Thermodynamics of Micelleâ Water Partitioning in Micellar

Environ. Sci. Technol. 1997, 31, 2812-2820

Thermodynamics of Micelle-Water Partitioning in Micellar Electrokinetic Chromatography: Comparisons with 1-Octanol-Water Partitioning and Biopartitioning BRIAN N. WOODROW AND JOHN G. DORSEY* Department of Chemistry, Florida State University, Tallahassee, Florida 32306-4390

Micellar electrokinetic chromatography (MEKC) was evaluated as a model for biopartitioning. The thermodynamics for water-micelle partitioning are measured and compared with literature values for both biopartitioning and water1-octanol partitioning. It was found that the free energy of transfer (∆G) is dominated by the entropic term (∆S) for both water-micelle partitioning and biopartitioning, but the enthalpic term (∆H) dominates for water-1-octanol partitioning. Thermodynamic values of transfer and water-micelle partition coefficients are presented for a series of 67 solutes with varying functionalities. This work demonstrates the usefulness of MEKC for estimating biopartitioning by establishing a correlation between biopartitioning and water-micelle partitioning. Also, this method incorporates the economic advantages of capillary electrophoresis (CE), which include fast analysis times, low sample consumption, the use of aqueous buffer systems, and low organic solvent use and disposal. Solute retention for the MEKC buffer system used was evaluated through the use of enthalpy-entropy compensation plots. It was found that the mechanism of retention varied for each of four general solute classes: strong hydrogen bonding solutes, single ring weak hydrogen bonding solutes, multiple ring weak hydrogen bonding solutes, and multifunctional hydrogen bonding solutes.

Introduction The determination of a chemical’s probability of entering the food chain is extremely important to both the general public and the chemical’s manufacturer. Bioavailability and bioconcentration studies have attempted to determine the extent to which environmentally relevant chemicals enter and accumulate in the food chain through adsorption from an aqueous environment and ingestion of contaminated fish and plants by other animals. Once these chemicals begin to concentrate in the food chain, they pose a potential threat to humans as well as animal and plant life. Bioconcentration tests have typically focused on fish because of the availability of standardized test methods as well as their prevalence as a human food source. The importance of this field can be assessed simply by looking at the amount of information available on this subject in the literature (1-5). However, the amount of pollutant available for bioconcentration is not the total amount of pollutant present in the environment. In * Corresponding author e-mail: [email protected].

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general, the chemical must be in solution before it is available for bioconcentration. Pollutants associated with dissolved organic matter, bound to particulate matter, or in solid form are not generally available to biopartition (3). In addition to environmental concerns, measurement of bioavailability and bioconcentration is also applicable to the transport of pharmaceuticals in blood and ultimately partitioning into fatty tissues of the body. Partitioning occurs across membranes and is measured using estimation techniques such as the water-1-octanol partition coefficient, log KOW. (For clarity, we will refer to 1-octanol-water partitioning as water1-octanol partitioning because this designation is a better descriptor of the process being monitored even though convention refers to the process by the former designation.) Since the classic oil-water partitioning experiments of Meyer (6) and Overton (7), scientists have used this type of liquid-liquid system as a model for biological partitioning. In these systems, the oil phase represents the fatty tissue of organisms while the water phase represents an aqueous environment. Currently, the water-1-octanol partition coefficient, log KOW, is the most frequently used model. For a solute partitioning between immiscible organic and water phases, log KOW is defined as the ratio of the concentration of solute in the organic phase to the concentration of solute in the aqueous phase at equilibrium (i.e., Corg/Caq). The water-1-octanol model has been used to collect the bulk of data in the field of study known as quantitative structureactivity relationships (QSAR). QSAR studies are a way of estimating the fate of environmentally relevant chemicals from their log KOW values without performing expensive bioconcentration tests. For example, in 1985 Veith et al. (8) estimated that bioconcentration tests ranged in cost from $6000 to $10000. Bioconcentration tests have been carried out on a large number of chemicals but represent only a small percentage of the chemicals on the EPA’s list of manufactured chemicals (9). The development of models giving accurate bioconcentration and bioavailability estimates would be of great economic importance. Hansch and co-workers (10-13) in the mid-1950s to mid-1960s began work on the water-1octanol model, which today is the standard for environmental partitioning and used extensively in QSAR studies. A recently published book by Hansch et al. (14) on QSAR lists the water1-octanol partition coefficient for over 16000 chemicals. With this volume of data available in a single reference, it is easy to understand why this technique is used to such an extent that it has become the defacto standard in the environmental field for determining biological activity. Since the inception of the technique, there have been many ways of directly measuring and estimating water-1-octanol partition coefficients. Log KWO values have been measured directly using the shake flask method (10-13, 15, 16), generator columns (17), and counter-current chromatography (18). The indirect measurement of log KWO has been carried out by a variety of techniques including: solid-phase microextraction (19), reversed-phase liquid chromatography (RPLC) (20-25), and micellar electrokinetic chromatography (MEKC) (26-28). In 1988, Opperhuizen et al. (29) elegantly demonstrated the failure of water-1-octanol as a model for bioaccumulation processes. They investigated the thermodynamic properties of five chlorobenzenes partitioning between water and fish lipids and showed that biopartitioning is accompanied by positive enthalpy (∆H) and entropy (∆S) changes. In contrast, the partitioning of these compounds between water and 1-octanol is accompanied by negative enthalpy and small negative or positive entropy changes. They concluded that the differences in thermodynamic properties of these pro-

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cesses arise from the differing structures of fish lipids and octanol and that only under very specific conditions for structurally similar compounds can a relationship between water-1-octanol partitioning and bioaccumulation be expected. The thermodynamics of partitioning to membranes and bilayers have been studied in detail with the bulk of studies centering on the debate about the relative contributions of ∆H and ∆S to the free energy (30). Russell and co-workers (31) determined that the entropic term is the dominate force for peptide partitioning from water to a phospholipid bilayer. It was stressed that, while the enthalpy of transfer from water to a phospholipid bilayer does not make a trivial contribution to the free energy change, it was not the dominant force behind the incorporation of peptide into the bilayer. Jain et al. (32) found the binding of peptides to bilayer vesicles to be entropically dominated with small positive or negative enthalpy changes. Beschiaschvili and Seelig (33) also found peptide transfer to large vesicles to be an entropically dominated process. The transfer of solutes from water to membranes and bilayers has been shown to be entropically dominated as is the transfer of solutes from water to micelles (34). In this paper, we examine both water-micelle partitioning as a predictor of biopartitioning and micelles as a biomimetic agent. The thermodynamic quantities enthalpy (∆H), entropy (∆S), and free energy of transfer (∆G) are determined for a series of solutes with varying chemical functionality. The thermodynamic quantities and partition coefficients are compared with literature values for both biopartitioning and water-1-octanol partitioning. The primary goal of this work is to demonstrate that water-micelle partitioning is a better predictor of biopartitioning than is water-1-octanol partitioning.

Theory In MEKC the only partitioning process that must be considered is between the aqueous buffer system and the micellar “pseudophase”. [Here, pseudophase refers to the micelles in the capillary that migrate at a rate slower than the electroosmotic flow (EOF) of the system and thus act in the same capacity as a stationary phase in RPLC.] Water-micelle partition coefficients, KWM, are readily obtainable from the capacity factor, k′, using the relationships derived by Terabe et al. (34, 35). The electrophoretic capacity factor, k′, can be calculated from the relationship

k′ )

tr - t0 tr t0 1 tmc

[(

)]

(1)

where t0 is the migration time of an unretained solute, tr is the migration time of the solute of interest, and tmc is the migration time of the micellar pseudophase. Capacity factors can be related to the water-micelle partition coefficients by

( )

k′ ) KWM

Vmc Vaq

(2)

where Vmc represents the volume of the micelles in the buffer solution (volume of the micellar phase) and Vaq represents the volume of the water and dissolved electrolyte in the system (volume of the aqueous phase). The volume phase ratio (Vmc/ Vaq) can be calculated from the critical micelle concentration (cmc) and the partial specific volume of the micelle, υ, through the relationship

(csf - cmc) Vmc )ϑ Vaq [1 - ϑ(csf - cmc)] where csf is the surfactant concentration used.

(3)

The partial specific volume of the micellar phase is defined as the contribution the SDS makes to the total solution volume and can be calculated by determining the slope of a plot of solution density (g/mL) versus molal concentration of surfactant. The partial specific volume of the micellar solution is then calculated from the slope of the resultant plot using eq 4 (34)

ϑ)

[ ][( ) ( ) ] 1 MW

MW 1000 + b F F2

(4)

where b is the slope of the graph mentioned above, F is the density of the surfactant solution for which υ is being determined, and MW is the molecular weight of the surfactant used in the study. This value is for a specific temperature, and each temperature studied must be independently determined. The thermodynamic parameters can then be calculated from a plot of ln K versus 1/T where the data follow the van’t Hoff relationship

ln KWM ) -

∆H ∆S + RT R

(5)

In eq 5, ∆H is the enthalpy of partitioning and ∆S is the entropy of partitioning. The enthalpy of solute transfer can be determined from the slope of the van’t Hoff plot, and the entropy of transfer can be calculated from the y-intercept of the same plot, assuming that the plot is linear. The free energy of solute partitioning (∆G) can then be calculated from the thermodynamic relationship

∆G ) ∆H - T∆S

(6)

where T is temperature and is given in Kelvin. This free energy of partitioning is dependent on the temperature specified and must be calculated independently for each temperature. The calculation of the partition coefficients from the volume phase ratio, which is a molar volume ratio, in eq 2 yields partition coefficient data expressed as conventional concentration ratios and not as mole fractions. This clarification will be important in allowing the data collected here to be compared directly with literature values. Interphase Theory. Interphase theory is a bilayer structure theory proposed by Dill and Flory (36, 37) and later used by Dill and co-workers (38-46) to describe solute partitioning into micelles, monolayers, and bilayers. All of these structures are types of interfacial phases because they have high surface to volume ratios. The term “interphase” is also used to describe interfacial phases of this type. Interphases have properties that change with distance from the surface of the interphase. For example, the properties of a bilayer membrane change on going from the solution-interphase interface to the hydrophobic center of the bilayer. This type of structure can be contrasted with bulk phases (i.e., 1-octanol) that have properties that are uniform throughout the phase. According to lattice theory calculations by Dill and Flory, (36, 37), molecular organization within bilayer membranes and in the core of micelles does not resemble the all-trans crystalline state or the randomly structured liquid state of n-alkane chains. In addition, the structure does not resemble the intermediate order of a liquid-crystalline state either. This chain organization can be compared to the interphase between crystalline and amorphous regions of a semicrystalline polymer (47). The phase has regions of liquid-like disorder adjacent to regions of high order which give rise to a disorder gradient within the phase. All of this combines to give rise to a phase that has the properties of both liquids and crystalline materials simultaneously and for this reason is referred to as an interphase (36, 37). The interphase model has been extended by Dill and coworkers (37-42, 44) to describe the structure of micelles and

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vesicles. For spherical micelles, the surfactant chains are aligned radially inward with segments of the chain extending perpendicular to the radius of the sphere and staying a given distance from the center of the micelle. This prevents the super high density of chain segments that would occur in the center of a micelle if the conventional micelle representation were correct. The conventional representation has all chains fully extended radially inward from the head group and terminating at the exact center of the micelle. The Dill model contains areas of hydrophobic alkane chains on the surface of the micelle in addition to the hydrophilic areas occupied by the polar headgroups of the chains. Micelles satisfy all of the properties of interphases discussed above, with the addition of spacial constraints in the core of the micelle. Theory predicts that the core of the micelle is devoid of water and is close packed (37, 38, 41). Interphase theory indicates that the center of the micelle core is very ordered while the outer layers are more disordered and liquid like (41). This would lead to the conclusion that most solute molecules dissolved in the micelle are confined to the outer layers of the micelle core. In contrast to micellar phases, bulk phases, such as 1-octanol, can be saturated with water, which will not give a true water-1-octanol partition coefficient. Also, by using bulk phases it is assumed that thermodynamic driving forces for biological partitioning are the same as for bulk phase partitioning. Interphases, such as micelles, should provide a better model than bulk phases for measuring biopartitioning and bioaccumulation for several reasons: (1) interphases, like biological membranes, have properties that vary with distance from the solution interface; (2) the chain ordering in an interphase is more closely related to chain ordering in a membrane than in an amorphous bulk phase; (3) micelles have areas of high and low chain density like the bilayer membranes found in biological systems. Interphase theory predicts that using an interphase as the model for biopartitioning and bioavailability should give better results than the bulk phase water-1-octanol measurements currently used. Surfactant Type. Sodium dodecyl sulfate (SDS) was chosen as the micelle-forming surfactant to be used in this study. SDS has an aggregation number of 64 with a standard deviation of 13 and is the most widely used surfactant for MEKC (48). This is partially due to the low cost of the surfactant as well as the high purity in which it is available. In addition, SDS micelles have properties that make them a good chromatographic phase. First, the mobility of the micelle when under the influence of the electric field is affected very little by the pH of the solution (49). Second, during an experiment the diffusion coefficient of SDS micelles is small and thus does not contribute to a loss of separation efficiency (50). Third, the polydispersity of the SDS micelle contributes little to solute band broadening and thus has little effect on the MEKC separation efficiency (50). The major disadvantage for using SDS in MEKC is the relatively short elution range achieved when using this surfactant. The elution range is the time span between the migration time for an unretained solute and the migration time for the micellar phase. Given the discussion of interphase theory presented above, the SDS micelle should also be a good model for biopartitioning studies. SDS is a straight chain surfactant of the type used by Dill to model the structure of a micelle presented in interphase theory. The surface of the SDS micelle, while having areas of high negative charge, should also contain areas of very low charge that are occupied by alkyl groups from the surfactant chains. The ordering of the alkyl chains in the micelle should also be relatively close packed with the density increasing toward the center of the micelle. This ordered phase should constitute a good model for a biological membrane. In addition, the ordering of the surfactant chains provides an entropy-dominated partitioning process that is analogous to the partitioning process in biological systems.

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MEKC combines the speed of capillary electrophoresis with the membrane mimetic properties of micelles. Dill’s interphase theory describes the structure and solute interactions of the micelle and predicts that interphases such as micelles will be better biomimetic models than bulk phases such as a water-1-octanol system. Also, these surfactants provide micelles that form in aqueous solutions, making them better suited for studying biological processes than models that rely on the use of organic solutions.

Experimental Section Reagents. HPLC-grade methanol and 85% o-phosphoric acid were purchased from Fisher Scientific (Fair Lawn, NJ) and were used without further purification. Decanophenone was purchased from Aldrich (Milwaukee, WI). Water purified with a Barnstead Nanopure II purification system (Barnstead, Boston, MA) was used to prepare all buffers. Solutes were obtained from various sources and were used without further purification. Retention Measurements. All retention measurements were performed on a Bio-Rad BioFocus 3000 capillary electrophoresis system (Bio-Rad, Hercules, CA). Measurements were performed over the temperature range of 15-40 °C in 5-deg increments. All measurements were performed in triplicate. Capacity factors, k′, for the individual electropherograms were averaged, and the average values were used to calculate partition coefficients, K. These water-micelle partition coefficients were then used to construct van’t Hoff plots (ln K versus 1/T) from which enthalpies and entropies of partitioning were calculated. Capillary (50 µm i.d.) was obtained from Polymicro Technologies, Inc. (Phoenix, AZ). Capillary dimensions were 40 cm × 50 µm i.d. (354-360 µm o.d.). Injections were made at 2 psi/s (13.8 kPa/s), and the separation field strength used was 425 V/cm (17 kV). A 5-minute buffer rinse was used between each run and between successive sequences. Detection was performed using a scanning UV detector, and an appropriate wavelength was chosen for each solute. All solutions were filtered through a Titan 0.45-µm nylon membrane syringe filter (Scientific Resources Inc., Eatontown, NJ) prior to use. Samples were prepared in the run buffer as follows: 4 mL of buffer was added to a sample vial. To this was added 200-300 µL of a methanol solution containing a small amount of the solute and decanophenone. Methanol was added as a solubility aid for water-insoluble solutes and as an electroosmotic flow (EOF) marker. Decanophenone was added as a micelle marker. Preparation of Sodium Phosphate/SDS Buffer. The 0.020 M phosphate buffer used in this study was prepared as follows: 5.3614 g of sodium phosphate heptahydrate (dibasic), Na2HPO4‚7H2O, was placed into a 1-L volumetric flask. The flask was then filled with water, and the pH of the solution was adjusted to pH 7.0 using 85% o-phosphoric acid, H3PO4. To the phosphate buffer solution was added enough sodium dodecyl sulfate (SDS), C12H25O4SNa, to make a 0.030 M solution. For 1 L, the amount of SDS added was 8.6520 g. Determination of Partial Molal Volume of SDS. The determination of the partial molal volume was done using the method of Hutchinson and Mosher (51) as modified by Terabe and co-workers (34). All solutions were made in 20 mM sodium phosphate solution (pH 7.0). Density measurements were performed for SDS solutions of 10, 30, 50, and 70 mM. Density was measured using two pycnometers, which were made in-house. The density (g/mL) was then plotted versus the molal concentration (m) of SDS solution, and the partial specific volume of the micelle, υ, was calculated using the slope of this plot and eq 4. These measurements were made at 5-deg increments from 15 to 50 °C. The apparatus used for temperatures between 15 and 25 °C consisted of a Styrofoam waterbath containing a copper coil for heat transfer from the temperature control unit to the waterbath. The

TABLE 1. cmca and Partial Specific Volumes, (υ) of SDS Micelles reference valuesb temp. (°C)

υ (mL/g)c

cmc (mM)d

υ (mL/g)

cmc (mM)

10 15 20 25 30 35 40 45 50

0.9959 0.9967 0.9976 0.9988 1.0003 1.0019 1.0036 1.0056 1.0080

3.2 2.9 2.8 2.9 2.9 2.9 3.0 3.0 3.2

0.8562 0.8610 0.8686 0.8710 0.8758

2.9 2.5 2.6 3.0

a Critical micelle concentration. b Literature values taken from ref 33, where υ was calculated for 100 mM borate-50 mM phosphate/50 mM SDS buffer (pH 7.0) and cmc values were determined in 100 mM borate-50 mM phosphate buffer (pH 7.0). c Calculated for 20 mM phosphate/30 mM SDS buffer (pH 7.0). d Determined in 20 mM phosphate buffer (pH 7.0).

temperature was controlled by a Fisher Isotemp refrigerated circulator Model 9000 (Fisher Scientific, Fair Lawn, NJ). The apparatus used for the temperatures above 25 °C consisted of a Plexiglas waterbath with a Fisher Scientific circulator as the temperature control device. In both cases temperature was controlled to within 0.1 °C. Determination of Critical Micelle Concentrations (cmc). The cmc at each temperature to be studied was determined using conductimetric titrations. A solution of 75 mL of 20 mM sodium phosphate buffer was titrated with a solution of 100 mM SDS/20 mM sodium phosphate. The temperature was controlled using the apparatus for the low-temperature studies described in the previous section. Temperature was increased in 5 °C increments from 10 to 50 °C. Temperature was monitored by a 0.1 °C thermometer (Fisher Scientific, Fair Lawn, NJ). The conductance was monitored using a YSI Model 35 conductance meter (Yellow Springs Instrument Co., Inc., Yellow Springs, OH) with a Fisher Scientific conductivity probe (no. 13-620-156) (Fisher Scientific, Fair Lawn, NJ, USA). The cmc was taken as the break point in the slope of a plot of conductance versus molar concentration of SDS.

Results and Discussion The partial specific volumes, υ, for the SDS/sodium phosphate buffer at each temperature being used in this study were determined. Table 1 shows the experimental values obtained as well as some literature values published by Terabe and co-workers (34). The discrepancy between our υ values and Terabe’s could simply be due to differences in the buffer systems used for each study. The buffer used by Terabe consisted of a mixture of sodium phosphate (50 mM) and sodium borate (100 mM) with 50 mM SDS added. The buffer used in this study consisted of 20 mM sodium phosphate with 30 mM SDS added. cmc values for SDS were also determined in the 20 mM phosphate buffer for each temperature to be used in this study and are listed in Table 1. cmc values reported by Terabe for SDS in a borate/phosphate buffer are either lower or the same as those found in this study. This difference can be partially attributed to two factors: (1) the different makeup of the buffer system and (2) the different ionic strengths of the two buffer systems. (We understand that direct comparisons between even slightly different buffer systems are extremely difficult due to changes in ionic strength, pH, and buffer chemistry. However, the comparisons with the data of Terabe et al. are done to show that the values obtained in this study are comparable to phosphate/SDS-based CE and MEKC buffer systems, not to show that our values are correct because they are identical to published literature values.) The higher the ionic strength of the buffer, the lower the

observed cmc values. For example, in our lab we have observed a 0.3 mM increase in the cmc for SDS in a phosphate buffer by changing from a 20 mM buffer to a 16 mM buffer while the cmc value of SDS in pure water is around 8mM (52). The buffer used by Terabe was a 100 mM borate/50 mM phosphate buffer. This will have a higher ionic strength than the 20 mM phosphate buffer used in this study. Thus, the cmc values reported by Terabe should be lower than those obtained in this study. The equal values at 20 and 40 °C could simply be due to different interactions with the two buffer systems and SDS. Another interesting phenomenon is the minimum in the cmc around 20 °C. This decrease is much more dramatic in pure water than in the buffer system used here, but is still present. Attwood and Florence (53) state that this minimum typically occurs between 20 and 30 °C. They attribute the decrease in cmc with increased temperature at low temperatures to the dehydration of monomer units. Additional increases in temperature cause the water structure around the surfactant’s hydrophobic groups, which oppose micellation, to be disrupted. This results in an increase in the cmc at higher temperatures. This decrease in cmc at 20-30 °C is also a manifestation of the hydrophobic effect (52, 54, 55). Around 25 °C the hydrophobic effect is strongest and expels the nonpolar molecules from the water phase. According to Tanford (54), the hydrophobicity of these nonpolar molecules will tend to keep them in a fluid state. With surfactants this will encourage the formation of micelles at a lower overall surfactant concentration. Thus, giving a lower cmc value between 20 and 30 °C. Table 2 shows the enthalpy of transfer (∆H), the entropy of transfer (∆S), and the free energy of transfer (∆G) for 67 solutes run during this study. The solutes used in this study contain a wide variety of functionalities and therefore are arranged by compound class. In all cases, the free energy of transfer was negative in value, indicating an exothermic partitioning process. For the majority of solutes studied, the entropy term had a greater contribution to the free energy of transfer than the enthalpy term. This is in agreement with the findings of Terabe et al. (34) that solute-micelle partitioning is an entropically driven process. Using naphthalene as a test solute, we can compare our results with those of Muijselaar et al. (56), who reported an enthalpy change of -8.8 kJ/mol, an entropy change of 31.5 J/mol‚K, and a free energy change of -18.2 kJ/mol for water-micelle partitioning in MEKC. Our value for the free energy of partitioning has a smaller magnitude than that reported by Muijselaar, but the entropy and enthalpy changes follow the same trends. Both sets of values show a system with a large positive entropy change that dominates over the enthalpy contribution to the free energy of partitioning. The differences between our values and those of Muijselaar et al. (56) may be due to differences in the buffer systems and surfactant concentrations employed. Muijselaar also reports values for five other solutes that were run during this study. All of these solutes follow the same trends as naphthalene. The free energy change per methylene unit, ∆GCH2, was calculated for a plot of ∆G versus carbon number, n, for a series of alkylbenzenes and alkylphenones. The alkylbenzene series consisted of only three solutes, toluene to propylbenzene, and gave a ∆GCH2 of -2.43 kJ/mol. This is in excellent agreement with the -2.43 kJ/mol per methylene unit found by Hussam et al. (57) measured by headspace gas chromatography and in good agreement with the ∆GCH2 value of -2.23 kJ/mol found by Vitha et al. (58), also measured by headspace gas chromatography. The alkylphenone series consisted of six solutes, acetophenone to heptanophenone, and gave a ∆GCH2 of -2.25 kJ/mol. This value is in the same range as the ∆GCH2 value for an alkylbenzene methylene unit. The slight difference in values is not surprising because differences in solute orientation within the micelle due to the polar group on the alkylphenones can affect the free energy change (58).


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TABLE 2. Thermodynamic Values for Solutes Studied (Arranged by Solute Class) chemical name

log KWM (298 K)

solute class

β

∆H (kJ/mol)

∆S (J/mol‚K) [T∆S (kJ/mol)]b

∆G (kJ/mol)

-2.24 -2.31 -2.63 -2.94 -3.67 -4.81

23.86 [7.11] 29.70 [8.85] 35.28 [10.51] 41.90 [12.49] 47.86 [14.26] 52.97 [15.79]

-9.35 -11.16 -13.14 -15.24 -17.93 -20.59

-5.31 -0.91 -1.50 -1.66 1.20 -1.50

37.42 [11.15] 39.68 [11.82] 50.75 [15.12] 46.11 [13.74] 39.41 [11.74] 38.53 [11.48]

-16.47 -12.73 -16.62 -15.40 -10.55 -12.98

1.26 -0.21 0.50 -0.67 -10.65

32.85 [9.79] 26.86 [8.00] 31.01 [9.24] 39.66 [11.82] 7.55 [2.25]

-8.53 -8.21 -8.74 -12.49 -12.85

-10.45 -31.41 -2.28 -1.72 -1.93 -7.06 -19.57

24.91 [7.42] -32.96 [-9.82] 39.02 [11.63] 60.52 [18.03] 51.16 [15.25] 24.82 [7.40] 6.93 [2.10]

-17.88 -21.58 -13.90 -19.75 -17.17 -14.46 -21.64

-2.09 -7.20 -0.86 -1.84 1.46 2.79

29.75 [8.87] 8.32 [2.48] 26.43 [7.88] 55.95 [16.67] 44.42 [13.24] 32.38 [9.65]

-10.95 -9.68 -8.74 -18.52 -11.78 -6.86

-28.27 -15.19 -6.39 -1.72 -1.94

-31.43 [-9.37] 5.50 [1.64] 11.26 [3.36] 33.66 [10.03] 32.45 [9.67]

-18.91 -16.83 -9.75 -11.75 -11.61

12.34 -4.35 -5.20 -5.55 7.22 -3.74 -0.65 1.29 1.28 -4.34 -4.71 -1.18 -5.69 -2.32 -1.50 -5.14 -7.59 -1.50 -3.35 -3.86 -4.75

118.76 [35.39] 66.59 [19.84] 62.97 [18.77] 60.72 [18.09] 104.19 [31.05] 34.92 [10.41] 43.25 [12.89] 89.45 [26.65] 83.26 [24.81] 61.78 [18.41] 64.68 [19.28] 71.44 [21.29] 54.40 [16.21] 61.30 [18.27] 34.96 [10.42] 58.36 [17.39] 40.33 [12.02] 60.98 [18.17] 46.21 [13.77] 48.41 [14.43] 40.31 [12.01]

-23.05 -24.19 -23.97 -23.65 -23.83 -14.14 -13.54 -25.36 -23.56 -22.75 -23.99 -22.47 -21.90 -20.59 -11.92 -22.53 -19.61 -19.67 -17.12 -18.29 -16.76

-14.27 -19.95 -15.37 -15.19 -10.32 -16.11

6.73 [2.01] 2.24 [0.67] 17.81 [5.31] 4.84 [1.44] 28.21 [8.41] -0.99 [-0.30]

-16.27 -20.61 -20.68 -16.64 -18.73 -15.81

-7.86 -7.41 -6.60 6.34 -1.97

52.72 [15.71] 55.84 [16.64] 44.91 [13.38] 104.32 [31.09] 71.38 [21.27]

-23.57 -24.05 -19.98 -24.74 -23.24

Alkylphenones acetophenone propiophenone butyrophenone valerophenone hexanophenone heptanophenone

1.64 ( 0.000 1.96 ( 0.005 2.31 ( 0.011 2.71 ( 0.011 3.14 ( 0.011 3.61 ( 0.015

0.49 0.43

tert-butylbenzene ethylbenzene 1-methylnaphthalene n-propylbenzene toluene p-xylene

2.89 ( 0.008 2.23 ( 0.012 2.91 ( 0.004 2.70 ( 0.013 1.85 ( 0.003 2.27 ( 0.013

0.12 0.12 0.16 0.12 0.11 0.12

m-cresol o-cresol p-cresol 1-naphthol 2-naphthol

1.49 ( 0.001 1.44 ( 0.003 1.53 ( 0.001 2.18 ( 0.007 2.25 ( 0.006

acenaphthene anthracene azulene bibenzyl biphenyl naphthalene phenanthrene

3.13 ( 0.006 3.77 ( 0.003 2.43 ( 0.009 3.46 ( 0.006 3.01 ( 0.005 2.53 ( 0.006 3.79 ( 0.005

0.17 0.20

p-chloronitrobenzene o-nitroaniline nitrobenzene 2,3,5,6-tetrachloronitrobenzene 1-nitrohexane 1-nitrobutane

1.92 ( 0.003 1.62 ( 0.001 1.53 ( 0.001 3.24 ( 0.003 2.06 ( 0.002 1.20 ( 0.003

0.26 0.31 0.30

acridine carbazole indole 3-methylindole 1-methylindole

3.30 ( 0.010 2.95 ( 0.005 1.70 ( 0.002 2.06 ( 0.004 2.03 ( 0.004

aldrin R-chlordane γ-chlordane DDT DDE 1,3-dichlorobenzene 1,4-dichlorobenzene endrin heptachlor heptachlor epoxide hexachlorobenzene pentachloroanisol pentachlorobenzene pentachloronitrobenzene pentachlorophenol 1,2,4,5-tetrabromobenzene 1,2,3,4-tetrachlorobenzene 1,3,5-tribromobenzene 1,3,5-trichlorobenzene 2,3,4-trichloroanisol 1,2,4-trichlorobenzene

4.02 ( 0.011 4.22 ( 0.004 4.18 ( 0.004 4.15 ( 0.021 4.18 ( 0.003 2.48 ( 0.014 3.37 ( 0.005 4.44 ( 0.005 4.12 ( 0.017 4.01 ( 0.015 4.20 ( 0.003 3.93 ( 0.007 3.85 ( 0.002 3.61 ( 0.010 2.08 ( 0.005 3.92 ( 0.017 3.45 ( 0.005 3.45 ( 0.004 3.00 ( 0.010 3.20 ( 0.004 2.94 ( 0.005

cortisone β-estradiol 17R-estradiol hydrocortisone lidocaine prednisone

2.79 ( 0.001 3.62 ( 0.010 3.62 ( 0.015 2.92 ( 0.008 3.28 ( 0.006 2.77 ( 0.008

chlorpyrifos coumaphos fenthion oxadiazon ronnel

4.13 ( 0.016 4.17 ( 0.045 3.49 ( 0.011 4.37 ( 0.005 4.07 ( 0.025

s s s s s s

Alkylbenzenes w w w2 w w w

Alcohols and Phenols 0.34 0.34 0.34 0.33 0.33

s s s s s

Polycyclic Aromatic Hydrocarbons w2 w2 w2 w2 w2 w2 w2

0.22 0.20 0.15 0.20

Nitro Compounds w s w w s s

Aromatic Nitrogens 0.44

s s s s s

Halogenated Pesticides w2 w2 w2 w2 w2 w w w2 w2 w2 w w w w w w w w w w w

0.26

0.00

0.30 0.02 0.02 0.02 0.00

Steroids mf mf mf mf mf mf

Organophosphorus Pesticides

a

mf mf mf mf mf

s, strong; w, single ring weak; w2, multiple ring weak; mf, multifunctional. b T∆S values were calculated for 298 K.

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TABLE 3. Comparison of Thermodynamic Values for Water-Micelle Partitioning with Literature Values for Water-Fish and Water-1-Octanol Partitioning (in kJ mol-1) at 292 K for Five Chlorobenzenes water-fisha

water-micelle

water-1-octanola

compound

∆GWM

∆HWM

T∆SWM

∆GWF

∆HWF

T∆SWF

∆GWO

∆HWO

T∆SWO

1,3-di 1,3,5-tri 1,2,3,4-tetra penta hexa

-13.94 -16.84 -19.37 -21.57 -23.60

-3.74 -3.35 -7.59 -5.69 -4.71

10.20 13.49 11.78 15.88 18.90

-26.3 -29.5 -31.7 -34.2 -36.6

6.0 10.5 13.5 16.0 17.9

32.3 40.0 45.2 50.2 54.3

-25.0 -29.4 -31.0 -33.6

-15.0 -21.7 -26.3 -30.8

10.0 7.7 4.7 2.8

a

Literature thermodynamic values taken from ref 29.

Table 3 compares the thermodynamic values from watermicelle partitioning with water-fish and water-1-octanol partitioning, respectively, for a series of five chlorinated benzenes. The data in Table 3 have been recalculated to correspond to a temperature of 292 K to give a more accurate comparison with literature data. As can be seen from Table 3, the main contribution to the free energy of transfer in water-fish partitioning is the entropy term while the main contribution to the free energy for water-1-octanol partitioning is the enthalpy term. Opperhuizen and co-workers (29) have determined that biopartitioning, represented here by water-fish partitioning, is an entropically dominated process while water-1-octanol partitioning is an enthalpically dominated process. The water-micelle partitioning data show a larger contribution to the free energy from the entropy term than from the enthalpy term. These data indicate that water-micelle partitioning for this series of chlorinated benzenes is an entropically dominated process. Watermicelle partitioning is not as entropically dominated as fishwater partitioning, but this is to be expected given that the structure of phospholipid bilayers is different from that of micelles. According to interphase theory, both micelles and bilayers are represented by the interphase model because (1)they have properties that vary with distance from the solution interface, (2) their chain ordering should disfavor solute partitioning relative to bulk phases, and (3) the partitioning to the these structures should decrease as chain density increases (37-42, 44). However, the bilayers used in the interphase model are composed of a single type of monomer, while cell membranes can be composed of proteins, steroids, and other biological molecules in addition to phospholipids. When extending the theory described for bilayers to SDS micelles, it must be remembered that SDS micelles have a large negative charge while biological membranes are composed to a large extent of zwitterionic and neutral lipids and contain only a small percentage of molecules with a net negative charge. This difference between micelles and fish cell membranes may account for the differences in the entropic contribution to the driving force of partitioning. With respect to water-1-octanol partitioning, the data show that there is a clear difference in the thermodynamic driving force when compared to water-micelle partitioning. The enthalpic contribution to the free energy of partitioning is dominant for water-1-octanol partitioning, while the entropy term is dominant for water-micelle partitioning. This finding is again not surprising given the differences in the structure of a micelle and the structure of a bulk phase, such as 1-octanol. Figure 1, plots a and b, shows the plots of T∆SWM versus T∆SWF and T∆SWO, respectively. The correlation coefficients (r) obtained for these two plots, 0.797 for Figure 1a and 0.586 for Figure 1b, are marginal at best, but this can be explained by the small data set for each plot (n ) 5 and n ) 4, respectively). However, Figure 1 does demonstrate that the data from water-micelle partitioning correlates better with water-fish partitioning data than with water-1-octanol

FIGURE 1. (a) Open squares represent a comparison of the entropy term from water-micelle partitioning with the entropy term from water-fish partitioning; the correlation is 0.797, n ) 5. (b) Filled diamonds represent a comparison of the entropy terms from watermicelle and water-1-octanol partitioning; the correlation is 0.586, n ) 4. partitioning data. As can be seen from Figure 1, the two plots have different slopes. At first glance this seems counterintuitive but upon close examination of the data is justifiable. Figure 1a states that the entropy for water-fish partitioning increases as the entropy for water-micelle partitioning increases. Figure 1b states that the entropy for water-1octanol partitioning decreases as the entropy for watermicelle partitioning increases. Table 3 shows this trend more clearly. When examining the T∆S data (entropy multiplied by a scalar of 292 K) as a function of the number of ring substitutions, it can be seen that entropy increases for both water-micelle and water-fish partitioning and decreases for water-1-octanol partitioning along the same series. The partition coefficients at 298 K for the five chlorobenzenes discussed above are also presented in Table 2. These partition coefficients are at a slightly different temperature than any of those reported by Opperhuizen et al. (29). However, Opperhuizen reports values at 292 and 301 K for both water-fish and water-1-octanol partitioning. These values were compared with the values for water-micelle partitioning at 298 K. These plots are shown in Figure 2. The log K values for water-fish and water-1-octanol were compared at both 292 and 301 K. These plots, Figure 2a,b, both show reasonable linear correlation with the plot at 292 K having the better correlation. Figure 2c,d shows the correlation of log KWM with the values for log KWF at both 292 and 301, respectively. Both of these plots show that the log


9

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FIGURE 2. Correlation plots for log KWF-log KOW and for log KWFlog KWM are shown for 292 and 301 K. The correlation coefficients for the plots are as follows: a ) 0.990, b ) 0.967, c ) 0.997, d ) 0.995. Linear least squares fit equations are given in the text. KWF values correlate better with log KWM values than with log KWO values. From the plots in Figure 2, this observation seems to be independent of the small temperature differences between the partition coefficients. Also, the log KWF-log KWM relationships have slopes and intercepts closer to unity than the log KWF-log KWO relationships. The linear least squares fit equations (n ) 5) for the four plots in Figure 2 are listed below: Figure 2a Figure 2b Figure 2c Figure 2d

y ) 0.62464 + 0.88450x y ) 0.79419 + 0.89486x y ) 1.1733 + 1.0485x y ) 1.2137 + 1.0448x

r 2 ) 0.990 r 2 ) 0.967 r 2 ) 0.997 r 2 ) 0.995

These plots also support the findings from the comparison of thermodynamic data that water-micelle partitioning is a better descriptor of biopartitioning than the water-1-octanol model. In addition, the plots in Figure 2 suggest that the partition coefficients from water-micelle partitioning could be used as a substitute for water-1-octanol partition coefficients when performing biopartitioning experiments and QSAR studies. However, additional work needs to be done to verify this prediction. The equilibration of solutes between the aqueous phase and the organic phase has long been a problem faced by researchers using methods such as the shake flask method to determine partition coefficients. This problem raises the same questions about equilibration times for water-micelle partitioning: Is the equilibration time for water-micelle partitioning longer than the time required to conduct an MEKC experiment? The answer to this question lies in the formation and deformation constants of the micelle and has been studied for SDS micelles. Micelles are dynamic entities with finite lifetimes that are kinetically controlled. However, there are actually two kinetic processes involved in micelle formation. The first is micelle formation-deformation, which occurs on the order of milliseconds, and the second is the exchange of monomers with the solution, which occurs on the time scale of about 10 µs (53). Aniansson et al. (48) have determined that the lifetime for SDS micelles is 100-1000 ms while solute residence time ranges from a few to 250 µs. For an equilibrated system to exist, the solute must have a fast enough solute sorption-desorption rate constant to allow

2818

9


partitioning into and out of the micelle within the micelle’s lifetime. This rate constant should be no slower than the micelle formation-deformation rate. Almgren et al. (59) determined that the desorption rate constant for perylene (which is more hydrophobic and would have a larger partition coefficient that the solutes used in this study) is 4.1 × 102 s-1 while the desorption rate constant for benzene is 4.4 × 106 s-1. These sorption-desorption rate constants are indicative of the time required for equilibrium to be reached in the micellar system, and therefore an equilibrium state is considered to be established very rapidly (35). Also, it has been found that micelle exit rates for solutes parallel solubility and thus distribution coefficients (50, 59). This agrees with MEKC data because very hydrophobic solutes, such as perylene, which co-elute with the injected micelles, will not desorb from the micellar phase and will not reach equilibrium with the aqueous phase. Thus, these extremely hydrophobic solutes cannot be studied using this technique. However, the distribution coefficients for the solutes used in this study should give some indication to the equilibration state of the system. Those solutes that have not reached equilibrium should have large standard deviations for the distribution coefficients. This was not observed in the present study because the relative error of the distribution coefficients shown in Table 2 had a maximum value of 1.08%. Taken as a whole, this data indicate that an equilibrium state is reached for the solute in the micellar system during a typical MEKC experiment. Enthalpy-entropy compensation is an extra-thermodynamic approach to analyze physicochemical data (60), which according to Melander et al. (61) appears as a linear dependence of the free energy changes on the corresponding enthalpy changes for similar physicochemical processes. This approach is referred to as an extra-thermodynamic method because it cannot be rigorously grounded in thermodynamic principals. Enthalpy-entropy compensation has been used extensively to determine if solutes are retained by similar mechanisms in RPLC (61-65). A plot of ∆H versus ∆S is constructed for a series of solutes, and the linear correlation is evaluated to determine if compensation exists in the system. If compensation does exist, then the retention mechanism is considered to be the same for the solutes in question. Solutes yielding plots with correlation coefficients below 0.5 are interpreted as having different retention mechanisms, but coefficients of 0.7 or higher are considered likely to have the same retention mechanism (61, 64, 66-69). The probability of similar retention mechanisms increases with increasing correlation coefficient. The compensation temperature, which is equal to the slope of the enthalpy-entropy compensation plot, is the temperature where ∆H equals T∆S and the Gibbs free energy change goes to zero (60). According to Boots (60), compensation used in this context refers to the case where entropy and enthalpy changes are independent of temperature in the range of temperatures used to determine the compensation temperature and including the compensation temperature itself. However, it is pointed out that the compensation effect can still occur even if the enthalpy and entropy changes depend on temperature. If this is the case, then the compensation temperature will be temperature dependent also. The compensation temperature has been used to aid in determining the driving force of partitioning in MEKC. Terabe et al. (34) reported for a series of solutes studied using MEKC that the compensation temperature was smaller than the corresponding compensation temperature from RPLC. They attributed this finding to the greater importance of the entropic contribution in micellar solubilization than in partitioning to RPLC bonded phases. This has also been determined for a series of alkylphenones run during this study. Kikta et al. (62) found that for a series of six alkylphenones that the compensation temperature ranged from 460 to 512

hydrogen bonders, 0.7153, n ) 17; single ring weak hydrogen bonders, 0.8634, n ) 21; multiple ring weak hydrogen bonders, 0.8913, n ) 16; and multifunctional hydrogen bonders, 0.9466, n ) 11. It should be noted that two solutes were omitted from the single ring weak hydrogen bonding solutes. These solutes were the two substituted anisols listed in Table 2. It was found that these solutes do not follow the same retention mechanism as the other solutes in this class. This is possibly due to the character of the anisol group, which is a methoxy (-O-CH3) substituted benzene. The methoxy group is a very strong electron donator. This is in contrast to the other solutes in this class that have electron withdrawing groups (-NO2 and halogens) substituted onto the benzene rings. Before exclusion of the anisols, the correlation coefficient for this class was 0.4608, and after their exclusion it rose to the 0.8634 value reported above.

FIGURE 3. Enthalpy-entropy compensation plot for 65 solutes from Table 2. The solutes are divided into four classes. Open circles denote multifunctional hydrogen bonding solutes; the correlation is 0.9466, n ) 11. Open triangles denote multiple ring weak hydrogen bonding solutes; the correlation is 0.8913, n ) 16. Single ring weak hydrogen bonding solutes are denoted by [ with a correlation of 0.8634, n ) 21. Strong hydrogen bonding solutes are denoted by * with a correlation of 0.7153, n ) 17. K depending on the RPLC column used, while we determined a compensation temperature of 83 K using MEKC. This finding, coupled with Terabe’s explanation, gives further evidence for the conclusion that water-micelle partitioning is an entropically dominated process. Enthalpy-entropy compensation plots have been constructed for the solutes run in this study. It was found that when all 67 solutes were included in a single plot a correlation coefficient of 0.6175 was obtained. This low value makes it very questionable if the same retention mechanism is involved in the partitioning of all solutes in the series. The solutes in Table 2 were divided into strong and weak hydrogen bonding as well as multifunctional hydrogen bonding solutes in an attempt to determine which solutes were partitioning via the same retention mechanism. The hydrogen bonding ability of the solutes in Table 2 were determined using available literature values for the hydrogen bond accepting (HBA) basicity parameter, β (70-73). The solutes were classified as weak or strong HBA solutes using the following generalization: β values in the range 0.0 e β e 0.30 were classified as weak while β values in the range 0.30 < β e 0.7 were classified as strong. Those solutes for which there were no β values found were classified using the observed trends for other molecules in their respective solute classes. For example, amines, aldehydes, ketones, and phenols were all found to be strong HBA solutes while PAHs, alkylbenzenes, and many halogenated pesticides were found to be weak HBA solutes. This technique was employed for the small molecules used in this study, but the large multifunctional solutes were grouped together and left unclassified. The multifunctional hydrogen bonders consist of the solutes in the steroid and organophosphorus pesticide classes listed in Table 2. These solutes contain more than one functional group and have a very high affinity for the micelle. The weak hydrogen bonding solutes were divided into two subunits, consisting of solutes with one ring system and solutes with multiple ring systems. This series of divisions dramatically improved the correlations for the enthalpy-entropy compensation plots (Figure 3). The improved correlation coefficients were as follows: strong

The enthalpy-entropy compensation data suggest that there are multiple retention mechanisms responsible for retention in MEKC. It would appear that the retention mechanism changes depending on the size of the hydrophobic group present (i.e., one ring versus multiring systems), the number of functional groups present, and whether or not the solute has the ability to strongly hydrogen bond. Given the micelle structure proposed by Dill and co-workers (37, 41) in which there are both pockets of hydrophobic and hydrophilic character on the micelle surface, these results are not surprising. Multifunctional solutes, such as steroids, have hydrophobic and hydrophilic areas, which can simultaneously bind to the micelle via separate mechanisms giving them a higher affinity for the micelle. This would explain the large partition coefficients observed for these solutes that almost co-elute with the micelle marker in MEKC. Also, the strong hydrogen bonding solutes would interact differently with the micelle than purely hydrophobic solutes, such as the PAHs, which are part of the weak hydrogen bonding series. Previously, Herbert and Dorsey (27) have shown that capacity factors from MEKC do correlate well with water1-octanol partition coefficients for a series of over 100 solutes with varying chemical functionality. Given the widely varying chemical functionality of the solutes used by Herbert and Dorsey (27), the reason for obtaining this good correlation (r 2 ) 0.835) is not well understood but is probably caused by the same phenomenon that enables the water-1-octanol model to give reasonable biopartitioning estimations for functionally similar compounds even though it has been shown by Opperhuizen (29) that the water-1-octanol model is not thermodynamically relevant for biopartitioning, which it is designed to model. We have shown that water-micelle partitioning determined via MEKC is a more thermodynamically correct model with respect to the enthalpic and entropic contributions to the free energy of transfer than is the water1-octanol model. Water-micelle partition coefficients, used directly, should provide a better model of bioavailability and bioconcentration than forcing a fit between KMW and the water-1-octanol partition coefficient. Work is in progress in our lab to determine the effects of using a mixed micelle system as a model for biopartitioning. The elucidation of retention mechanisms is important for determining correct models for biological processes. If the mechanism(s) for micelle partitioning was known, it could be modified using mixed micellar systems or other means to more closely match the biopartitioning process being studied.

Acknowledgments The authors would like to thank the Air Force Office of Scientific Research and the National Institutes of Health (GM48561) for funding. J.G.D. also thanks Merck Research Laboratories for continued support of our work.


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Received for review January 15, 1997. Revised manuscript received June 23, 1997. Accepted June 23, 1997.X ES9700313 X

Abstract published in Advance ACS Abstracts, August 15, 1997.

Thermodynamics of Micelleâ Water Partitioning in Micellar

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