm x 4.6 mm with 5 µm stationary phase particles, was utilized for all aspects of this study. According to the manufacturer, this column has a monomeric, non-endcapped stationary phase with a pore size of 80 A,˚ a surface area of 180 m2/g, and a carbon load of 10%.

49 The chromatographic system used in this work consisted of a Waters (Milford, MA, USA) Model 501 HPLC pump, a Kratos Analytical (Manchester, UK) Spectroﬂow 757 UV-Vis Detector, a Sigma-Aldrich (St. Louis, MO, USA) Rheodyne Model 7125 6-port injection valve, and a Perkin Elmer (Waltham, MA, USA) Nelson 950A interface with TotalChrom Workstation v. 6.2.1 software. A glass column jacket and Fisher Scientiﬁc (Waltham, MA, USA) Isotemp 9105 circulating water bath was employed to maintain a constant column temperature.

3.2.3 Procedures

For the van’t Hoff portion of this study, the alkylbenzene series was injected onto a Sta- blebond C-18 column in triplicate with a flow rate of 1.00 mL/min, a detection wavelength of 254 nm, and an injection volume of 20 µL with acetone as the void marker. This series was examined for mobile phases of 50% ACN/H2O, 30% ACN/H2O, 60% MeOH/H2O, 40% MeOH/H2O, ◦ ◦ 40% EtOH/H2O, and 30% EtOH/H2O at 5 temperatures between 30 C to 50 C for the acetonitrile/water and methanol/water mobile phases and between 50◦C and 80◦C for the ethanol/water mobile phases. For the linear solvation energy relationship, analytes in Table 3.1 were injected onto the Stablebond C-18 column in triplicate with a flow rate of 1.00 mL/min, a detection wavelength of 254 nm, and an injection volume of 20 µL. The traditional mobile phases examined were 60% and ◦ 40% MeOH/H2O and 50% and 30% ACN/H2O at 30 C. The ethanol/water mobile phase conditions ◦ ◦ ◦ examined were 50% EtOH/H2O at 30 C; 40% EtOH/H2O at 50 C and 80 C; 30% EtOH/H2O at ◦ ◦ ◦ ◦ 50 C, 65 C, and 80 C; and 20% EtOH/H2O at 80 C.

3.3 Results and Discussion 3.3.1 van’t Hoﬀ Analysis

A van’t Hoﬀ analysis was performed to analyze the thermodynamics of retention for the alkylbenzene series (benzene, toluene, ethylbenzene, propylbenzene, and butylbenzene) with acetonitrile/water (50% and 30%, 30◦C - 50◦C), methanol/water (60% and 40%, 30◦C - 50◦C), and ethanol/water (40% and 30%, 50◦C - 80◦C) mobile phases. The van’t Hoﬀ retention factor plots were examined for each analyte at all conditions and were determined to have a high degree of linearity as evidenced by their R2 values being greater than 0.995. This high correlation indicates

50 that the van’t Hoff model provided an excellent fit with the data. The standard enthalpies of transfer for each alkylbenzene with each mobile phase are listed in Table 3.2 at the end of this section. As noted in Section 3.1.1, however, these plots are insufficient to determine the standard entropies of transfer due to the phase ratio term. Methylene selectivities were, therefore, measured ◦ for each temperature and mobile phase. The selectivity plot for 40% EtOH/H2O at 50 C is shown in Figure 3.1. All selectivity curves in this study had R2 values of 0.999 or greater.

Figure 3.1: The methylene selectivity curve for the 40% EtOH/H2O mobile phase at 60◦C. Homolog number corresponds to the number of methylene groups on alkylbenzene analytes.

The slope of each selectivity curve was then set versus inverse temperature in the van’t Hoff selectivity plots. These plots are shown in Figure 3.2. The van’t Hoff curves were found to be linear with R2 values of at least 0.99, implying that the model provided an excellent for the data over the temperature range studied. The standard enthalpies and entropies of transfer for the methylene group were calculated from the slopes and intercepts of the van’t Hoff selectivity plots, respectively. This thermodynamic information is presented in Table 3.2 at the end of this subsection. Table 3.2 also includes the Gibbs free energy of transfer for the methylene group at 50◦C for each mobile phase.

51 Figure 3.2: The van’t Hoff selectivity plots for methylene are presented for the 50% and 30% ACN/H2O mobile phases, the 60% and 40% MeOH/H2O mobile phases, and 40% and 30% EtOH/H2O mobile phases. For methanol/water and acetonitrile/water mobile phases, the temperature range for the van’t Hoff analysis was 30◦C - 50◦C. For the ethanol/water mobile phases, the temperature range for the van’t Hoff analysis was 50◦C - 80◦C. A temperature axis is added for additional clarity.

In this work, the enthalpies of transfer were negative and larger in magnitude than the entropy of transfer terms (T ∗∆S) for both acetontrile and methanol mobile phases. In addition, the thermodynamics differed significantly between the two modifiers. Enthalpies were found to be larger in magnitude (more favorable) for methanol mobile phases. The entropies were likewise quite different between the modifiers with positive (favorable) values for acetonitrile/water mobile phases

52 and negative (unfavorable) values for methanol/water mobile phases. In comparison to van’t Hoff analyses performed by other authors [81, 104, 110, 111], these thermodyanmic values for methylene transfer were found to be comparable in both magnitude and sign for these modifiers. For example, in a study by Rantangua and Carr with C-18 stationary phases [104], the enthalpies of methylene transfer for acetonitrile/water mobile phases varied by composition between -0.131 and -0.285 kcal/mol (-0.549 and -1.19 kJ/mol), which were generally smaller than the enthalpies for the methanol mobile phases (-0.213 to -0.871 kcal/mol or -0.892 to -0.871 kJ/mol). Additionally, the entropy terms (T ∗∆S at 25◦C) for acetonitrile/water mobile phases were found to be positive at moderate compositions (<70%) varying between 0.129 and 0.050 kcal/mol (0.540 and 0.209 kJ/mol). Methanol/water mobile phases had negative entropy terms at 25◦C that varied between -0.309 and -0.09 kcal/mol (-1.29 and -0.377 kJ/mol). In another study that employed methylene van’t Hoff analyses, Allmon and Dorsey found similar trends on a polystyrene-coated zirconia stationary phase [81]. In their study, the enthalpies of methylene transfer for 15% ACN/H2O, 25% ACN/H2O, and 35% MeOH/H2O mobile phases were found to be -2.1 ± 0.74 kJ/mol, -1.4 ± 0.16 kJ/mol, and -4.4 ± 0.44 kJ/mol, respectively. For these mobile phases, the entropy values were 0.3 ± 2.3 J/(K*mol), 0.63 ± 0.50 J/(K*mol), and -8.1 ± 1.4 J/(K*mol). As presented in Figure 3.3 of this dissertation, the standard enthalpies for the transfer of an analyte from the mobile phase to the stationary phase provide a greater contribution to retention than the entropies. In addition, the larger negative enthalpies of methanol/water and ethanol/water mobile phases means that temperature has a greater impact on the elution strength of these mobile phases than would be observed with acetonitrile/water mobile phases. The greater sensitivity of ethanol/water mobile phases versus acetonitrile/water mobile phases supports the conclusion presented in Chapter 2 that the lack of significant changes in the solvent strength parameter S with acetonitrile/water mobile phases observed in the literature is due to acetonitrile/water mobile phases being less sensitive to changing temperature. Like with previous van’t Hoff studies on C-18 columns [104], Figure 3.3 of this dissertation demonstrates similar thermodynamic behavior. Specifically, entropic contributions provide a minor favorable effect on retention with acetonitrile/water mobile phases and minor unfavorable contributions with methanol/water mobile phases. Ethanol/water mixtures, however, are illustrated to

53 Figure 3.3: Histogram of the thermodynamics of transfer for methylene between a Sta- blebond C-18 stationary phase and one of several mobile phases: 50% or 30% ACN/H2O; 60% or 40% MeOH/H2O; 40% or 30% EtOH/H2O. For methanol/water and acetonitrile/water mobile phases, the temperature range was 30◦ - 50◦C. For the ethanol/water mobile phases, the temperature range was 50◦ - 80◦C. The thermodynamic values are presented in Table 3.2.

54 have significantly more unfavorable entropies. In addition, increasing water content results in a less favorable entropic contribution that counters the increasingly favorable enthalpic contributions of an analyte partitioning to the more similar, non-polar C-18 stationary phase. These observations demonstrate that the partition process results in the mobile phase-stationary phase system becoming more ordered with alcohol mobile phases when an analyte partitions into the stationary phase. According to the partition model of chromatography, the retention process is the transfer of an analyte from a cavity in the aqueous mobile phase to a cavity into an alkyl-ligand stationary phase. The thermodynamics of these two transfer processes has been studied by Rantangua and Carr [104]. In their work, the transfer of a non-polar analyte into the stationary phase was demonstrated to be enthalpically favorable with an unfavorable entropic component due to the increased order of the stationary phase by the formation of a cavity around the analyte. They also determined that the transfer of the analyte out of the mobile phase is enthalpically unfavorable but entropically favorable. This change in entropy indicates that the presence of a non-polar analyte disrupts the hydrogen-bonding network of water, thus increasing the order of the mobile phase. Further, they suggested that the observed differences in entropies between methanol and acetonitrile modifiers was due to increased competitive hydrogen-bonding as a result of the hydroxyl group on methanol. As observed in this dissertation and works by other authors, the overall entropy of the stationary phase-mobile phase system changes from positive (favorable) to negative (unfavorable) by switching the organic modifier to an alcohol. This observation thereby indicates that the increase in entropy from exclusion of the non-polar analyte from the mobile phase is smaller when either methanol or ethanol is employed as the modifier. This result then implies that the analyte cavity within the mobile phase is stabilized by the hydrogen-bonding interactions of the solvating alcohol, which limits the cavity’s disruption of the hydrogen-bonding network of the mobile phase. As presented in Chapter 2, ethanol is a stronger modifier than methanol. Looking at identical organic fractions of 40% alcohol; methanol and ethanol have similar enthalpic contributions, which is expected given their similar dipole moments and hydrogen-bonding character. While the entropic contributions to retention are relatively minor, these contributions with ethanol are found to be greater and result in a significant reduction in the favorability of transfer as presented by the Gibbs free energies of transfer in Figure 3.3. This observation indicates that ethanol/water mobile

55 phases are stronger because of a signiﬁcant increase in the order of the mobile phase-stationary phase system upon analyte transfer thus reducing the favorability of analytes leaving the mobile phase. As ethanol has a similar dipole and larger size versus methanol, it is improbable that ethanol participate more readily in the hydrogen-bonding network of water. The greater dispersion forces of ethanol, however, could result in improved solvation of the non-polar analyte thereby forming a cavity that is less disruptive of water’s hydrogen-bonding network.

3.3.2 Linear Solvation Energy Relationships

The analyte set shown in Table 3.1 was chosen from a 22-solute set previously described by Zhao and Carr [112]. The 22-solute set consists of aliphatic and aromatic compounds representing a wide range of solvatochromic parameter values for effective application of the LSER model. This set was carefully chosen by Zhao and Carr from a larger, broad set of 87 aliphatic and aromatic compounds used by Tan, Carr, and Abraham[113]. The 22-solute set was found to achieve near identical LSER coefficients compared to the larger 87-analyte set providing a more efficient data set for LSER analysis with reversed phase liquid chromatographic columns. The retention factor was determined for each analyte at each condition studied. These values are listed in Table 3.3 and Table 3.4 as log k’. A multiparameter linear regression analysis was then performed with the LINEST function contained within Microsoft Office 2007 Excel (Redmond, WA, USA) in order to fit the LSER model presented in Equation 3.5 onto the log k’ values and solvatochromic parameters presented in Table 3.1. Only a few solutes were excluded from some of the analyses due to retention times being too short or too long. These solutes are denoted in Table 3.3 and Table 3.4 by either a dash (-) or an asterisk (*). Analytes that were too short (k’ less than 0.2) elute too close to the void marker for the analyte to be distinguishable from the void marker. Analytes that were too well-retained (k’ values generally greater than 120) elute as a peak too broad for accurate determination of its retention time. The resultant LSER coefficients from these analyses are listed in Table 3.5 at the end of the chapter. All regressions had an R2 value of 0.99 or greater. Plots of the predicted log k’ values versus experimental log k’ values are also presented at the end of this chapter (Figures 3.8 to 3.11) to demonstrate the applicability of the model. These figures further demonstrate that there were no egregious outliers, and that the LSER model provided a reasonable fit to the data.

56 Table 3.2: The van’t Hoﬀ analyses of ACN/H2O, MeOH/H2O, and EtOH/H2O mobile phases on an Agilent Stablebond C-18 column. The ∆G◦ and T∆S◦ are calculated at 50◦C. Entropies are in units of J/mol*K. All other terms are in units of kJ/mol. C ◦ 0.31 0.36 0.48 0.33 0.79 0.17 0.52 0.17 0.24 80 ± ± ± ± ± ± ± ± ± − C ◦ 30% EtOH -3.60 -4.12 -1.33 -2.27 50 -16.41 -19.78 -22.33 -27.66 -31.93 C ◦ 1.8 0.11 0.30 0.24 0.27 0.18 0.55 0.18 0.25 80 ± ± ± ± ± ± ± ± ± − C ◦ 40% EtOH -23.6 -3.11 -3.97 -1.28 -1.83 50 -14.71 -17.02 -20.42 -26.95 C ◦ 0.22 0.24 0.24 0.17 0.16 0.049 0.050 0.070 50 ± ± ± ± - ± ± ± ± − C ◦ 40% MeOH -2.53 30 -13.28 -16.40 -19.02 -22.82 -3.122 -0.817 -2.306 C ◦ 0.30 0.24 0.26 0.26 0.26 0.029 0.093 0.030 0.042 50 ± ± ± ± ± ± ± ± ± − C ◦ 60% MeOH 30 -10.18 -12.36 -14.10 -16.49 -19.07 -2.191 -1.768 -0.571 -1.620 C ◦ 0.26 0.21 0.31 0.34 0.041 0.094 0.13 0.068 50 ± ± ± ± - ± ± ± − ± C ◦ 30% ACN 1.74 30 -12.47 -13.77 -14.98 -17.13 0.470 -1.521 -2.091 C ◦ 0.37 0.42 0.035 0.050 0.36 0.36 0.11 0.036 0.038 50 ± ± ± ± ± ± − ± ± ± C ◦ 50% ACN 2.37 -8.56 -8.66 30 -9.24 -10.29 -11.25 0.765 -0.701 -1.466 2 2 2 2 CH CH CH CH − − − − ◦ ◦ ◦ ◦ Toluene Benzene ◦ S S G ◦ H H ∆ ∆ H ∆ Ethylbenzene Butylbenzene T∆ ∆ Propylbenzene ◦ ◦ ∆ ◦ H H H ∆ ∆ ∆

57 As shown in Table 3.5, the retention mechanism is dominated by both the v and b terms. This result is found ubiquitously in LSER studies respecting RPLC systems[105]. This observation makes chemical sense when considering the partitioning process and the phases involved. A large, positive v term indicates that the larger and mainly non-polar analytes will more readily partition into the more disorganized aliphatic environment of the stationary phase from the more polar and highly associated aqueous mobile phase due to easier formation of a cavity. The large, negative b term corresponds to the significant hydrogen-bond donation from both water and alcohol molecules toward hydrogen-bond accepting sites on the analytes. Thus, analytes with greater hydrogen-bond basicity will be elute more readily. Figure 3.4 and Figure 3.5 present the impact of changing mobile phase condition on the LSERs between ethanol/water mobile phases and the Stablebond C-18 column. In Figure 3.4, changes in the ethanol content are shown to have a negligible impact on most chemical interactions excluding the dominant v and b terms. Decreasing the ethanol concentration in the mobile phase is found to increase the v term, which results in greater retention; while increasing the magnitude of the b term makes elution more favorable. Due to the larger V parameters for solutes, the competing changes in retention result in a much larger retention factor. The contributions of the stationary phase to the v parameter would not be expected to change dramatically relative to mobile phase contributions when the composition of the mobile phase is adjusted. Thus, the increase in the v term with water content would primarily be due to increasing mobile phase cohesion and a reduction in dispersive interactions within the mobile phase. From a comparison of the thermodynamic parameters between mobile phases of differing ethanol content, however, the insignificant differences in the change of entropy term with composition implies a similar degree of change in the mobile phase structure from analyte transfer. The smaller change in relative hydrogen-bond acidity of the mobile phase versus the stationary phase further indicates that increasing composition only moderately reduces cohesion of the mobile phase. These results reveal that the increase in solvent strength with higher ethanol content is more an outcome of reduced cavity formation energy from increasing dispersive interactions than from a disruption of the hydrogen-bonding network by a solvated analyte. Figure 3.5 presents the changes in coefficients with increasing temperature. A similar trend is observed as the solvent strength is increased with higher temperatures. The v term becomes smaller

58 Table 3.3: The analytes and log k’ values (average of 3 measurements) for LSER analysis of several mobile phases at 30◦C. A dash (-) indicates insuﬃcient retention for analysis. An asterisk (*) indicates retention was too long for accurate determination of the retention. 0.404 0.677 0.917 0.948 1.177 1.430 0.906 0.718 0.329 0.140 0.474 0.062 0.291 0.292 0.035 0.831 1.283 1.097 -0.245 -0.178 -0.007 -0.463 -0.709 -0.400 50% EtOH * * * 0.831 1.251 1.633 1.685 2.071 1.700 1.399 0.858 0.406 0.940 0.559 1.587 0.792 0.133 0.027 0.103 0.714 1.017 1.025 0.614 1.669 40% MeOH 0.291 0.595 0.857 0.878 1.148 1.441 0.862 0.650 0.244 0.222 0.606 0.066 0.006 0.292 0.262 0.802 1.360 1.105 -0.218 -0.127 -0.442 -0.629 -0.509 -0.015 60% MeOH 0.922 1.274 1.612 1.622 1.992 2.292 1.602 1.387 0.903 0.577 0.860 0.525 1.489 0.456 0.099 0.626 1.118 1.118 0.785 1.601 2.290 1.997 -0.118 -0.267 30% ACN - 0.349 0.592 0.820 0.841 1.071 1.317 0.842 0.656 0.286 0.015 0.232 0.635 0.415 0.415 0.186 0.768 1.206 1.007 -0.021 -0.209 -0.672 -0.454 -0.064 50% ACN Phenol Anisole Toluene Analyte Benzene Biphenyl p-Xylene Anthracene Benzonitrile Naphthalene Nitrobenzene Ethylbenzene Butylbenzene Acetophenone Benzophenone Benzyl alcohol p-Nitrotoluene Propylbenzene Bromobenzene p-Chlorophenol Methylbenzoate 3-Phenylpropanol p-Dichlorobenzene N-benzyl formamide p-Nitrobenzylchloride

59 Table 3.4: The analytes and log k’ values (average of 3 measurements) for LSER analysis of ethanol/water mobile phases at several temperatures. An asterisk (*) indicates retention was too long for accurate determination of the retention time. C ◦ * * 80 0.843 1.231 1.603 1.628 2.012 1.652 1.338 0.779 0.327 0.816 0.428 1.399 0.638 0.568 0.890 0.863 0.518 1.528 1.960 -0.014 -0.216 -0.042 20% EtOH C ◦ 80 0.596 0.922 1.224 1.242 1.546 1.864 1.238 0.987 0.491 0.032 0.457 0.110 0.916 0.276 0.282 0.555 0.527 0.244 1.127 1.671 1.466 -0.289 -0.523 -0.271 30% EtOH C ◦ 65 0.701 1.048 1.371 1.390 1.726 2.065 1.408 1.141 0.603 0.123 0.567 0.196 1.050 0.397 0.429 0.677 0.664 0.350 1.290 1.889 1.656 -0.194 -0.419 -0.159 30% EtOH C ◦ * * 50 0.833 1.205 1.541 1.559 1.937 1.590 1.305 0.717 0.270 0.689 0.309 1.218 0.522 0.601 0.820 0.826 0.475 1.469 1.867 -0.092 -0.273 -0.014 30% EtOH C ◦ 80 0.355 0.616 0.858 0.874 1.113 1.358 0.863 0.652 0.218 0.132 0.496 0.236 0.198 0.751 1.175 1.015 -0.260 -0.187 -0.049 -0.545 -0.872 -0.522 -0.034 -0.031 40% EtOH C ◦ 50 0.530 0.838 1.118 1.140 1.419 1.711 1.114 0.899 0.402 0.317 0.718 0.165 0.223 0.456 0.448 0.173 1.022 1.532 1.335 -0.093 -0.024 -0.348 -0.589 -0.292 40% EtOH Phenol Anisole Toluene Analyte Benzene Biphenyl p-Xylene Anthracene Benzonitrile Naphthalene Nitrobenzene Ethylbenzene Butylbenzene Acetophenone Benzophenone Benzyl alcohol p-Nitrotoluene Propylbenzene Bromobenzene p-Chlorophenol Methylbenzoate 3-Phenylpropanol p-Dichlorobenzene N-benzyl formamide p-Nitrobenzylchloride

60 Figure 3.4: Variation of LSER system constants for ethanol/water mobile phases with varying organic content.

61 Figure 3.5: Variation of LSER system constants for ethanol/water mobile phases with varying temperature.

62 favoring elution and the b term increases favoring retention. These changes reflect the observations by Pawlowski and Poole [114] and Allmon and Dorsey [81]. Unlike changes in composition, however, temperature is shown to have a modest impact on the v and b coefficients. This indicates that temperature increases solvent strength primarily by reducing hydrogen-bond formation and mobile phase cohesiveness while increased ethanol provides more dispersive interactions to solvate the analyte. These results explain the observation presented in Chapter 2 of the weaker impact on solvent strength by temperature. From a more practical perspective, these findings present two alternative means of adjusting solvent strength. While an increase in temperature will reduce retention, the increase in the b term by lower mobile phase hydrogen-bond acidity will significantly mitigate the negative impact on retention by the reduction in mobile phase cohesivity for strong hydrogen-bond bases. On the other hand, increases in ethanol content provide a far stronger reduction in retention with respect to analyte size. Comparing mobile phases with similar retention in Figures 3.6 and 3.7 reveals significant differences in the cavity formation term, especially between the alcohol/water mobile phases and the acetonitrile/water mobile phases. It must be noted, however, that direct comparisons of mobile phases with different modifiers is significantly complicated by several factors. First, the v parameter is not a purely entropic term and, second, LSERs cannot distinguish enthalpic and entropic effects [105]. Also as noted previously, the v parameter includes both dispersive interactions and energies of cavity formation from both the mobile phase and the modifier-solvated stationary phase. The difference in the v parameter combined with the differences in entropic contributions to methylene transfer do indicate that even at similar eluting strength, the mobile phase structures differ significantly even between methanol/water and ethanol/water mobile phases. This conclusion is demonstrated more significantly by comparison of the thermodynamics at these conditions. For the 60% methanol/water mobile phase, enthalpic contributions are reduced by only a quarter in magnitude due to entropic effects. For the 40% ethanol/water mobile phase at 80◦C, however, enthalpic contributions are reduced by almost half in magnitude as a result of entropic effects. While the v parameters differ, the b terms were found to be very similar indicating that the relative hydrogen-bond acidity of these mobile phases versus the hydrogen-bond acidity of their corresponding modifier-solvated stationary phases is nearly identical. This observation was not

63 Figure 3.6: Linear solvation energy relationship system values for the 50% ACN/H2O, ◦ 60% MeOH/H2O, and 40% EtOH/H2O at 80 C. The standard error of each coeﬃcient is represented by vertical error bars.

64 Figure 3.7: Linear solvation energy relationship system values for the 30% ACN/H2O, ◦ 40% MeOH/H2O, and 20% EtOH/H2O at 80 C. The standard error of each coeﬃcient is represented by vertical error bars.

65 expected. The adsorption of either alcohol to the stationary phase contributes to its hydrogen- bond acidity whereas acetonitrile should not contribute at all to the stationary phase. The similar b terms imply that the increase in the hydrogen-bond acidity of the stationary phase with either ethanol or methanol is balanced out by that alcohol’s contributions to the hydrogen-bond acidity in the mobile phase, such that; the net hydrogen-bond acidity of the either alcohol/water mobile phase does not signiﬁcantly diﬀer from that found with ambient 50% or 30% aprotic/water mobile phases.

3.4 Conclusions

In order to further explore the ethanol/water mobile phases, this chapter studied the similarities and differences between ethanol and the traditional organic modifiers by examining their thermodynamic and solvatochromic relationships with RPLC retention. The results from these studies demonstrate that ethanol varies considerably from the acetonitrile modifier and unexpectedly from the methanol modifier. While the retention mechanism for methanol/water and ethanol/water mobile phases appeared to be more similar than with acetonitrile/water mobile phases, van’t Hoff analysis and linear solvation energy relationship analysis demonstrated considerable differences in how the mobile phase structure changes with analyte partitioning. As observed by other authors, the thermodynamics of methylene transfer for RPLC are dominated by enthalpic contributions. Entropic contributions are also known to differ between acetonitrile and methanol modifiers with minor favorable contributions for acetonitrile/water mobile phases and moderate unfavorable contributions for methanol/water mobile phases. From this researcher’s work, the enthalpic contributions for the alcohols were expectantly more favorable for retention compared to acetonitrile/water mobile phases. This greater enthalpic contribution indicates that retention with either alcohol modifier is more sensitive to changes in temperature. This comparison supports the conclusion from Chapter 2 that the S parameter for acetonitrile/water mobile phases is apparently temperature independent due to an undetectable change in the S parameter with respect to temperature. From the comparison of the two alcohols, similar enthalpies were observed at the same composition, even though their solvent strengths differ considerably, which was established in Chapter 2. The modifiers entropic contributions, however, clearly exhibited significant differences. This

66 deviation implies that the greater solvent strength of the ethanol modifier results from differences in system order with analyte partitioning. Moreover, based on the thermodynamics of the partition model, the more unfavorable entropies of ethanol/water mobile phases indicate that the presence of non-polar analytes has less of an impact on the order of the ethanol/water mobile phase compared to what is observed with methanol/water mobile phases. As a result, the cavity around the analyte within the mobile phase is more entropically stable and the overall change in entropy of the system is dominated more significantly by stationary phase cavity formation. Further investigations into the retention mechanism were accomplished by a solvatochromic analysis employing linear solvation energy relationships. From this analysis at similar values of retention for the three modifiers (acetonitrile, methanol, and ethanol), it was observed that cavity formation and dispersive interactions differ significantly even between the two alcohol modifiers. These differences, combined with earlier observed deviations in entropic contributions to methylene transfer, evidence that even at similar eluting strength, the mobile phase structures differ significantly between the modifiers. Furthermore, while differences in the other chemical interactions were small or insignificant, the hydrogen-bonding basicity terms for the modifiers were effectively identical to statistical degree. This observation implies that the differential effects of hydrogen-bonding acidity by the mobile phase versus the stationary phase were indistinguishable between the modifiers. As a result of these differences and similarities, replacement of one modifier with another modifier based on solvent strength would result in observable differences in retention; however, these differences would likely be more noticeable in separations dominated by differences in molecular size than those with separations dominated by differences in the hydrogen-bonding basicity of the analytes. Additionally, LSER analyses presented significant differences in how temperature and percent organic affected the retention mechanism. Increases in temperature reduced retention by weakening hydrogen-bonding interactions and reducing cohesion moderately in the mobile phase. With changes in percent composition, the mobile phase’s eluting strength increased more from greater dispersive interactions between ethanol and analyte molecules than from disruption of the hydrogen-bonding network or reduced mobile phase cohesion from weaker hydrogen-bonding interactions. This conclusion presents two differing methods for adjusting solvent strength: (1) moderately altering hydrogen-bonding acidity by changing temperature and (2) greatly modifying

67 mobile phase cavity formation effects on retention by varying ethanol content. This result supports and clarifies the difference in relative impact on solvent strength between changes in these two system parameters that was observed in Chapter 2.

Figure 3.8: Predicted versus experimental log k’ values for ambient acetonitrile/water LSER systems described in Table 3.5.

68 Figure 3.9: Predicted versus experimental log k’ values for ambient alcohol/water LSER systems described in Table 3.5.

69 Figure 3.10: Predicted versus experimental log k’ values for ethanol/water LSER systems described in Table 3.5.

70 Figure 3.11: Predicted versus experimental log k’ values for ethanol/water LSER systems described in Table 3.5.

71 Table 3.5: Comparison of the Linear Solvation Energy Relationship system coeﬃcients. 2 R 0.992 0.994 0.992 0.994 0.989 0.993 0.994 0.994 0.995 0.996 0.995 ) 0.15 0.10 0.11 0.11 0.10 0 0.076 0.079 0.085 0.081 0.091 0.077 ± ± ± ± ± ± ± ± ± ± ± log(k -0.43 -0.44 -0.84 -0.56 -0.59 -0.74 -0.61 -0.52 -0.56 -0.62 -0.60 0.14 0.09 0.10 0.10 0.093 0.096 0.093 0.096 0.072 0.082 0.094 e ± ± ± ± ± ± ± ± ± ± ± 0.41 0.32 0.13 0.17 0.44 0.33 0.16 0.33 0.32 0.31 0.12 0.21 0.14 0.15 0.15 0.14 0.11 0.11 0.11 0.13 0.10 0.099 ± ± ± ± ± ± ± ± ± ± b ± -1.81 -2.39 -1.72 -1.89 -2.04 -1.79 -2.38 -2.18 -2.03 -2.22 -2.17 0.11 0.077 0.078 0.053 0.079 0.074 0.059 0.060 0.057 0.067 0.054 ± a ± ± ± ± ± ± ± ± ± ± -0.80 -0.69 -0.48 -0.39 -0.46 -0.45 -0.54 -0.39 -0.45 -0.51 -0.49 0.12 0.083 0.085 0.063 0.085 0.080 0.064 0.070 0.067 0.067 0.064 s ± ± ± ± ± ± ± ± ± ± ± -0.70 -0.62 -0.72 -0.71 -0.75 -0.77 -0.79 -0.74 -0.76 -0.79 -0.78 0.18 0.12 0.12 0.13 0.12 0.11 0.085 0.096 0.095 0.091 0.087 v ± ± ± ± ± ± ± ± ± ± ± 1.64 2.42 2.08 1.90 2.17 2.32 2.97 1.91 2.70 2.52 2.85 C C C C C C C C C C C ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ 30 30 30 30 30 50 80 50 65 80 80 Temp. 50 ACN 30 ACN 50 EtOH 40 EtOH 40 EtOH 30 EtOH 30 EtOH 30 EtOH 20 EtOH 60 MeOH 40 MeOH % Organic

72 CHAPTER 4

ESTIMATION OF THE OCTANOL/WATER PARTITION COEFFICIENT

4.1 Introduction

As previously presented in Section 1.3, ethanol has been successfully applied to several specific separations. This specificity, however, does not demonstrate how broadly ethanol can be employed. This fact limits whether ethanol/water mobile phases can be viewed as a reasonable and green alternative. A far-broader application of RPLC is the estimation of a compound’s lipophilicity. Adapting high temperature ethanol/water mobile phases to this widely-used application would significantly reduce disposal costs resulting from both decreases in organic volume and waste toxicity.

4.1.1 Lipophilicity

Lipophilicity is an important physiochemical property that defines the capability of a chemical to dissolve in fats, oils, lipids, and non-polar solvents. This property is an important descriptor widely used to ascertain a drug’s absorption, distribution, metabolism, and excretion (ADME) behavior. This property is generally expressed as the logarithm of the n-octanol/water partition coefficient (log P ). While direct measurements via shake-flask or slow stirring methods are traditional, these techniques are time-consuming and labor intensive that also encompasses high requirements in solvent volume [115, 116]. Multiple techniques have been developed to measure log P indirectly through either experimental approximations or algorithmic estimations. RPLC has become one of the more commonly employed experimental methods [117, 118] and has been recommended by several organizations such as the Organization for Economic Cooperation and Development (OECD) [119]. The estimation method for RPLC is accomplished by correlating a training set of solutes with known log P values against log k’w values using the model [120] shown in Equation 4.1, which is based on the Collander equation [121].

73 ′ log P = p + qlog(kw) (4.1)

For Equation 4.1, p and q are linear fitting constants. Log k’w represents the partitioning of a solute between a C-8 or C-18 stationary phase and a pure water mobile phase usually extrapolated by the logarithm of the retention factor to neat aqueous fraction of the mobile phase via the Snyder- Soczewinski equation [122] that was presented in Equation 2.1 and discussed in Chapter 2. This indirect method circumvents many of the drawbacks of direct techniques, but the accuracy of the extrapolation is often limited due to the key differences noted between RPLC and octanol/water partition systems; including pore size effects, reproducibility with generic column types between manufacturers and column batches, and interactions with sorbent surfaces [123]. In addition, log P values are often characterized by errors in reporting, incomplete data compilations, large order of magnitude data spreads, and the majority of original data being of “poor or unevaluatable quality” [124].

The traditional RPLC methodology has been the extrapolation of log k’w with at least four isocratic mobile phase ratios using Equation 2.1 and correlating to log P via Equation 4.1 [125]. The log P value for a compound with unknown log P is then determined using the fitting parameters for Equation 4.1 and the compound’s log k’w value. Since this technique was first proposed, however, a well-defined procedure and set of system parameters has not been definitively established at this late date. These deficiencies include a standard analyte set, required column specifications, or definite requirements for uncertainty and analyte choice. The closest document to fulfilling these details is the OECD guideline 117 [119]. The guideline specifies that the R2 for the fitting of Equation 4.1 must be 0.90 or better, “corresponding to an octanol/water partition coefficient of log Pow ± 0.5 log units” based on a set of 6 to 10 reference analytes similar in structure to the test substance. In addition, the guideline includes a list of reference analytes with their log P values rounded to the nearest tenth. Also, the guideline recommends that the stationary phase should have a minimal number of polar groups and the methanol mobile phase should be at least 25% water for substances with a log P of less than 6, which is considered the upper bound for RPLC log P estimation. Most of the recent developments with reversed phase HPLC estimation of octanol/water partition coefficients have focused on the various weaknesses of this method [125]. More specifically,

74 recent work has focused on improving estimations of log P values for strong acidic/basic compounds, metal complexes, and surfactants, among others. In addition, various works have focused on improving the speed of these methods by reducing run times with gradient elution or novel equations to reducing the total number of runs required for estimation. One notable focus of this research is the addition of octanol to mobile phases. In a study by Benhaim and Grushka [126], methanol/water mobile phases at varying pH were employed to estimate the octanol/water partition coefficient with and without octanol in the mobile phase for a variety of ionic and neutral analytes. In their work, they found that the presence of octanol significantly improved estimations of log P values with an increase in the R2 from 0.91 to 0.93 for a set of 41 neutral analytes. In addition, the Collander slope was found to increase from 0.96 to 1.00 with the octanol additive, thereby implying the process became more homoenergetic with the shake-flask method. These improvements in estimation were further validated by a comparison of the LSERs, which demonstrated a significantly higher correlation in coefficients between RPLC with the octanol additive and the octanol/water partition system. Most coefficients, in fact, were not significantly different with only hydrogen-bonding interactions deviating to any significant degree.

4.1.2 Current Work

In this work, ethanol/water mobile phases at high temperatures were explored as a modiﬁer for the estimation of log P . As a basis for comparison, acetonitrile/water and methanol/water mobile phases were employed based on the OECD guideline. However, due to the large diﬀerences in temperature between the use of ethanol/water mobile phases and the acetonitrile/water and methanol/water mobile phases, an extrapolation based on temperature was also performed using the method presented in Chapter 2.

4.2 Experimental 4.2.1 Reagents

75 Table 4.1: Analytes used in this work are noted with their octanol/water partition coeﬃ- cients and their LSER parameters.

Analyte log(P) Ref. V S A B E Ref. Benzene 2.13 [128] 0.7176 0.511 0 0.144 0.608 [108] Toluene 2.73 [128] 0.8573 0.499 0 0.139 0.606 [108] Ethylbenzene 3.15 [128] 0.9982 0.499 0 0.139 0.613 [108] Anisole 2.11 [127] 0.916 0.768 0 0.311 0.712 [108] Bromobenzene 2.99 [128] 0.8914 0.723 0 0.089 0.882 [108] Benzonitrile 1.56 [128] 0.8711 1.135 0 0.331 0.742 [108] Methylbenzoate 2.12 [128] 1.0726 0.923 0 0.439 0.738 [108] Acetophenone 1.58 [128] 1.0139 1.026 0 0.503 0.806 [108] Benzophenone 3.18 [128] 1.4808 1.33 0 0.576 1.224 [108] Benzyl Alcohol 1.10 [128] 0.916 0.882 0.4 0.557 0.803 [108] Phenol 1.46 [128] 0.7751 0.759 0.716 0.319 0.769 [108] p-Chlorophenol 2.39 [128] 0.8975 0.794 0.886 0.205 1.016 [108] p-NitroToluene 2.37 [128] 1.0315 1.194 0 0.264 0.918 [108] Nitrobenzene 1.85 [128] 0.8906 1.138 0 0.269 0.846 [108] Napthalene 3.30 [128] 1.0854 0.906 0 0.193 1.24 [108] p-Dichlorobenzene 3.44 [128] 0.9612 0.771 0 0.054 0.872 [108] Biphenyl 4.01 [128] 1.3242 0.874 0 0.298 1.312 [108] Diphenyl Ether 4.21 [128] 1.3829 0.912 0 0.267 1.216 [109] Ethylparaben 2.47 [128] N/A N/A N/A N/A N/A 3-Methylindole 2.60 [128] N/A N/A N/A N/A N/A DDE 6.22 ± 0.33 [124] N/A N/A N/A N/A N/A

vacuum filtration. Each mobile phase was degassed through vigorous helium sparging. The analytes used in this experiment are shown in Table 4.1 with their octanol/water partition coefficients (log P ) [124, 127, 128] and their LSER solute-dependent values [108, 109]. Acetone was used as the void marker for all runs. All analytes were obtained from Sigma-Aldrich Chemical Company. Differences in octanol/water partition coefficients can be quite substantial, thereby resulting in significant uncertainty upon which value is the most accurate for an octanol/water partitioning method. This uncertainty in the literature was explored thoroughly in a case study by Pontolillo and Eganhouse [124] as it respects DDT and DDE log P values. The variation in the literature values for even simple organic compounds is also quite apparent when using log P compilations such as Sangster’s 1989 compilation [129]. While Guideline 117 provides log P values for most of the analytes studied, they are limited to a single decimal place potentially limiting comparison via LSER

76 modeling. In addition, several analytes were observed to differ significantly in comparison to other references, specifically the analytes biphenyl and naphthalene. As a result of these discrepancies, the majority of the log P values were sourced through NIH’s Hazardous Substances Data Bank (HSDB) due to its high degree of peer review.

4.2.2 Equipment

An Agilent (Santa Clara, CA, USA) ZORBAX Stablebond C-18 column, 50 mm x 4.6 mm with 5 µm stationary phase particles, was used for all aspects of this study. According to the manufacturer, this column has a monomeric, non-endcapped stationary phase with a pore size of 80 A,˚ a surface area of 180 m2/g, and a carbon load of 10%. The chromatographic system used in this work consisted of a Waters (Milford, MA, USA) Model 501 HPLC pump, a Kratos Analytical (Manchester, UK) Spectroﬂow 757 UV-Vis Detector, a Sigma-Aldrich (St. Louis, MO, USA) Rheodyne Model 7125 6-port injection valve, and a Perkin Elmer (Waltham, MA, USA) Nelson 950A interface with TotalChrom Workstation v. 6.2.1 software. A glass column jacket and Fisher Scientiﬁc (Waltham, MA, USA) Isotemp 9105 circulating water bath was employed to maintain a constant column temperature.

4.2.3 Procedures

All analyses were performed in triplicate with a flow rate of 1.00 mL/min, a detection wavelength of 254 nm, and an injection volume of 20 µL. Each analyte was run at four different mixtures of organic modifier and water. The ranges for each analyte were 30% to 60% for acetonitrile, 30% to 70% for methanol, and 30% to 70% for ethanol. Due to its strong retention, DDE was run separately at ranges of 50% to 80% for acetonitrile, 60% to 90% for methanol, and 60% to 80% for ethanol. Methanol and acetonitrile modifier runs were performed at 30◦C. All ethanol modifier runs were each performed at temperatures of 50◦C, 60◦C, 70◦C, and 80◦C. All regressions were performed using the LINEST function contained within Microsoft Office 2007 Excel. Linear Solvation Energy Relationship analyses were also performed to compare the estimated log P values against those found in the literature using the LINEST function.

77 4.3 Results and Discussion 4.3.1 Log P Estimation

In order to determine the feasibility of estimating log P values with high temperature ethanol/water as the mobile phase, acetonitrile and methanol were initially used as organic modifiers ◦ at 30 C for log P estimation. For both modifiers, log k’w was extrapolated for each analyte via a linear regression analysis of its retention factor at four different mobile phase compositions according to Equation 2.1. These log k’w values were then correlated with the log P values via Equation 4.1 to generate an equation to estimate log P based on log k’w. The log k’w and estimated log P values using acetonitrile or methanol as the modifier are shown in Table 4.2 along with their R2 values for their log k’w extrapolation. All of the fits illustrated high correlations of 0.995 or greater except for the DDE analyte with acetonitrile modifier resulting a slightly lower correlation of 0.984. From Table 4.2, there is a significant difference observed in the extrapolation to pure water retention by choice of mobile phase employed. This deviation has previously been attributed to the extrapolated log k’w representing a pure water mobile phase with a stationary phase solvated by an organic modifier [82]. This hypothesis is based on the well-supported observation of organic eluent uptake onto the stationary phase [130, 131]. As a result, the interface between the mobile phase and stationary phase will be significantly influenced by the organic modifier used. However, theoretically the direct measurement of retention with a pure water mobile phase on an organically adsorbed stationary phase would be the same as that extrapolated with the same organic solvent. Experimentally, a lower retention has been observed through direct measurements of <10% organic content mobile phases, which is often assumed to be due to significant changes in the stationary phase structure [132]. The observation of lower retention, however, has been demonstrated by Walter et al. to be the result of extrusion of highly aqueous mobile phases from stationary phase pores when the mobile phase is not under sufficient pressure to maintain contact with the interior surface area of the stationary phase, such as near the column outlet [133].

It should be noted that log k’w is not an actual representation of retention for a truly pure water mobile phase. The inclusion of a hypothetical adsorbed layer of organic modifier in the extrapolation, however, contributes to the retention mechanism in a significantly positive manner. Alkyl ligands of stationary phases have no inherent hydrogen-bonding character, which should result in a significant limitation for its use as an octanol analog. The addition of an adsorbed layer of a

78 ′ 2 Table 4.2: Extrapolated log kw values with R values and the estimated log P values for both acetonitrile and methanol as organic modiﬁer at 30◦C.

Organic Modiﬁer: Acetonitrile Methanol ′ 2 ′ 2 Analyte log(kw) R log(P) log(kw) R log(P) Benzene 1.819 ± 0.036 0.9987 2.227 1.850 ± 0.032 0.9986 1.910 Toluene 2.304 ± 0.025 0.9995 2.879 2.495 ± 0.019 0.9997 2.554 Ethylbenzene 2.793 ± 0.046 0.9987 3.536 3.107 ± 0.008 1.0000 3.166 Anisole 1.839 ± 0.042 0.9984 2.254 2.012 ± 0.015 0.9998 2.071 Bromobenzene 2.474 ± 0.051 0.9982 3.107 2.788 ± 0.013 0.9999 2.846 Benzonitrile 1.478 ± 0.074 0.9943 1.769 1.655 ± 0.017 0.9997 1.715 Methylbenzoate 1.821 ± 0.047 0.9980 2.230 2.360 ± 0.030 0.9994 2.419 Acetophenone 1.419 ± 0.061 0.9960 1.689 1.804 ± 0.031 0.9991 1.864 Benzophenone 2.750 ± 0.086 0.9961 3.478 3.478 ± 0.076 0.9977 3.536 Benzyl Alcohol 0.760 ± 0.064 0.9954 0.804 1.221 ± 0.003 1.0000 1.281 Phenol 0.991 ± 0.062 0.9959 1.114 1.206 ± 0.032 0.9988 1.266 p-Chlorophenol 1.634 ± 0.051 0.9979 1.979 2.054 ± 0.013 0.9987 2.113 p-NitroToluene 2.191 ± 0.045 0.9985 2.726 2.375 ± 0.012 0.9988 2.434 Nitrobenzene 1.745 ± 0.042 0.9984 2.127 1.757 ± 0.032 1.0000 1.817 Napthalene 2.826 ± 0.071 0.9972 3.580 3.301 ± 0.027 0.9996 3.359 p-Dichlorobenzene 2.685 ± 0.086 0.9950 3.391 3.217 ± 0.042 0.9993 3.275 Biphenyl 3.21 ± 0.11 0.9944 4.093 4.043 ± 0.035 0.9997 4.101 Diphenyl Ether 3.25 ± 0.12 0.9931 4.149 3.968 ± 0.083 0.9981 4.026 Ethylparaben 1.505 ± 0.044 0.9984 1.806 2.436 ± 0.040 0.9992 2.495 3-Methylindole 2.136 ± 0.065 0.9969 2.652 2.462 ± 0.004 1.0000 2.521 DDE 4.17 ± 0.26 0.9838 5.379 6.14 ± 0.12 0.9987 6.201

hydrogen-bonding solvent such as methanol would therefore contribute hydrogen-bonding character to the ‘octanol equivalent’ of the stationary phase-mobile phase interface while the other phase is pure water. This perspective is further supported by the observed improvements in log P /log k’ correlations with the methanol/water mobile phase as “the stationary phase-mobile phase interface contains hydrogen-bonding activity owing to adsorbed methanol” [96]. Correlating the literature log P values (Table 4.1) against the estimated log P values (Table 4.2) generated R2 values of 0.917 for acetonitrile and 0.975 for methanol. Further metrics for this fitting are presented in Table 4.3. While the average of the square residuals for both modifiers was effectively zero, the standard deviation of the residuals for acetonitrile was 0.33 evidencing significant deviations between the literature and estimated log P values with that modifier. Based

79 Table 4.3: The slope, intercept, R2 values, and standard deviation of the residuals for log P versus log k’w based on Equation 1.1. The average of the square residuals was less than 3x10−15 for all data sets.

Mobile Phase Slope (q) Intercept (p) R2 Std. Dev. Resid. ACN 30◦C 1.343 ± 0.093 -0.22 ± 0.22 0.9171 0.33 MeOH 30◦C 0.999 ± 0.037 0.06 ± 0.10 0.9753 0.18 EtOH 30◦C 1.226 ± 0.042 0.24 ± 0.09 0.9781 0.17

on the OECD Guideline 117 [119], this correlation would be deemed adequate; however, several analytes exceeded the stated 0.5 log units in residual. DDE had a residual of 0.841 log units while p-hydroxybenzoic acid ethyl ester was 0.664 log units in residual. In addition, ethylbenzene, benzophenone, benzyl alcohol, phenol, p-chlorophenol, p-nitrotoluene, nitrobenzene, and naphthalene had residuals of between 0.25 and 0.5 log units. These deviations demonstrate a weakness for acetonitrile in estimating the log P values for compounds with significant hydrogen-bonding interactions when using a diverse data set. As shown in Figure 4.1, methanol provides a far better Collander fit than acetonitrile with a slope of 0.999 ± 0.037 and an intercept of 0.06 ± 0.10 with a standard deviation of the residuals at 0.18. According to Minick et al. [134], this near unity slope indicates that the methanol log k’w values in this system are nearly homoenergetic with octanol/water partitioning. In addition, only four analytes (methylbenzoate, acetophenone, benzophenone, and p-chlorophenol) had residuals between 0.25 and 0.5. This illustrates that even with a diverse set of analytes, methanol is a better modifier than acetonitrile for log P estimation. From these observations, it is clear there are deficiencies with employing a larger data set for log P estimation even with log k’w extrapolation with methanol/water mobile phases. It has been observed repeatedly in the literature that the primary weakness with log P estimation based on RPLC is due to differences between a retention process and the partition process of octanol/water particularly with contributions from hydrogen-bonding interactions [135]. In addition, the retention mechanism is significantly influenced by an analyte’s shape and steric interactions, which are not applicable in a liquid-liquid distribution system [96]. As a result of these factors, RPLC log P estimation has been limited to less diverse molecular data sets often grouping by hydrogen-bonding

80 Figure 4.1: Collander plots based on the acetonitrile, methanol, and ethanol modiﬁers at ◦ ′ ◦ 30 C. The ethanol-based log kw values were extrapolated to 30 C from higher temperatures.

81 character or structural characteristics to achieve reasonable correlations for the estimation of a molecule of similar structure [136]. Due to the high correlation of literature log P values with those derived from RPLC with methanol/water mobile phases, it has been determined that ethanol/water mobile phases should provide a similar degree of correlation due to the limited differences in the structure and properties between the two alcohols. As previously discussed for ethanol modifiers, its high viscosity results in a significantly greater backpressure and reduced efficiency at ambient conditions. By increasing to moderately high temperatures of at least 50◦C, these issues are adequately minimized to allow for reasonable flow rates and separations. The addition of high temperatures, however, requires an additional factor in the extrapolation of log k’w. The method for temperature extrapolation employed in this chapter was previously described in Chapter 2 wherein, the log k’w values are determined via Equation 2.1 at each temperature. Subsequently, these values are fitted versus temperature with Equation 2.5 to generate a function to determine log k’w at a particular temperature.

For this study, the log k’w values were extrapolated using the retention factor at four ethanol compositions at each of the four temperatures studied. These values are presented in Table 4.4 with their R2 values for each temperature. The correlations were found to be very strong at 0.995 R2 for all analytes. These high correlations indicate a high degree of conﬁdence in the conclusions drawn from the data within this dissertation and its overall applicability to Green Chemistry.

The fitting parameters of slope a and intercept log k’w,0◦C for log k’w versus temperature are presented in Table 4.5. The correlations were also generally strong at 0.99 or greater, but several analytes (ethylparaben, acetophenone, and biphenyl) had a weaker thermal correlation of 0.96 or ◦ 0.97. From these fits, log k’w at 30 C were calculated and are listed in Table 4.5 along with the estimated log P values from the Collander fit. The Collander fitting parameters for ethanol/water are presented in Table 4.3. When the data was correlated with log P values, the R2 was found to be 0.978 with a slightly smaller 0.17 standard deviation of the residuals as compared to methanol. No analytes exceeded 0.5 log units in deviation with only ethylbenzene, ethylparaben, and DDE deviating between 0.25 to 0.5 log units from the literature. This high correlation demonstrates a slight improvement when using ethanol over methanol as the organic modifier. A minor difference in the estimation of log

82 ′ Table 4.4: Extrapolated log kw values for ethanol as organic modiﬁer at four temperatures with their R2 values. 2 R 1.0000 0.9999 0.9997 0.9994 0.9995 0.9980 0.9997 0.9989 0.9986 0.9999 0.9960 0.9999 0.9999 0.9989 0.9992 0.9953 0.9958 0.9964 0.9963 0.9947 0.9957 ′ w k 0.024 0.010 0.016 0.018 0.019 0.033 0.014 0.024 0.037 0.007 0.008 0.008 0.008 0.023 0.026 0.080 0.080 0.078 0.058 0.059 0.15 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± C log ◦ 3.60 80 1.141 1.564 1.970 1.047 1.670 0.628 1.090 0.657 1.682 0.280 0.280 0.954 1.206 0.792 1.870 1.945 2.183 2.174 0.882 1.142 2 R 0.9983 0.9994 0.9990 0.9999 1.0000 0.9994 0.9991 0.9991 0.9995 0.9930 0.9723 0.9973 0.9998 0.9992 1.0000 0.9949 0.9944 0.9949 0.9939 0.9947 0.9881 ′ w k 0.029 0.021 0.030 0.008 0.002 0.020 0.024 0.024 0.024 0.060 0.038 0.002 0.011 0.019 0.005 0.086 0.098 0.096 0.076 0.061 0.25 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± C log ◦ 3.73 70 1.242 1.685 2.106 1.171 1.830 0.739 1.202 0.806 1.856 0.373 0.416 1.084 1.317 0.907 2.048 2.071 2.351 2.331 1.000 1.265 2 R 0.9997 1.0000 0.9997 0.9996 0.9994 0.9999 0.9989 0.9997 0.9982 0.9989 0.9986 0.9998 0.9999 0.9992 0.9991 0.9941 0.9909 0.9950 0.9971 0.9944 0.9943 ′ w k 0.012 0.004 0.017 0.015 0.024 0.008 0.029 0.012 0.046 0.023 0.027 0.013 0.008 0.021 0.030 0.099 0.053 0.067 0.13 0.10 0.18 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± C log ◦ 2.47 2.53 4.01 60 1.331 1.793 2.236 1.234 1.940 0.813 1.292 0.854 1.971 0.445 0.480 1.211 1.412 0.989 2.168 2.249 1.090 1.418 2 R 0.9996 1.0000 0.9998 1.0000 0.9999 0.9998 0.9993 0.9985 0.9968 0.9990 0.9969 0.9997 0.9996 0.9997 0.9992 0.9942 0.9978 0.9974 0.9964 0.9977 0.9944 ′ w k 0.015 0.003 0.016 0.005 0.010 0.010 0.023 0.032 0.065 0.023 0.043 0.015 0.018 0.013 0.030 0.071 0.078 0.064 0.045 0.10 0.19 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± C log ◦ 2.44 4.21 50 1.424 1.910 2.377 1.347 2.092 0.893 1.421 0.959 2.126 0.530 0.621 1.359 1.548 1.102 2.349 2.768 2.739 1.278 1.585 DDE Phenol Anisole Toluene Analyte Benzene Biphenyl Napthalene Benzonitrile Nitrobenzene Ethylbenzene Ethylparaben Acetophenone Benzophenone Bromobenzene 3-Methylindole Benzyl Alcohol p-NitroToluene p-Chlorophenol Diphenyl Ether Methylbenzoate p-Dichlorobenzene

83 P either positively or negatively is not unexpected given that the two alcohols diﬀer by only a methylene group, which would cause ethanol to be slightly more lipophilic.

Table 4.5: The slope, intercept, and R2 values are listed for the temperature ﬁtting of ′ ′ the log kw values based on ethanol/water log kw values from Table 4.3. Log k’w values extrapolated to 30◦C and their estimated log P values are also listed.

′ ◦ 2 ◦ ′ Analyte log kw,0 C) a (x10-3) R 30 C log kw log(P) Benzene 1.893 ± 0.011 9.37 ± 0.17 0.9994 1.612 ± 0.012 2.213 Toluene 2.482 ± 0.011 11.45 ± 0.16 0.9996 2.139 ± 0.012 2.859 Ethylbenzene 3.049 ± 0.010 13.48 ± 0.15 0.9998 2.644 ± 0.011 3.479 Anisole 1.825 ± 0.052 9.63 ± 0.79 0.9867 1.537 ± 0.057 2.121 Bromobenzene 2.778 ± 0.043 13.77 ± 0.66 0.9954 2.365 ± 0.048 3.136 Benzonitrile 1.333 ± 0.037 8.69 ± 0.57 0.9916 1.073 ± 0.041 1.552 Methylbenzoate 1.953 ± 0.034 10.80 ± 0.52 0.9955 1.629 ± 0.037 2.234 Acetophenone 1.438 ± 0.087 9.5 ± 1.3 0.9629 1.152 ± 0.096 1.650 Benzophenone 2.850 ± 0.051 14.47 ± 0.77 0.9944 2.416 ± 0.056 3.199 Benzyl Alcohol 0.941 ± 0.018 8.22 ± 0.28 0.9977 0.694 ± 0.020 1.088 Phenol 1.155 ± 0.070 10.9 ± 1.1 0.9810 0.829 ± 0.077 1.253 p-Chlorophenol 2.024 ± 0.020 13.41 ± 0.31 0.9989 1.622 ± 0.022 2.225 p-NitroToluene 2.100 ± 0.037 11.22 ± 0.56 0.9951 1.763 ± 0.040 2.399 Nitrobenzene 1.606 ± 0.030 10.13 ± 0.45 0.9961 1.302 ± 0.032 1.833 Napthalene 3.120 ± 0.055 15.56 ± 0.84 0.9943 2.653 ± 0.061 3.490 p-Dichlorobenzene 3.256 ± 0.069 16.6 ± 1.1 0.9921 2.757 ± 0.076 3.618 Biphenyl 3.66 ± 0.17 18.8 ± 2.6 0.9631 3.100 ± 0.19 4.038 Diphenyl Ether 3.671 ± 0.058 18.90 ± 0.89 0.9956 3.104 ± 0.064 4.043 Ethylparaben 1.89 ± 0.094 12.8 ± 1.4 0.9757 1.51 ± 0.10 2.088 3-Methylindole 2.315 ± 0.046 14.80 ± 0.70 0.9955 1.871 ± 0.051 2.530 DDE 5.28 ± 0.13 21.4 ± 2.0 0.9831 4.64 ± 0.14 5.921

Importantly, as shown in Table 4.3 the Collander slope and intercept for the two alcohols are significantly different. This divergence demonstrates a clear deviation in the log k’w values indicating that the extrapolated mechanism for retention is distinctly different between the two modifiers. This implies that the environment within the column with a pure water mobile phase is less conducive to retention with an adsorbed layer of ethanol than with an adsorbed layer of methanol. This interpretation, however, cannot be considered definitive due to the dependence of the Synder-Soczewinski equation on retention at moderate organic compositions. Ethanol is a stronger eluent versus methanol due to ethanol’s greater capacity for dispersive interactions with

84 analytes in the mobile phase. This detail results in lower retention times for moderate ethanol compositions, which therefore results in a smaller value versus methanol for the intercept of the

Synder-Soczewinski equation - ie log k’w. This observation is irrespective of how an analyte would behave under the conditions indicated by the Synder-Soczewinski equation. In conclusion, while there is a slight improvement with estimating log P using ethanol as the modiﬁer, the slope of the Collander equation is not 1:1. This indicates that log k’w based on ethanol is not homoenergetic with the octanol/water partitioning system. Even so, the net result is that ethanol is not signiﬁcantly better or worse at estimating log P than methanol.

4.3.2 Linear Solvation Energy Relationship Comparison

In this work, Abraham’s general solvation model (as detailed in the LSER section of Chapter

3 - Section 3.1.2) was applied to log k’w and log P values to further explore the eﬀect of mobile phase on improving the estimation of log P values. To accomplish this analysis, log k’w values were regressed against the solute descriptors in Table 4.1 to generate a set of system constants.

The resulting parameters are presented in Table 4.6 for the log k’w values based on acetonitrile, methanol, or ethanol modiﬁers. In Table 4.8, the log P values presented are both from the literature ′ and estimated via the log k’w values from Table 4.6. Plots of the predicted log kw or log P values ′ versus experimental log kw or log P values are also presented at the end of the Chapter (Figures 4.4 to 4.6) to demonstrate the applicability of the model. While the directly measured log P values do not represent a retention process, the values do represent the retention of an ideal chromatographic system that perfectly matches the direct measurement methods. Comparison of the system constants between methods approximating the same environment allows for comparison of the impact organic modiﬁer has on the approximation.

Overall differences in the set of system constants for log k’w values between modifiers will therefore yield clues into how the approximated environment differs. Comparison between system constants of the log P values will provide a means of determining how effective each modifier is in estimating log P based on the structural properties of the analytes. Differences in the log P values will also indicate the kinds of analytes that would be readily estimated with a particular modifier. For the comparison of the pure water approximation, the LSER coefficients are presented in Table 4.6. As shown across the table, significant differences are observed among the three modifiers (acetonitrile, methanol, and ethanol) employed to estimate pure water retention. Larger differences

85 Table 4.6: LSER comparison of the log k’w values based on retention data for acetonitrile, methanol, and ethanol organic modiﬁers.

System Constants ACN 30◦C MeOH 30◦C EtOH 30◦C R2 0.9978 0.9935 0.9972 v 3.08 ± 0.23 3.93 ± 0.31 3.19 ± 0.27 b -3.02 ± 0.22 -2.69 ± 0.29 -3.00 ± 0.25 a -0.58 ± 0.10 -0.47 ± 0.14 -0.41 ± 0.12 s -0.42 ± 0.14 -0.84 ± 0.18 -0.92 ± 0.16 e 0.18 ± 0.20 0.53 ± 0.27 0.42 ± 0.23 c 0.15 ± 0.13 -0.39 ± 0.17 0.03 ± 0.15

are particularly notable with the v, s, and e terms. The table also presents a significant deviation in the b term. These differences further support the earlier disclaimer that log k’w represents pure water retention with an organically adsorbed stationary phase. It can thus be stated that the differences in the extrapolated retention mechanism are due to the adsorbed layers.

To further explore these differences, the three k’w systems are graphically presented in Figure 4.2 with the direct log P system obtained from the literature (Table 4.1) for comparison. Importantly, the similarities and differences with the v coefficient are readily apparent from Fig- ure 4.2. Both acetonitrile- and ethanol-based zero percent regressions have similar v coefficients, which are significantly lower than that observed for a methanol regression. Previously, it had been suggested that the greater adsorption of the less-polar acetonitrile onto the stationary phase lowers dispersive interactions of the solute with the stationary phase [81, 136]. The similarity of the v coefficients between acetonitrile and ethanol extrapolations, therefore, indicates a similar degree of stationary phase adsorption by ethanol. This conclusion is further supported by an examination of the adsorption isotherms on an ODS column by Scott and Simpson [97]. In their study, they found that ethanol had a greater coverage of the stationary phase surface at low organic content compared to methanol. This observation reveals that ethanol has a higher affinity for the stationary phase surface and therefore would adsorb more readily than methanol and possibly to a similar extent as found with acetonitrile. Both the dipolarity/polarizability (s) and excess polarizability (e) coefficients, however, demonstrate a significant similarity between 0% extrapolation based upon methanol/water and ethanol/water mobile phases. However, the extrapolation based on acetonitrile differs significantly

86 ′ Figure 4.2: Histogram comparing the LSER system constants for the log kw RPLC systems extrapolated from methanol/water, acetonitrile/water, and ethanol/water mobile phases. The coefficients for the octanol/water partition system is also provided. The standard error of each coefficient is represented by vertical error bars. The order of the systems ′ presented is the octanol/water partitioning system and the log kw extrapolated systems based on the methanol, acetonitrile, and ethanol modifiers.

87 from the two alcohol extrapolations. The most probable explanation for this observation is the fact that both alcohols have identical dipole moments (1.69 D) and similar structures while acetonitrile has a dipole moment twice as large (3.92 D). As a result of this greater dipole moment, an adsorbed layer of acetonitrile on the stationary phase would be more favorable for retention of analytes with larger dipoles. Conversely, the hydrogen-bond donating hydroxyl groups of an adsorbed alcohol layer would be more favorable for retention of analytes with a greater presence of n and π electrons. The final coefficient of interest is the hydrogen-bond basicity coefficient (b). This coefficient represents the differing capacity of the stationary and mobile phases to donating hydrogen-bonds to solutes that are hydrogen-bond acceptors. There exists a small but significant increase in the b term for methanol in comparison to the other modifiers. This difference indicates that the adsorbed methanol layer differs from the other modifiers. In particular, this means that the methanol- adsorbed stationary phase has a slightly less unfavorable hydrogen-bonding environment versus pure water than is observed with the other modifiers. What is unexpected, however, concerns an absence of a statistical difference in the b coefficient between ethanol and acetonitrile extrapolations. This result implies that an adsorbed ethanol layer confers no improvement in retention of hydrogen- bond acceptors versus an acetonitrile adsorbed layer. Another means of comparing LSER parameters is via a method presented by Ishihama and Asakawa [137], in which; the LSER coefficients are treated as a vector. With this method, the dot product of the vectors will determine the cos θ between two systems. Thus, the closer cos θ is to 1, the more similar the systems become. The cos θ values are presented in Table 4.7 for the log k’w LSER coefficients and literature log P . Based on cos θ, methanol and acetonitrile log k’w systems are somewhat dissimilar; however, ethanol log k’w is determined to exhibit greater similarity to acetonitrile log k’w than to methanol log k’w. This result is most likely due to the significant similarities in both the b and v terms for acetonitrile and ethanol log k’w systems. In directly comparing each of these extrapolated systems versus an ideal log P system (Figure 4.2), methanol demonstrates the greatest similarity for the v, s, and e coefficients. This similarity further supports the earlier 1:1 observed relationship between log k’w and log P for methanol. On the other hand, the hydrogen-bond donating term (b) and, to a lesser extent, the hydrogen-bond accepting term (a) differ significantly between the two systems. This divergence evidences that the hydrogen-bonding environment of octanol in the partition system with water is

88 Table 4.7: The cos θ values are provided for comparison of the methanol, acetonitrile, ′ and ethanol extrapolated log kw systems and the octanol/water partitioning system log P . The cos θ values are identical irrespective of the application of the Collander equation.

30◦C MeOH 30◦C ACN 30◦C EtOH 30◦C MeOH 1 30◦C ACN 0.979 1 30◦C EtOH 0.988 0.992 1 Literature Log P 0.987 0.989 0.998

signiﬁcantly more unfavorable than indicated by log k’w for methanol/water RPLC. In fact, the large, negative b coeﬃcient indicates that log P has very little hydrogen-bond acidity in comparison to water; and therefore, the other two systems are quite dissimilar in this capacity. This dissimilarity in the hydrogen-bonding component also results in ethanol’s log k’w system, and to a lesser extent, acetonitrile’s log k’w systems being more similar to octanol/water log P .

Table 4.8: LSER comparison of the literature log P versus log P values estimated using methanol, acetonitrile, and ethanol organic modiﬁers.

System Constants Literature ACN 30◦C MeOH 30◦C EtOH 30◦C R2 0.9962 0.9978 0.9935 0.9972 v 3.89 ± 0.18 4.13 ± 0.31 3.93 ± 0.31 3.91 ± 0.33 b -3.61 ± 0.17 -4.05 ± 0.29 -2.69 ± 0.29 -3.68 ± 0.31 a -0.185 ± 0.080 -0.78 ± 0.14 -0.47 ± 0.14 -0.50 ± 0.15 s -0.95 ± 0.11 -0.57 ± 0.18 -0.84 ± 0.18 -1.13 ± 0.20 e 0.59 ± 0.16 0.24 ± 0.27 0.53 ± 0.27 0.51 ± 0.29 c -0.01 ± 0.10 -0.01 ± 0.17 -0.33 ± 0.17 0.28 ± 0.18

The application of the Collander equation supports these observations. The LSER coefficients based on log P estimates are presented numerically in Table 4.8 and graphically in Figure 4.3. While the values of LSER coefficients differ before and after the Collander equation, this change only represents a shift in the scalar properties of the vector. As a result, the cos θ between the

LSER vectors of log k’w and log P for a modiﬁer will be and are equal to a value of exactly 1. This means that the cos θ values in Table 4.7 between octanol/water and each modiﬁer is identical regardless of the Collander calibration.

89 Figure 4.3: Values for each coeﬃcient in the extrapolated methanol, acetonitrile, and ethanol systems after application of the Collander equation and the octanol/water partition system. The standard error of each coeﬃcient is represented by vertical error bars. Order of the systems is octanol/water partition, acetonitrile estimated log P , methanol estimated log P , and ethanol estimated log P .

90 As shown by the comparison of the LSER values in Table 4.8, the high similarity between ethanol’s estimated log P system and the octanol/water partition system is due to a high correlation of all the LSER coefficients (excluding the a parameter). In addition, while the octanol/water comparison cos θ values are very similar for methanol-based system and acetonitrile-based system, the reason for the deviation from 1 are very different. For acetonitrile’s estimated log P system, all of the coefficients differ to a moderate degree. However, for methanol’s estimated log P system, most coefficients are very similar, but a large difference in the b parameter results in the lower cos θ value. While the improvements in the Collander fit were negligible between the two alcohols, the changes in the retention mechanism indicate that ethanol is a better estimator of log P values than methanol. For comparison, the study conducted by Benhaim and Grushka[126] respecting RPLC lipophilicity measurement with an octanol additive determined the presence of octanol in the mobile phase significantly improved both the Collander fit and correlation in LSER coefficients. Hydrogen- bonding interactions, especially hydrogen-bonding basicity, were found to still differ significantly with the octanol/water partition system. In this researcher’s work, however, the log P estimated system with the ethanol modifier did not differ significantly with the hydrogen-bond basicity term nor with any other term except for the hydrogen-bond acidity, which is a weak contributor to retention. These results imply that ethanol/water mobile phases provide a better approximation of log P on an ethylene-bridged C-18 silica stationary phase than was observed using a methanol mobile phase with an octanol additive on a grafted silica-polymer hydrid C-18 stationary phase. Finally, it should be noted that this comparison does not indicate whether the addition of an octanol additive to an ethanol mobile phase would positively or negatively impact log P estimation. Therefore, this chapter has generated a novel RPLC method that produces far better estimates of log P coefficients than observed with other RPLC methods.

4.4 Conclusions

The ﬁnal study contained in this dissertation evaluated high temperature ethanol/water mobile phases as a green eluent in the reversed-phase liquid chromatographic estimation of the octanol/water partition coeﬃcient with a silica C-18 column. Two metrics were employed to deter-

91 mine the effectiveness of high temperature ethanol/water mobile phases in comparison to traditional hydro-organic mobile phases. One metric regarded the R2 (goodness of fit) for a correlation of the ′ log kw extrapolated values for each modifier versus the octanol/water partition coefficients from the literature. A more detailed metric compared the estimated retention/partition mechanism with linear solvation energy relationship coefficients. The Collander equation was applied to determine the degree of correlation between the octanol/water partition coefficients (log P ) and the pure water retention factors (log k’w) extrapolated from high temperature ethanol/water mobile phases and then compared against similar correlations using ambient methanol/water and acetonitrile/water mobile phases. Linear solvation ′ energy relationships were then used to compare the log kw extrapolated from the three modifiers, the Collander calibrated log P estimates for the three modifiers, and the directly measured log P values taken from the literature. From the Collander correlations, the ethanol extrapolations were determined to have a similarly high estimating capability versus methanol extrapolations (0.978 versus 0.975). Acetonitrile, however, was shown to be demonstratively weaker with a Collander R2 of only 0.91. These correlations suggest that ethanol and methanol were equally effective estimators and provided a reasonable estimation of the log P for neutral aromatic and aliphatic compounds. ′ Chapter 4 also provided a closer examination of the extrapolated log kw systems and es- ′ timated log P systems with LSER analyses. Comparisons of the log kw systems demonstrated a divergence in the extrapolated system dependent upon the modifier employed. This observation ′ supports the view that log kw represents pure water retention with a modifier-adsorbed stationary phase. In particular, it was observed where the v and b terms of the ethanol extrapolated system ′ exhibited greater similarity to the log kw based on the acetonitrile modifier. This unexpected conclusion implies that the lower v term for ethanol is due to the well-supported greater adsorption of ethanol to C-18 stationary phases. The higher adsorption of the modifier has been suggested to reduce dispersive interactions of the solute with the stationary phase; thus lowering the v term and favoring elution. The less unfavorable b term of the methanol extrapolated system indicates that the relative hydrogen-bond acidity of the methanol-adsorbed stationary phase versus a pure water mobile phase is significantly greater than observed with either acetonitrile or ethanol mod-

92 ifiers. This observation implicates methanol as a significant hydrogen-bond acidity contributor to the stationary phase. Direct comparisons of the estimated log P systems versus the octanol/water partition system revealed that methanol and ethanol are equally similar in estimating the octanol/water partition system excepting the b term. In particular, it was observed that the b terms for the ethanol estimated and directly measured log P systems are not statistically different. On the other hand, the methanol estimated system’s b term exhibited large differences in magnitude. This result describes the methanol modifier as conferring significantly greater hydrogen-bond acidity to the stationary phase than is observed with ethanol. In addition, the octanol phase in the octanol/water partition system is known to have a low hydrogen-bond acidity. It can thus be concluded that versus methanol, the ethanol modifier provides a better estimation of the log P system due to the ethanol- adsorbed stationary phase possessing a similar hydrogen-bond acidity characteristic. Comparisons of the LSER coefficients as vectors against the octanol/water partition system also quantitatively expresses this correlation with a near unity cos θ for the ethanol modifier and less than 0.990 value for the other two organic modifiers. While the method employed by this work for approximating log P via high temperature ethanol/water may require more runs to complete, researchers should note the reduction in run time afforded by the increased solvent strength of both ethanol and higher temperatures in comparison to ambient methanol/water mobile phases at identical volume percents. In fact, this higher elution strength means that solvent usage per run can be more easily reduced with high temperature ethanol/water in comparison to ambient methanol/water mobile phases. More importantly, ethanol is a greener solvent versus methanol due to its lower toxicity and cleaner disposal. By using ethanol at high temperatures, this work has demonstrated that ethanol can be used as a greener substitute for methanol in octanol/water partition coefficient approximations. Additionally, from the comparison of the LSER results, there is evidence to suggest that ethanol is a significantly better eluent for approximation of the octanol/water partition coefficient via RPLC.

93 ′ ◦ Figure 4.4: Predicted versus experimental log k’ values for the log kw system at 30 C that was extrapolated from acetonitrile/water, methanol/water, or ethanol/water mobile phases. LSER coeﬃcients are described in Table 4.6.

94 Figure 4.5: Predicted versus experimental log P values for the estimated log P system at 30◦C that was extrapolated from acetonitrile/water, methanol/water, or ethanol/water mobile phases. LSER coeﬃcients are described in Table 4.8.

95 Figure 4.6: Predicted versus experimental log k’ values for the octanol/water partition system at 25◦C described in Table 4.8.

96 CHAPTER 5

SUMMARY AND CONCLUSIONS

The purpose of this work has been the exploration of high temperature ethanol/water mobile phases as a viable green alternative to traditional mobile phases in reversed phase liquid chromatography. The introduction of a variety of commercially available, high-temperature-tolerant stationary phase columns has resulted in high temperature chromatography becoming a valuable and easily employable tool for analytical chemists. Several studies have successfully applied ethanol/water mobile phases at moderately elevated temperatures (≤ 50◦C), but the impact of this mobile phase on retention has not been thoroughly explored. Thus, the application of temperatures beyond 50◦C to ethanol/water mobile phases is a relatively unexplored avenue of green chromatography. This work therefore presents the first thorough examination of the combination of these two green approaches to chromatography: (1) reduction of solvent waste with higher temperatures and (2) replacement of toxic organic modifiers with a less toxic alternative. A solid understanding of how high temperature ethanol/water mobile phases differ mechanistically with traditional hydro-organic mobile phases is necessary in order for this green method to be readily employed in RPLC. The experiments detailed in this dissertation represent the most significant work to date comparing high temperature ethanol/water mobile phases against traditional hydro-organic mobile phases in RPLC. The first experimental section (Chapter 2) explores the effects of temperature and composition on the solvent strength of ethanol/water mobile phases in comparison to other mobile phases. Retention was found to be several times more sensitive to changes in percent composition than per degree changes in temperature. This reduced sensitivity to temperature demonstrates that temperature programming is of limited utility in implementing gradient elution in liquid chromatography. The sensitivity of retention to ethanol content was also found to decrease with increasing temperatures. Such changes in sensitivity are not observed to be significant with acetonitrile/water mobile phases. This deviation between the two modifiers indicates that solvent strength sensitivity can be adapted by changing the temperature of the system. In addition, the free energy term for

97 the solvent strength of ethanol/water mobile phases was determined to not change significantly with temperature. This implies that the relative difference for the Gibbs free energy of transfer between pure water and pure ethanol mobile phases are not affected by temperature and thus the enthalpies of transfer for ethanol/water mobile phases are temperature independent. Contrasting the eluting power of various mobile phases, subcritical water was shown to be the weakest eluent with the high temperature of 160◦C corresponding to only approximately

36% ACN/H2O mobile phases. Chapter 2, however, clearly demonstrated that EtOH/H2O mobile phases at temperatures of 50◦C to 120◦C have a greater range of elutrophic strength exceeding

60% ACN/H2O mobile phases without exceeding 50% in ethanol content. In addition, even though methanol and ethanol are similar alcohols, methanol was determined to be a significantly weaker modifier. From these comparisons, ethanol/water mobile phases are shown to provide similar or greater eluting power to ambient acetonitrile/water while producing lower quantities of less toxic organic waste - an important consideration for green chemistry. Exploring these mobile phases further, Chapter 3 explores the similarities and differences between ethanol and the traditional organic modifier by examining their thermodynamic and solvatochromic relationships with RPLC retention. The results from these studies demonstrate that ethanol/water mixtures vary considerably from acetonitrile/water mobile phases and unexpectedly from methanol/water mobile phases. While the retention mechanism for methanol and ethanol mobile phases appeared to be more similar than with acetonitrile mobile phases, van’t Hoff analysis and linear solvation energy relationship analysis demonstrated considerable differences in how the mobile phase structure changes with analyte partitioning. Differences in the thermodynamics of methylene transfer were significant between the three modifiers studied (acetonitrile, methanol, and ethanol). As expected, the enthalpies for the two alcohols were more favorable in comparison to acetonitrile. However, while methanol and ethanol modifiers had similar enthalpies at the same composition, the greater solvent strength of ethanol was demonstrated to result from significant differences in their entropies of transfer, thereby implying differences in system order with analyte partitioning. Furthermore, the more unfavorable entropies of ethanol/water mobile phases versus methanol/water indicate that the presence of non-polar analytes is stabilized by ethanol molecules due to ethanol’s greater dispersive interactions with

98 the analyte. Thus, the change in entropy is more significantly dominated by unfavorable cavity formation within the stationary phase. Chapter 3 also investigated the retention mechanism with a solvatochromic analysis that employed linear solvation energy relationships. At similar values of retention for the three modifiers, it was observed that cavity formation and dispersive interactions differed significantly even between the two alcohol modifiers. These differences combined with earlier observed deviations in entropic contributions to methylene transfer indicate that even at similar eluting strength, the mobile phase structures differ significantly between the modifiers. Moreover, while differences in the other chemical interactions were small, the hydrogen-bonding basicity terms for the modifiers did not deviate to a statistical degree. This similarity implies that the differential effects of hydrogen-bonding acidity by mobile phase versus the stationary phase were indistinguishable between the modifiers. As a result of these differences and similarities, replacement of one modifier with another modifier based on solvent strength would result in observable differences in retention; however, these differences would likely be more noticeable in separations dominated by differences in molecular size than with those separations dominated by differences in the hydrogen-bonding basicity of the analytes. Additionally, LSER analyses from Chapter 3 presented significant differences in how temperature and organic content affected the retention mechanism. Increases in temperature reduced retention by weakening hydrogen-bonding interactions and reducing cohesion moderately in the mobile phase. With changes in percent composition, the mobile phase’s eluting strength increased more from greater dispersive interactions between ethanol and analyte molecules than from disruption of the hydrogen-bonding network or reduced mobile phase cohesion from weaker hydrogen-bonding interactions. This conclusion presents two differing methods for adjusting solvent strength: (1) moderately altering hydrogen-bonding acidity by changing temperature and (2) greatly modifying mobile phase cavity formation effects on retention by varying ethanol content. This result supports and clarifies differences in the relative impact on solvent strength between changes in these two system parameters that was observed in Chapter 2. The final Chapter of this dissertation evaluated high temperature ethanol/water mobile phases as a green eluent in the reversed-phase liquid chromatographic estimation of the octanol/water partition coefficient. The effectiveness of this method was determined by two metrics: the good-

99 ness of fit for the Collander equation and the correlation of linear solvation energy relationship coefficients to the octanol/water partition system. The Collander equation was applied to determine the degree of correlation between the octanol/water partition coefficients (log P ) and the pure water retention factors (log k’w) extrapolated from high temperature ethanol/water mobile phases and compared against similar correlations using ambient methanol/water and acetonitrile/water mobile phases. Linear solvation energy rela- ′ tionships were then used to compare the log kw extrapolated from the three modifiers, the Collander calibrated log P estimates for the three modifiers, and the directly measured log P values taken from the literature. From the Collander correlations, the ethanol extrapolations were determined to have a similarly high estimating capability versus methanol extrapolations (0.978 versus 0.975). Acetoni- trile, however, was shown to be demonstratively weaker with a Collander R2 of only 0.91. These correlations indicate that ethanol and methanol were equally effective and provided a reasonable estimation of the log P for neutral aromatic and aliphatic compounds. ′ Chapter 4 also provided a closer examination of the extrapolated log kw systems and es- ′ timated log P systems with LSER analyses. Comparisons of the log kw systems demonstrated a divergence in the extrapolated system dependent upon the modifier employed. This observation ′ supports the view that log kw represents pure water retention with a modifier-adsorbed stationary phase. In particular, it was observed where the v and b terms of the ethanol extrapolated system ′ exhibited greater similarity to the log kw system based on the acetonitrile modifier. This unexpected conclusion implies that the lower v term for ethanol is due to the well-supported greater adsorption of ethanol to C-18 stationary phases. The higher adsorption of the modifier has been suggested to reduce dispersive interactions of the solute with the stationary phase; thus lowering the v term and favoring elution. The less unfavorable b term of the methanol extrapolated system indicates that the relative hydrogen-bond acidity of the methanol-adsorbed stationary phase versus a pure water mobile phase is distinguishable greater than observed with either acetonitrile or ethanol modifier. This implicates methanol as a significant hydrogen-bond acidity contributor to the stationary phase. Direct comparisons of the estimated log P systems versus the octanol/water partition system conveyed that methanol and ethanol are equally similar in estimating the octanol/water partition

100 system excepting the b term. In particular, while the b terms for the ethanol estimated and directly measured log P systems are not statistically different, the methanol estimated system’s b terms exhibited large differences in magnitude. This result describes the methanol modifier as conferring significantly greater hydrogen-bond acidity to the stationary phase than is observed with ethanol. In addition, the octanol phase in the octanol/water partitioning system is known to have a low hydrogen-bond acidity. It can thus be concluded that versus methanol, the ethanol modifier provides a better estimation of the log P system due to the ethanol-adsorbed stationary phase possessing a similar hydrogen-bond acidity characteristic. Comparisons of the LSER coefficients as vectors against the octanol/water partition system also quantitatively expresses this correlation with a near unity cos θ value for the ethanol modifier with a value of less than 0.990 for the other two modifiers. As this dissertation has demonstrated, ethanol is a viable organic modifier for reversed- phase liquid chromatography when employed at temperatures of greater than 50◦C. Through the addition of higher temperatures, the elution strength of ethanol/water mobile phases is observably greater than the elution strength found with either ambient acetonitrile/water or methanol/water mobile phases. As a result, ethanol can substitute acetonitrile or methanol in RPLC separations and produce less organic waste. The reduced quantity of waste combined with the significant reduction in the toxicity of that waste reveals high temperature ethanol modifiers as a highly green alternative. The similarities between the retention mechanisms for ethanol and methanol modifiers indicates that a substitution can be performed with changes in solvent strength being the most noteworthy requirement. However, notable differences in the thermodynamics and retention mechanism between acetonitrile/water and ethanol/water mobile phases necessitates additional method development to ensure adequate and similar separations. It should be considered that substitutions of acetonitrile are less problematic with ethanol than with methanol owing to the greater similarities between acetonitrile and ethanol as organic modifiers than that observed between acetonitrile and methanol. Additionally, this dissertation revealed that ethanol provides significantly better estimations of the octanol/water partition coefficient than is observed with methanol/water mobile phases. Though the method employed in this work required more runs to determine each log P , the run times for each analyte are considerably smaller due to the higher solvent strength of high temperature

101 ethanol versus ambient methanol. In fact, this higher elution strength allows runs to be performed ′ at lower organic compositions to improve the accuracy of the log kw estimation without incurring excessive run times. In addition, ethanol is a greener solvent versus methanol due to its lower toxicity and cleaner disposal. Therefore, high temperature ethanol/water is a viable green substitute in RPLC estimations of the octanol/water partition coefficient. In conclusion, high temperature ethanol/water mobile phases have the solvent strength and lower toxicity necessary for an intriguing and enticing green alternative in RPLC. In addition to presenting a novel and effective method for log P estimation, the results of this dissertation provide the detailed examination of the retention mechanism necessary for the ethanol modifier to become a more appealing and practical option in the analytical chemist’s toolkit.

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113 BIOGRAPHICAL SKETCH

Phillip Bradford Ogden was born in Mobile, Alabama, in 1987. He graduated from Baker High School in 2005 with Honors, wherein he received over one million dollars in academic scholarships to various Universities; including a $150,000.00 NROTC scholarship and a $120,000.00 academic scholarship to the University of Miami. However, Phillip elected to remain in his hometown where he accepted the Frederick P. Whiddon Academic Scholarship and was admitted into the Honors Program at the University of South Alabama. Phillip received five top academic award distinctions during Seniors Honors Day as the top academic student across multiple disciplines including AP Calculus, AP Pre-Calculus, AP Physics, US Government/Economics, and AP English. From 2005 to 2010, Phillip performed academic research in Liquid Chromatography under Dr. Jason W. Coym. This research produced an Honors Thesis and two peer-reviewed, first author publications in the Journal of Chromatography A respecting the application of cholesterol as a mobile phase additive to improve reversed phase liquid chromatographic approximations of the octanol/water partition coefficient. In 2010, he graduated with a Bachelors of Science with Honors in Chemistry with minors in Math and Physics. This BS degree culminated with the completion of 170 semester hours of earned coursework with a perfect 4.0 GPA based on an unweighted scale. Phillip was awarded the 2006-2007 Outstanding Physics Student Award for Calculus-Based Physics. He was subsequently accepted into Who’s Who Among Students in American Universities and Colleges for 2010. During his undergraduate education, Phillip was active in the following Honor Societies: Phi Kappa Phi, Phi Eta Sigma, Alpha Chi, Alpha Epsilon Delta, National Society of Collegiate Scholars, and the National Society of Leadership and Success. After graduation, Phillip applied for only one graduate school and was accepted to the Analytical Chemistry Ph.D. Program at Florida State University while also being awarded the Hoffman Merit Award for 2010 and a Departmental Teaching Assistantship from the Department of Chemistry and Biochemistry. Phillip joined Dr. John G. Dorsey’s Group in January 2011. Phillip earned his Ph.D. in Analytical Chemistry during Summer 2016 with a 3.82 GPA.

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