Anal. Chem. 1093, 65, 193-198
193
AC RESEARCH
Optimization of pH for the Separation of Organic Acids in Capillary Zone Electrophoresis Scott C. Smith+and Morteza G. Khaledi' Department of Chemistry, North Carolina State University, Box 8204, Raleigh, North Carolina 27695-8204
The mlgratlon behavlor and optimum Separation of a set of substituted phenols Is presented. A mathematical model that descrlbes mobllity In CZE In terms of fundamental constants of each solute (acid dlssoclatlon constant K,, mobllity of the dlssoclated acid bA-)and the pH of the buffer was used to predlct the moblllty of each solute as a functlon of pH. The resolutlon between each peak pair was calculated, and a window dlagram of mlnlmum resolution as a functlon of pH was employed In determlnlngthe optimum pH. A comparlson of the predlcted and actual electropherograms shows the usefulness of thls technlque.
INTRODUCTION High-performance capillary electrophoresis (HPCE) has gained much popularity over the past decade mainly because of the combination of certain advantages that resemble, and sometimes surpass, those of HPLC. The enormous separation power of HPCE stems from the possibility of generating a very large number of theoretical plates in a short period of time.' For the separation of compounds with nearly identical electrophoretic mobility, however, the high efficiency of the technique might be inadequate. For such cases,manipulation of the buffer composition in order to enhance the selectivity of the separation has been examined.l-'O A major advantage of HPLC, on the other hand, is the feasibilityof controllingthe extent of partitioning of individual solutes (and consequently the chromatographic retention and selectivity) by changing the mobile-phase composition. The quality of the separation can easily be improved through the + Present address: Magellan Laboratories, Incorporated, P.O. Box 13341,Research Triangle Park, NC 27709. (1)Jorgenson, J. W.; Lucacs, K. D. Anal. Chem. 1981,53,1298. (2)Cohen, A. S.;Terabe, S.; Smith, J. A.; Karger, B.L. Anal. Chem. 1987,59,1021-1027. (3)Fujiwara, S.;Iwase, S., Honda, S. J. Chromatogr. 1988,477,133140. (4)Gozel, P.; Gassman, E.; Michelson, H.; Zare, R. N. Anal. Chem. 1987,59,44-49. (5)Jorgenson, J. W.; Lucacs, K. D. Science 1983,222,266-272. (6)Jelinek, I.; Dohnal, J.; Snopek, J.; Smolkova Keulemansova, E. J. Chromatogr. 1988,435,496-500. (7) Terabe, S.; Toshiyuki, Y., Nobuo, T., Mikio, A. Anal. Chem. 1989, 60,1673-1679. (8)Fanali, S.J. Chromatogr. 1989,474,441-446. (9)Guttman, A.; Paulus, A.; Cohen, A. S.; Grinberg, N.; Karger, B.L. J. Chromatogr. 1988,448, 41-53. (10)Terabe, S.; Ozaki, H.; Otauka, K.; Ando, T. J. Chromatogr. 1986, 332,211-217. 0003-2700/93/036$-0193$04.00/0
careful manipulation of one or more of the several experimental parameters such as the concentration and type of organic modifiers, pH, concentration and type of ion-pairing reagents, etc. The influence of different parameters on retention and selectivity as well as the effective strategies for prediction of retention behavior and the optimization of these parameters have been the focus of many intensive investigations."-14 The vast knowledge on the enhancement of HPLC separation selectivity by changing the solvent composition and through the use of different chemical equilibria as well as the wealth of information which is available on the methodologies for optimization of important parameters is of great use in HPCE separation. Due to the fundamentally different migration mechanisms between the two techniques, however, the exact role of these parameters on the HPCE migration behavior should be carefully examined. For example, the type of organic cosolvent that has a tremendous impact on the selectivity of retention in HPLC would not necessarily have the same effect on the selectivity of mobility in HPCE. A better understanding of the influence of various experimental parameters is essential for the prediction of migration behavior of individual solutes in a mixture and consequently for the optimization of the separation.15J6 In electrophoretic separations of ionizable compounds, pH plays an important role as it determines the extent of ionization of each individual solute. In this paper, the usefulness of a mathematical model which describes the variation of the mobility of a solute is discussed. The migration behavior of individual solutes is predicted on the basis of a few initial experiments. Consequently, the optimum pH value can be predicted using a window diagram.14
EXPERIMENTAL SECTION Apparatus. Experiments were carried out on a laboratorybuilt CE system in the same manner as Jorgenson and Lukacs.1 A0-30 KV high-voltagepower supply (SeriesEH, Glassman High (11)Schoenmakers, P. J. Journal of Chromotography Library,volume 35 Optimization of Chromatographic Selectivity-A Guide t o Method Deuelopment; Elsevier: Amsterdam, 1986. (12)Ahuja, S.Selectiuity andDetectability Optimizations in HPLC; John Wiley and Sons: New York, 1989. (13)Snyder,L.R.;Glazch,J.L.;Kirkland,J. J.PracticalHPLCMethod Deuelopment; Wiley: New York, 1988. (14)Price, W. P.;Deming, S. N. Anal. Chim. Acta 1979,108,227. (15)Khaledi, M. G.; Smith, S. C.; Strasters, J. K. Anal. Chem. 1991. 63,1820-1830. (16)Strasters, J. K.; Khaledi, M. G. Anal. Chem. 1991,63,2503. 0 1993 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY I, I993
Voltage, Inc., Whitehouse Station, NJ), a variable-wavelength UV detector (Model 200, Linear Instruments, Reno, NV) operating at 210 nm, and 50-pm-i.d., 375-mm-0.d. fused silica capillary tubing (Polymicro Technologies, Phoenix, AZ) were used. The totallength of the capillary was 43 cm, and detection was performed 30-cm downstream. The capillary temperature was controlled by jacketing in light mineral oil using a constant temperature recirculator (Type K2-R, Lauda, Germany). Electropherogramswere recorded with an electronicintegrator (Model SP4200, Spectra-Physics,San Jose, CAI. The total current was recorded on a strip chart recorder by measuring the voltage across a 1-kQresistor in series with and downstream of the capillary. Reagents and Chemicals. In addition to phenol, several substituted phenols (2-chloro-, 4-ChlOrO-, 4-bromo-, 4-fluoro-, 4-methyl-, 4-ethyl-, 4-propyl-, 4-isopropyl-, 2,5-dichloro-, 3,5dichloro-, and 2,4,5-trichlorophenol (Aldrich Chemical Co., Milwaukee, WI)) were selected as the test compounds based on three factors: water solubility, UV absorptivity, and acid dissociation constant. For the remainder of this text, these will be referenced by abbreviations that are related to the substituents H, 2C, 4C, 4B, 4F, 4M, 4E, 4P, 41,25C, 35C, and 245C, respectively. The buffer system was 1:l phosphate/pyrophosphate and was made with sodium phosphate and tetrasodium pyrophosphate (Fisher, Raleigh, NC). The disturbance in the electropherogram baseline from HPLC-grademethanol (Fisher) was used as the neutral (to) marker. Procedure. All experiments were performed with 50 mM ionic strength phosphate/pyrophosphateover the pH range 6-12. The applied voltage, 20 kV, was selected in order to keep the total current less than 60 PA. The typical sample contained approximately 0.1 mg of each substituted phenol to be tested and 0 . 1 4 2 mL of methanol in enough buffer to make the total volume 2 mL. Injection was done by gravity; the upstream end of the capillary was placed in the sample vial, which was then raised approximately 5 cm for 5-20 5. At the startof each day's experiments, the capillarywas flushed continuously for about 30 min. Flushing was performed by applying vacuum froma water aspiratorto the sealed downstream buffer flask. When not under vacuum, the flask was open to air. The capillary was rinsed with buffer for 30 s before each run. Details regarding the necessity and effectiveness of this rinsing procedure have been described previ0us1y.l~ Calculations. Electrophoreticmobility of solutes were calculated using the following equation: P = Ir, - P, = L&./V(l/t, - l/tm] where p is electrophoreticmobility, gois the overall mobility, fi0 is the electroosmotic mobility, t, is the migration time, t , is the migration time for an uncharged solute (electroosmotic flow marker), Lt is the total length of capillary, L. is capillary length between the injection and the detection, and V is the applied voltage. Wherever the term mobility is used in this paper, it is referred to electrophoreticor inherent mobility of solutes unless otherwise specified. Elution times were compiled and mobilities were calculated in spreadsheet format using QuattroPro (Borland International, Inc., Scotts Valley, CA) on a 80286-microprocessor-based PC (Compaq Deskpro 286, Austin TX). Weighted nonlinear regression was performed using SASsoftware (Cary,NC) operating on an 80386-microprocessor-based PC (Northgate 386 Super Micro, Plymouth, MN).
RESULTS AND DISCUSSION The migration behavior of a weak acid (HA) in CZE can be described by the following relationship:
where p is mobility at a given pH, PA- is the mobility of the anionic form of the acid, and K, is the acid dissociation (17) Smith, S. C.; Strasters, J. K.; Khaledi, M. G. J. Chromatogr. 1991, 586, 221.
Set 10 Set 9
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Flgurr 1. Data sets used in determining pK. and PA-superimposed over the mobiltty of Cisopropyiphenol as a function of pH. Triangular tick marks along each pseudo-x-axis indicate the pH's included in the data set; indicates the average mobility at each pH.
constant.l8 This equation predicts a sigmoidal behavior for the variation of mobility as a function of pH. If the values of the two constants (pK, and PA-)for a given solute are known, the mobility of the solute can be predicted over the entire pH range. In order to get an estimate of these parameters, the mobility of each solute was determined within the pH range 6-12. This range was chosen because the pK:s of most of the test phenols are between 8 and 10. Using weighted nonlinear (WNLIN) regression, the data was fit to eq 2. This regression procedure provides the best estimate values for the two unknown parameters (pK. and MA-) through an iterative algorithm in order to minimize the error in the fit. As a result, the best prediction of mobility a t different pH values can be obtained. A procedure for weighted linear regression can also be used as was described in a previous paper.15 Based on these methods, the migration behavior can be predicted based on the measured mobility data; no previous knowledge of the PKa and PA-are necessary. Number of Measurements RequiredTo Determine pKa and PA-. The accuracy of prediction would depend upon the accuracy of the model (eq 2), the reproducibility of data, and the proper selection of the parameter space and the number of initial experiments. One important criterion in the effectiveness of an optimization procedure is the minimum number of initial experiments that is required in order to predict the optimum conditions for separation. The quality of the estimated parameters by WNLIN depends on where along the mobility vs pH curve the data are gathered. The operating pH range should encompass the migration behavior of the fully protonated and dissociated forms in order to accurately determine pK,. At low pH, the protonated (neutral) form of the acid is predominant, thus the electrophoretic mobility asymptotically approaches 0 as the pH decreases. At high pH (> PKa + 1))the deprotonated (anionic) form is dominant and the mobility approaches PA-. The effect of the number of initial data points and the pH region on the estimation of the parameters of eq 2 is shown in Figure 1 and Table I. Examination of the data in Table I reveals that as few as four evenly-spaced p H s (set 3) are required in order to estimate accurately pK, and MA-. Unevenly-spaced data can also provide good estimates of pKaand PA-, as seen with sets 6 and 7. Sets 8-10 examine the quality of the estimate if only a small pH range (approximately 2 pH units) are considered. If the pH range is less than the pKa (set 8) or is centered about the pKa (set lo), the WNLIN estimates are poor because there is little information on the (18)Morris,C.J.0.R.;Monis,P.SeparationMethodsinBiochemistry; Interscience Publishers: New York, 1963; Chapter 18.
ANALYTICAL CHEMISTRY, VOL. 65,NO. 3, FEBRUARY 1, 1993
le5
Table 1. Comparison of Anion Mobilities and Dissociation Constants Calculated from Reduced pH Data Sets for 4-Iso~ro~ybhenol ____ set no.
number of pH Data Seta
1 13 ( 0 ~ 5 ) ~ 2 3 (3)' 3 4 (2) 4 5 (1.5) 5 7 (1) 6 7 8 9 10
estimate
95 % CI
estimate
95 % CI
.-E
10.37 11.49 10.20 10.27 10.27
0.16 7.27 0.09 0.31 0.21
1
10.38 10.27 10.89 10.22 10.42
0.38 0.33 0.91 0.25 0.46
Evenly Spaced Seta" 0.82 -14.04 -17.71 70.52 -13.71 0.28 1.06 -13.77 0.65 -13.75
Other Seta 5 (6,7,9,11,12) -13.87 5 (6,8,9,10,12) -13.75 -31.87 pHSpK, pH>pK. -13.79 p K a - l < p H > p K a + 1 -14.82
-5 -
PK.
PA-
a Po-15-
-204
0.94 1.05 50.01 0.78 4.29
8
I 7
8
9
10
12
11
PH
-
Flguo 2. Comparison of the use of literature values of pK, (- -) and the WNLIN estimate of pK. (-) In eq 2 for phenol ( 0 ) and 3,5dichlorophenol(O) relathre to the actual data.
a All pH data seta cover the range of pH 6-12. The incrementa between the evenly-spaced seta are indicated in parentheses. Represents the best estimate of the anion mobility and dissociation constant. The nonlinear regression iteration did not converge. CI = confidence interval.
*
Table 11. Comparison of Literature and Experimental Dissociation Constants of Nine Substituted Phenols at 26 OC PK. 4P 4M H 4F 4c 2c 25C 35c 245C
lit.
WNLIN estimate
10.28brc 10.26b 9.996 9.8gb 9.38b 8.40b 7.W 8.27c 7.04c
10.37 (10.22-10.53) 10.29 (10.15-10.43) 9.98 (9.92-10.04) 9.81 (9.74-9.87) 9.30 (9.21-9.38) 8.38 (8.31-8.45) 7.32 (7.24-7.40) 7.98 (7.85-8.10) 6.83 (6.73-6.93)
-251 6
7
8
9
10
11
12
PH
Flgure 3. The variation In moblllty as a function of pH for phenol and elght substituted phenols. Curves were recreated using eq 2 from WNLIN estimates of data collected at pH 8, 9, 10, and 11 at 40 O C . 0
a See the Experimental
Section for abbreviations.
Reference 20.
Reference 21. -5
1
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I
limiting mobility value, PA-. Conversely, if the pH range is greater than the PKa (set 91, the WNLIN estimates are better because PA- can be measured directly, leaving pKa as the only UnknOWn.
Estimationof pK.. Similar to HPLC, CE can be used for the determination of physical and chemical properties of solutes such as acid dissociation constants.'g Several advantages can be envisioned, such as no sample purity requirement, small sample size, and speed of analysis. Using eq 2, one can obtain an accurate estimate of the pKa of an acidic group based on as few as four measurements. The pK:s of nine substituted phenols from the literature and from WNLIN estimation are presented in Table 11. In general, there is good agreement for all monosubstituted phenols; however, the di- and trichlorophenols do not agree well. The error might lie in the source of the literature values. The pKa values of the monosubstituted phenols were taken from a reference that measured the pKa values directlyFO whereas the pKavalues of the di- and trisubstituted phenols were calculated using the substituent constants.21 According to eq 2, if the value of pKa is available, only one experiment would be needed to determine PA- and subse-
.30
-2s
-20
-1 5
-10
-5
Actual Mobility (cmz/kV mln)
Flgure 4. The correlation between predicted and actual mobllty for phenol and elght substituted phenols at pH 8.2 (01,9.5 (A),and 10.5 (0). The slope Is 1.007 wlth a correlation of 0.996 as determined linear regression.
quently predict the migration behavior of each solute over the entire pH range. This can be done by measuring mobility a t high pH (pKa + 2)) which is a direct measurement of PA-. The effect of using literature pK. values on the goodness of the fit of eq 2 as compared to the WNLIN estimates is illustrated in Figure 2. For the cases where the literature and WNLIN pKa values are close (such as phenol), the mobility vs pH curve using eq 2 closely fits the actual data. If there (19) Hirokawa,T.;Nishino,M.;Aoki,N.;Kiso,Y.;Sawomoto,Y.;Yagi, is a significant error (as is the case with 3,S-dichlorophenol), T.; Akiyama, J. J. Chromatogr. 1983,271, Dl-DlW. (20) Schultz, W.; Riggin, G. W.; Wesley, S. K. QSAR inEnuironmental the estimation of mobility could be off by as much as 2 cm2/ Toxicology-Zfi Kaiser, K. L., Ed.; Reidel Publishing Co.: New York, kV min (approximately 20%) in the region about the pK.. 1987; p 333. Prediction of Mobility. The mobilities of nine substi(21) Perrin, D. D.; Dempsey, B.; Serjeant, E. P. pK. Prediction for tuted phenols were measured at pH's 8,9,10, and 11and a t Organic Acids and Bases; Chapman and Hall: London, 1981.
196
ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY 1, 1993 n* V’-
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Flgure 5. The window diagram of the minimum resolution of the worstresolved peak pair using the mobllity data presented in Figure 3.
40 “C. (At 25 “C,the mobilities of several compounds masked the t o marker; the temperature was increased to 40 “C to increasethe mobilitiesof these phenols and improve the ability to measure to.) This pH range was selected because it was known from previous experience that several of the phenols coelute at pH less than 8, while others coelute a t pH greater than 11. After calculating pKa and PA- for each substituted phenol by WNLIN using eq 2, the mobility of all solutes were calculated as a function of pH. This is shown in Figure 3. Note that this pH range is not optimal for determining accurate estimates of the “true” pKa and PA- of all solutes; however, the WNLIN algorithm determines estimates of pKa and PA- that best fit the data in this pH range. These estimates are useful for predicting mobility within the range 8-11 but may cause erroneous results if predictions are attempted outside this range. The mobility of each solute at pH 8.2,9.5, and 10.5 was predicted using the WNLIN estimates of pKa and PA-. The correlation between predicted and actual mobilities is presented in Figure 4. The slope (1.007; ideal is 1.W)and r2(0.996) indicate there is an excellent correlation between the predicted and actual mobilities using this data gathered a t four pH’s. Optimization of Separation. The resolution between the worst-resolved peak pair was selected as the criterion for optimization and was calculated according to the following equation:1%22
than 1.) Experiments were performed at pH 10.5 to test the accuracy of this predicted optimum. The electropherogramat pH 10.5 is presented in Figure 6a and can be compared to the predicted electropherogram illustrated in Figure 6b. Agreement is generally good, but the peaks for 4C, 245C, 35C, and 25C show differences in relative retention. This may be the result of estimating pKa and PA- in an improper pH range for these solutes. The error leads to a greater minimum resolution (0.75) than predicted. Propagation of Error in PredictingMobility. The error associated with the calculation of mobility using eq 2 is the result of the error in the WNLIN estimation of pKa and PA-, the error in measuring the pH of the buffer, as well as the reproducibility of the data. An estimate of the contribution of each term can be obtained through a propagation of error. The mobility vs pH curve for a hypothetical solute is shown as the dark curves in Figure 7. The “envelopes” created by the light curves about each mobility vs pH curve indicate the relative contribution of each term (PA- and pKa). As shown, the error in mobility if smallest a t pH < pKa - 1; however, operating in this pH range is usually not practical since the solute has little or no mobility. The correlationbetween predicted and inherent mobilities, presented previously in Figure 4, can be reexamined in light of the effect of the combined standard deviations on predicted mobility. The predicted mobilities are illustrated in Figure 8 as a probability range centered about the predicted mobility to reflect the error inherent in predicting mobility. The actual mobilities from a separate set of experiments are also presented in Figure 8. Six of the nine actual mobilities fall within the predicted ranges, illustrating the accuracy of using eq 2 in conjunction with WNLIN and reasonable reproducibility to predict migration behavior. Note that the error range for the last four solutes is smaller than the range for the first five. As shown previously in Figure 3, the pKa of these solutes is well below 10.5, which is the pH a t which these predictions were made. As shown in Figure 7, when the pH is more than two units greater than the pKa, the contributions of SK. and SH+ (s = standard deviations) are negligible and the deviation in mobility is solely attributable to SPA-.
Propagated Error and Elution Order Prediction.
where N is the separation efficiency. It should be noted that within the pH range 6-12 which was used in this study, the variation of the electroosmotic mobility with pH is minimal, and therefore this factor can be excluded from the optimization process. For situations where it is necessary to use lower pH ranges, it would be essential to consider the variations of this factor with pH and its influence on resolution.l*23 The minimum resolution as a function of pH was then determined, and the resulting window diagram is shown in Figure 5. The separation efficiencyNused in eq 2 was 50 000 theoretical plates. This number is far lower than is routinely reported in the literature for CZE separations but was typical of the efficiency of our system. Although better efficiency would lead to greater resolution, the ability to predict the optimum pH would not be altered. The optimum pH, according to Figure 5, is between 10.2 and 10.8. The predicted resolution in this range is approximately 0.35. (If the separation efficiency were 500000 theoretical plates, the predicted resolution would be greater
Uncertainty in the mobilities of two solutes will lead to uncertainty in their elution order if the mobilities are relatively close. It is possible to check the degree of overlap of the envelopesas an initial screeningto see if elution order reversal may be a possibility. The error envelopes of two closelyeluting pairs of solutes are presented in Figure 9. This figure illustrates the wide variation that can exist in the error envelope (compare 35C with the others) and the high degree of overlap of error envelopes between some closely-eluting solutes (41 and 4M). This shows that over the entire pH range it is possible that the elution order of one of these pairs may be reversed: 41 and 4M below pH 10.5, and 35C and 245C above pH 10.5. The more rigorous approach to this problem is the t test for the comparison of two means.24 For these experiments, triplicate injections were made at four pH values. The t value was calculated as a function of pH (Figure 10) for the pairs 41 and 4M and 35C and 245C. In the pH regions where the curve is above the horizontal line (indicating the tabulated t value of 2.120), the elution order of the peaks can be predicted to at least 95 % confidence. This function generally agrees with the “envelope overlap” test illustrated in Figure 9. The Effect of Temperature on Separations. The data presented in Table 111illustrate the considerable difference
(22) Giddings, J. C. Sep. Sci. 1969,4, 181-189. (23) Nielsen, R.G.; Rickard, E. C. J. Chrornatogr. 1990,516, 99-114.
(24) Miller, J. C.; Miller, J. N. Statistics for Analytical Chemistry, 2nd ed.; ElliE NorwoodiWiley and Sons: New York, 1988.
(3)
ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY 1, 1993
a
b
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0.5
1
1.5
2
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Figure 6. The (a) actual and (b) predicted electropherograms of the nine phenols presented In Flgure 3. Retention times in b were determined by setting the to to that of a. 0 O1
PA-
-5-
-5
Mobility (cmYkV min)
.-
pE -10
-10-
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-25
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Figure9. The variation In mobility (center lines) and standarddevlatbn as a functlon of pH for two closely-elutlng peak palrs.
--
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Figure 10. The Student t function vs pH for the peak palrs 41 and 4M and 35C and 245C +).The elution order can be determined to 95 % confidence for the regions Inwhich the curve Is above the tabulated t value line (- -) at 2.120.
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ably because of a change in their diffusion coefficients, as described by the Einstein-Nernst equation.18 However, the increases the mobilities vary between solutes, which affects the selectivity between solutes. In addition, the downward shift in pK,'s with increased temperature leads to higher mobility at lower pH. The combination of changes in pK, and PA- can substantially alter selectivity, as seen with the elution order reversal of 3,5-dichlorophenol and phenol. Other Considerations Regarding pH Optimizationin CZE. The optimization of pH is best suited for solutes that
ANALYTICAL CHEMISTRY, VOL. 65, NO. 3, FEBRUARY 1, 1993
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Table 111. Comparison of Dissociation Constants and Anion Mobilities of Nine Substituted Phenols at 25 and 40 O C PK.
4P
4M
H 4F 4c 2c 25C 35c 245C 4P 4E 4B
25%
40°C
10.37 10.29 9.98 9.81 9.30 8.38 7.32 7.98 6.83 10.33 10.22 9.29
9.95 10.01 9.69 9.59 9.11 8.29 7.34 7.95 7.14
mobility (cm2/kVmin) 25 OC 40 "C
-16.92 -19.26 -22.59 -22.93 -22.79 -25.26 -23.47 -23.18 -22.74
-14.04 -15.54 -17.96 -17.76 -17.81 -18.97 -18.34 -16.98 -17.08 -13.87 -14.36 -17.49
-*5 -30 6
7
8
9
10
11
12
PH
Figure 12. The mobllity of twelve phenols as a function of pH at 25 O C . In addition to the nine phenols presented In Figure 3, three more are included here: 4P, 4, and 48.
See the Experimental Section for abbreviations.
-5 -
'E -10-
P E-15-
.E
g
-20-25 -
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i
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7
8
9
10
11
12
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Ftgure 11. The mobility of phenol, Clsopropylphenol, and 33dichlorophenol as a function of pH at 25 and 40 O C .
Flgurr 13. The window dlagram of the mlnlmum resolution of the worst-resohred peak pair using the mobility data of all 12 phenols (- -) and the original 9 phenols (-), from the mobility data presented In
undergo some sort of pH-dependent chemical equilibrium such as acid-base equilibria or complex formation. In addition, the separation of solutes with very similar behavior will be difficult unless an additional dimension can be employed. An example of the latter case is illustrated in Figure 12. Originally, 12 substituted phenols were selected for pH optimization. Figure 12 shows the mobility of all 12 substituted phenols as a function of pH. Note that the pairs 41 and 4P,4B and 4C, and 4E and 4M coelute over almost the entire pH range. The pK,'s and ~ A - ' s of these three pairs a t 25 "C were nearly identical, their values can be seen in Table 111. In Figure 13,the window diagram usingall 12substituted phenols is compared to the window diagram if one of the solutes in each of the coeluting pairs is removed. Removing the three solutes shifts the optimum pH and greatly improves the predicted minimum resolution. Using a second parameter will allow the optimization of solute mixtures that cannot be done by one-parameter pH optimization alone. Ideally, the technique used for the second parameter would be complimentary to the effect of pH. One
Figure 12.
-
technique that has been shown to separate uncharged solutes is MECC.25v26 Separation of uncharged solutes in the presence of micelles occurs as a result of differences in the degree of partitioning of the solutesbetween the aqueous buffer and the hydrophobic micelle c 0 r e , ~ ~ ,In 2 ~addition, micelles are mobile, so the mobilities of the solutes do not converge at 0 a t low pH. A model that describes the mobility and migration factor of ionizable solutesas a function of pH and micelleconcentration has been deri~ed.'~?~' Work is proceeding to describe and demonstrate the optimization of these substituted phenols by varying both pH and micelle concentration.
ACKNOWLEDGMENT The authors gratefully acknowledge a research grant from the National Institutes of Health (FIRST Award, GM 38738). The authors also gratefully acknowledge the use of the loan of the SP-4200integrator from Burroughs Wellcome Company.
~
(25)Terabe, S.;Otsuka, K.; Ichikawa, K.; Tsushiya, A.; Ando, T. Anal. Chem. 1984,519, 111. (26)Terabe, S.;Otsuka, K.; Ando, T. Anal. Chem. 1985,57,834-841. (27)Smith, S. C.; Khaledi, M. G. J. Chromatogr. Accepted for publication.
RECEIVED for review January 24, 1992. Revised manuscript received October 22, 1992. Accepted October 22, 1992.
