Anal. Chem. 1986, 58,3091-3095 (12) Knorr, F. J.; Futrell, J. H. Anal. Chem. 1979, 5 1 , 1236. (13) Malinowski. E. R. Anal. Chem. 1877, 49, 612. (14) Malinowski, E. R. Anal. Chem. Acta 1882, 134, 129. (15) Registry of Mass Spectra; Stenhagen, E., Abrahamson, S., McLafferty, F. W., Eds.: Wiley: New York, 1974.
RECEIVED for review January 21, 1986. Resubmitted April 15, 1986. Accepted June 27, 1986. The support of the De-
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partment of Chemistry and the Division of Water Resources and Environment of The Research Institute at The University Of Petroleum and Minerals is sincerely appreciated. This paper was presented in part at the 37th Pittsburgh Conference and Exposition on Analytical Chemistry and Applied Spectroscopy, Atlantic City, NJ, March 10-14, 1986 (Paper No. 300).
Target Factor Analytical Model for Solute-Solvent Interactions in Gas-Liquid Chromatography Darryl G . Howery* and Joseph M. Soroka' Department of Chemistry, The City University of New York, Brooklyn College, Brooklyn, New York 11210
Retentlon Indexes for 7 stralght-chaln liquid-phase solvents of comparable length and 49 straight- and branched-chaln solutes are target factor analyzed. Three factors are requlred to reproduce the data near experimental error. Hexadecyl iodide Is the most unique solvent; none of the solutes are slgniflcantly unique. By use of data from the data matrlx, the best comblnatlon sets of three vectors are assoclated wlth alkane, bromide, and iodide solvents and wlth stralght-chaln alkane, branched-chaln alkane, and iodide solutes. Solute propertles such as carbon number, dlpole moment, and boiling point and solvent propertles such as dlpole moment and molecular weight target test successfully as basic factors. The best combination set of basic solute factors, lnvolvlng carbon number, total atom number, and dipole moment, glves an error less than 3 times the experimental error. The best combination set of basic solvent factors reproduces the data wlthln experhnental error. A complete target factor analytical model for the solute-solvent lnteractlons, conslstlng of properly matched pairs of the three basic solute factors and the three bask solvent factors, predicts new retentlon Indexes wlth an average error of less than 10 retention Index unlts.
Factor analysis (FA), a multivariate mathematical method for studying matrices of data, has been fruitfully applied in many areas of chemistry (1). Classical FA is particularly useful for classifying substances and for determining the number of components in multicomponent mixtures. An extended version of the method called target FA, which has capabilities for mathematically testing physically significant parameters, is used to identify factors and to build models for data. Matrices of retention indexes (r.i.) from gas-liquid chromatography involving data for a number of solutes on a number of stationary-phase solvents are well-suited for factor analysis. A review of over 20 applications of FA to chromatography is given in chapter 9 of ref 1. Recent studies involving retention on mixed stationary phases (21, models for retention indexes (3), peak overlap in chromatograms ( 4 ) ,and parallel-column separations ( 5 ) illustrate further the scope of FA/chromatography. Factor analysis furnishes solutions of the form 'Present address: Exxon Research and Engineering, P.O. Box
221, Florham Park, N J 07932.
where Iij is the retention index associated with the ith solute and the j t h solvent, n is the number of factors, m is the index for the number of factors, U,, is the mth cofactor for the ith solute, and V,, is the mth cofactor for the j t h solvent. By use of eq 1, the data matrix is the product of a raw matrix consisting of n solute factors and a column matrix consisting of n solvent factors. These two abstract matrices are calculated by using principal-component analysis after which the correct number of factors is determined. By use of a leastsquares technique called target testing, individual physically significant parameters of the solutes and of the solvents are tested as possible basic factors. Because the majority of the liquid phases used in gas-liquid chromatography are poorly characterized polymers, solvent factors that influence separations have not been extensively investigated with FA. Target tests for a few solvent properties have been reported by Weiner and Howery (6),Liao et al. (3, and Kindsvater et al. (8). Zielinski and Martire (9),attempting to determine unambiguously the effects of solvent functionality on retention, measured the retention indexes of 49 solutes on seven carefully selected solvents. The solvents are wellcharacterized, monofunctional, simple molecules of similar chain length. We report here a target factor analysis of Zielinski and Martire's data. In particular, we seek to test solvent properties and thereby to develop an overall model for the solute-solvent interactions that influence these retention data.
EXPERIMENTAL SECTION The retention indexes were measured at 45 "C by Zielinski and Martire (9),who estimated that the error in the data generally is less than 1 r.i. unit. Names and designations for the seven solvents and the 49 solutes are listed in Table I. An updated version of a computer program in FORTRAN IV developed by Malinowski, Howery, and co-workers (10) was utilized to perform the factor analysis. Computations were performed on an IBM Model 370/165 digital computer. A detailed discussion of the methodology employed is given in Malinowski and Howery's monograph ( I ) , which emphasizes the less-familar procedures of target FA. RESULTS AND DISCUSSION Reproduction. The first practical objective of a factor analysis is to determine the correct number of factors. Mathematically, a data mixture is described by a set of principal-component, abstract factors calculated using a
0003-2700/86/0358-3091$01.50/0 0 1986 American Chemical Society
ANALYTICAL CHEMISTRY, VOL. 58, NO. 14, DECEMBER 1986
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Table I. Solvents and Solutes Studied
Table 111. Effect on Reproduction of Deleting Individual Solutes and Selected Solvent Classes Using Three Factors
Stationary-Phase Solvents symbol
name
symbol
name
HDA HDE HDC HDB
n-heptadecane 1-hexadecene 1-hexadecylchloride 1-hexadecylbromide
HDI DOE DOT
1-hexadecyliodide di-n-octyl ether di-n-octyl thioether
Solutes symbol
name
symbol
1
n-pentane n-hexane n- heptane 1-pentene 1-hexene 1-heptene 1-chloropropane 1-chlorobutane 1-chloropentane bromoethane 1-bromopropane 1-bromobutane iodoethane 1-iodopropane trans-2-pentene cis-2-pentene trans-2-hexene cis-2-hexene trans-2-heptene cis-2-heptene t rans-3-heptene cis-3-heptene 2-methylbutane 2-methylpentane 3-methvl~entane
26
2 3 4
5 6 7
8 9
10 11 12 13 14 15 16 17
18 19 20 21 22 23 24
25
27
28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
43 44
name 2,2-dimethylbutane 2,3-dimethylbutane 2-methylhexane 3-methylhexane 3-ethylpentane 2,2-dimethylpentane 2,3-dimethylpentane 2,4-dimethylpentane 3,3-dimethylpentane 2,2,3-trimethylbutane 4-methyl-1-pentene 2-methyl-2-pentene 3,3-dimethyl-l-butene 2-methyl-1-hexene 5-methyl-1-hexene 4,4-dimethyl-l-pentene 2-chloropropane 2-chlorobutane
45
1-chloro-2-methylpropane 2-chloro-2-methylpropane
46 47
2- bromobutane
48 49
2-bromopropane 1-bromo-2-methylpropane 2-bromo-2-methylpropane
Table 11. Determination of Factor Size no. of factors
rms error
largest
1
5.99 0.87 0.56 0.35
22.2
2
3 4
error
5.1 2.9 1.7
70 absolute errors > 1.0
indicator function
error
98.3 18.3 8.2 1.7
0.193 0.054 0.068 0.097
6.94 1.36 1.08 0.88
real
standard eigenanalysis procedure. Two approaches, one involving a stepwise reproduction procedure and the other based on a theoretical formulation, are used to establish the correct, physically significant number of factors, Le., the factor size. In the reproduction procedure, the number of abstract factors required to reproduce the data reasonably near experimental error is determined. Successively greater numbers of abstract factors are employed to calculate the data. For example, reproduction with three factors utilizes the three most important principal-component factors. At some stage in the procedure, the data will be reproduced sufficiently near experimental error, thereby indicating the correct factor size. Results from the reproduction procedure are shown in Table 11. For the 7 X 49 matrix of retention indexes using 1-4 factors, we list the root mean square (rms) error, the largest error, the percentage of errors greater than one r.i. unit, and the values of two theoretical quantities called the indicator function and the real error. Considering the first three columns of information in the table and realizing that the upper limit of the experimental error is ca. 1r.i. unit, a three-factor model seems to best describe the data. Malinowski's indicator function ( I l ) ,based on a theory of error for abstract FA, often has a minimum value for the correct number of factors. For our data, the indicator function in Table I1 has a minimum value for two factors and only a slightly larger value for three
solvent deleted"
rrns error
solutes deleted"
rms error
HDA HDE HDI HDC DOE HDB DOT
0.55 0.55 0.47 0.51 0.44 0.55 0.50
branched chain (27) all alkenes (17) cis and trans alkenes (8) halides (16) chlorides (7) bromides (7) iodides (2)
0.54 0.53 0.56 0.40 0.52 0.49 0.55
See Table I for designations. The number of solutes deleted is given in parentheses. Table IV. Summary of Key Combination Sets Involving Typical and Basic Factors for Solvents and Solutes Using Three Factors factor type
mol. type
rms error
largest error
key factors"
typical typical basic basic
solvent solute solvent solute
0.61 0.65 0.66
4.3 2.6 2.9 8.9
HDA, HDB, HDI 1, 14, 33 U, LRD, DH NA, CN, DM
2.74
"See Table I for designations of typical factors and Tables V and VI1 for designations of basic factors. factors. The real error, calculated also from Malinowski's theory, has a value closer to the estimated experimental error for three factors than for two factors. Zielinski and Martire (9) also proposed a three-term equation to explain their data. We conclude that the correct number of factors is three. In order to estimate the relative importance of each solute and of each solvent, reproductions were carried out after deleting a single column or row from the data matrix. This modification of the reproduction procedure is useful for pinpointing molecules that contribute in a singular manner to the factor size. Removal of an important vector from the data matrix leads to a significant decrease in the rrns error for reproduction of the reduced matrix. Removal of a vector that is not responsible for unique behavior results in little or no diminution of the rrns error. The effects of removing vectors for individual solvents and for selected solute classes are summarized in Table 111. Comparing the reductions in the rrns errors in the table, we conclude that dioctyl ether and hexadecyl iodide influence the solvent-factor space slightly more than the other solvents. Among the solute groups, only the removal of the 7 bromides and of the entire group of 16 halides has any significant effect on the solute-factor space. Combination of Typical Vectors. The data matrix can be modeled with a target combination procedure using sets of rows and of columns, called typical vectors, from the matrix. In this data compression method, those sets of solvent typical vectors and of solute typical vectors which best reproduce the data matrix, called key sets, are determined. By testing all combination sets of typical vectors, taken three a t a time, quite accurate models for the data are obtained, as shown by the small rrns errors in the upper portion of Table IV. For solvents and for solutes, the key sets of three typical vectors give rrns errors (0.61 and 0.65 r.i. units, respectively) essentially equal to the rrns error for three abstract factors (0.56 in Table 11). The key set of solvent typical vectors involves the alkane, bromide, and iodide solvents. Heptadecane and hexadecene behave quite similarly, based on the solvents represented in the 10 best combination sets. A straight-chain alkane, a branched-chain alkane, and an iodoalkane are represented (see Table IV) in the key set of solute typical vectors. Comparison of the vectors in the 15 best solute combination sets indicates
ANALYTICAL CHEMISTRY, VOL. 58, NO. 14, DECEMBER 1986
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Table V. Summary of Selected Target Tests for Solvent Properties Using Three Factors designation
property
BP DM MR LRD
boiling point dipole moment molar refraction log refractive index ( N O 2 )
U
unity
UE UH UI DDd DVd DHd DAd
ether uniqueness halogen uniqueness iodide uniqueness HDA delta ( h l ~ ~ ~ ~ ) solvent group delta (6,) halide delta (A,’) alkene delta (A,’)
examde valuesn HDA HDI 576 0 81 15.7 1 0 0 0 0 0 0 0
pointsb
fitc
6 4 7 7 7 7 7 7 5 7 7 7
P
435 1.83 91 17.0 1 0 1 1 386 7.1 38 8
V
f g V
P P V
f V
g
g
See Table I for designations of solvents. *Number of test values input on the test vector. cQualitativeevaluation of the result of the target test: v = very good agreement between the test and predicted vector, g = good, f = fair (overall pattern predicted but a few points are predicted poorly), p = poor. dAverage solvent functionality vectors taken from Table I11 (DD),Table IV (DV), and Table V (DH and DA) of ref 9; Zielinski and Martire’s designations are in parentheses. that hexane and heptane are good substitutes for pentane, iodoethane for iodopentane, and 2-methylhexane for 2,4-dimethylpentane. Target Testing To Identify Basic Factors. With the target testing procedure developed by Malinowski and coworkers (12))a vector representing a property of the solutes or of the solvents can be examined as a potential basic factor. If the agreement between the input (known) test vector and the least-squares, predicted vector calculated from target FA is within reasonable expectations, then the tested property is called a basic factor. The simplest kind of target test, called a uniqueness test, makes use of a test vector consisting of a “1”for the molecule being tested for uniqueness and “On’s for all the other molecules. Any molecule that behaves differently from all of the other molecules will have a predicted uniqueness value fairly near unity on that molecule’s uniqueness test while the other molecules will have predicted values near zero. The test is useful for identifying outliers since the uniqueness test is performed for the correct number of factors. Results from uniqueness tests for each solvent and for each solute show that hexadecyl iodide (HDI) (predicted uniqueness value = 0.90) and heptadecane (0.65) are the most unique solvents. The largest uniqueness value obtained for the solutes was only 0.33 (for iodoethane). Since none of the solutes have a significantly large uniqueness value, each of the important solute factors appears to be associated with several solutes. Information from the uniqueness tests reinforces insights obtained from reproduction after deletion of a vedor and from combination of typical vectors. For example, the iodide solvent is an influential solvent from all three criteria. Removal of HDI from the data matrix decreased the rms error in reproduction to the second greatest extent, the best combination sets of typical solvent vectors included the iodide solvent, and HDI gave by far the largest uniqueness value among the solvents. In order to better understand the physical basis of the factors, vectors depicting the physical and structural properties of the solvents and of the solutes can be mathematically examined one at a time in target tests. A test vector consisting of the values of some property is input. By use of a leastsquares procedure, a corresponding predicted vector is calculated from coefficients giving the best fit between the test vector and the matrix for the first three abstract factors. If the target test is successful, Le., if the test vector and the predicted vector are reasonably similar, then the property being tested is called a basic factor. Further, properties that do not target test well (the test and predicted vectors are dissimilar) are eliminated as possible basic factors. A large
Table VI. Details of Target Tests for Three Solvent Properties Using Three Factors ether
dipole moment uniqueness solvent group 6 solvent” testb predicted test predicted test predicted HDA HDE HDC HDB HDI DOE DOT (I
0.00 0.51 1.96 1.83
-0.02 0.54 1.90 1.95 1.83 1.37 1.86
0.0 0.0 0.0 0.0 0.0 1.0 1.0
0.0 0.1 0.5 0.4 0.0 0.5 0.4
0.0 1.6 5.8 6.5 7.1 4.3 6.2
-0.1 1.8 6.0 6.5 7.1 4.1 6.1
See Table I for designations. Missing values were free floated.
number of solute and solvent properties and the square, reciprocal, and logarithm transforms of each vector were target tested. Summarized in Table V are representative target tests for solvent properties using three abstract factors. Several properties that tested successfully as well as a few which did not pass the target test criterion are included in the table. For each property, example input values on the test vector for two representative solvents, the number of test values inserted on the test vector, and a qualitative evaluation of the fit between the test and predicted vectors are listed. Test vectors do not have to be complete; missing points on test vectors can be left blank (free floated) as long as the number of test values input is greater than the number of factors. The fit is assigned, using chemical insight, from a comparison of overall pattern similarity and of a point-by-point magnitude similarity between the test vector and the predicted vector. The last four parameters in Table V are based on Zielinski and Martire’s analysis of the retention data (9). Details of the target tests for three properties, dipole moment, ether uniqueness, and solvent group 6 (a functionalgroup parameter proposed by Zielinski and Martire to measure overall interaction strengths), are given in Table VI. The fits for the first and third vectors are very good; these two properties are basic factors. The agreement between the test and predicted vectors for the ether uniqueness vector is quite poor; this property does not represent a basic factor. Representative target tests of solute properties are listed in Table VII. The last three vectors in the table are based on data from Zielinski and Martire. Details for the target tests of three solute properties, alkene uniqueness, solute group 6 (the functional-group parameter applied to the solutes), and HDE 6 (the solute functional-group contribution to the retention index on HDE calculated by Zielinski and Martire), are shown in Table VIII. The alkenes, consistent with their
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ANALYTICAL CHEMISTRY, VOL. 58, NO. 14, DECEMBER 1986
Table VII. Summary of Selected Target Tests for Solute Properties Using Three Factors designation
example values“ EtBr DMP
property
carbon numberd dipole moment enthalpy of vaporization molar refraction square of boiling point (X10-3) number of atoms number of branches alkene uniqueness solute group delta (6,) r.i. delta (AI,) HDE delta (A,x)
CN
DM HV MR SBP NA
NB UE DU‘
DIe DE‘
2.03 19.0 97.1 8 0 0 6.5
0 7.87 34.6 125.1 23 2 0 0
289 12
0
pointsb
6 31 19
49 45 49
49 49 49 14 47
fitC v g €!
g V
v P P V
P V
Example test points are given for solutes bromoethane (EtBr) and 2,4-dimethylpentane (DMP). Missing values represent free floated points. hNumber of test values input on the test vector. eSee footnote c of Table V. dBased on normal alkanes and alkenes (solutes 1-6), all other values being free floated. ‘Average solute functionality vectors taken from Table I11 (DU), Table IV (DI), and Table V (DE) of ref 9; Zielinski and Martire’s designations are in parentheses. Table VIII. Details of Target Tests for Three Solute Properties Using Three Factors alkene
uniqueness solute group 6 HDE 6 solute” test predicted test predicted testh predicted 2 5 7 10 13 14 15 16 30 33 34 45
48
0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0
0.5 0.4 0.1 0.0
0.0 1.6 5.8 6.5
0.1
7.1
0.2 0.3
7.1 1.6
0.3 0.5 0.5
1.6
7.0 7.1 6.9 1.1 1.3
0.0
0.4
0.0
0.0 1.1
0.3
0.1 0.2
1.6 5.8 6.5
0.0
1.5 6.2
5.3 6.2
0.0 3.0 11.0
12.0
0.3 2.8
10.9 12.3
12.8 12.4 3.0 3.0 0.0 0.0 3.0 10.0
11.0
2.0
2.4 0.8 0.0
1.8 9.5 11.1
“See Table I for designations. Results shown for selected soh Missina values were free floated.
lutes only.
minimal effect in the vector-deletion procedure (see Table 111), do not account for a unique factor according to the results’ in the first test. Both of the 6 parameters of Zielinski and Martire test successfully. Target Combination To Build Models. In the final stage of model building, basic factors are target tested in combination to determine which sets of basic factors best reproduce the data. Those combination sets of three basic factors which overall are most representative of the set of three abstract factors, called key sets of basic factors, are the best empirical models to describe the solute-solvent interactions. Target combination is a sensitive procedure for testing complete models. For instance, three basic factors, each of which target test with very good fits, may when used in a combination set give a large rms error in reproduction because the basic factors as a unit do not adequately span the abstract factors. In the most extreme case, if the three basic factors each unknowingly represented the same effect, then in combination two factors would be missing and the resultant model would give a very large rms error. Results from target combination involving sets of basic solvent factors and of basic solute factors are shown in the lower half of Table IV. The key set of solvent basic factors reproduces the data extremely well (rms error = 0.66 r.i. units), almost as well, in fact, as the three-factor abstract model (rms error = 0.56 from Table 11). For the solutes, the key set produces an rms error of 2.74, about 3 times experimental error. Unity (U) is the most dominant factor in the sol-
vent-combination model, being present in each of the 10 best combination sets. The unity test vector, a constant-valued vector consisting entirely of “l”’s, appears to represent the nearly constant chain lengths of the solvents. Solute factors for total number of atoms (NA) and for carbon number (CN) are especially important, being represented in each of the 10 best combination sets of solute properties. Comparison of the basic factors in the best sets shows that often a factor can be substituted for another factor without significantly altering the rms error. For example, the dipole moment and the halide 6 (DH in Table V) are redundant pairs of solvent basic factors based on this substitution criterion. With the key combination sets for both the solutes and the solvents, a complete model can be formulated if the factors are properly matched. From Table IV, the key set of basic solute factors incorporates carbon number, dipole moment, and number of atoms, while the key set of basic solvent factors incorporates the unity vector, halide 6 and the logarithm of the refractive index (a factor to which we can attach no physical meaning). Applying eq 1 and matching the factors in a physically reasonable manner, the retention index Iii for solute i on solvent j is
where the k’s are the proportionality constants for the three terms in the sum (the solute-solvent interaction terms), and the specific parameters are the key solute and solvent factors identified from target combination. Equation 2 represents a complete target FA model for the retention data. Such a complete solution to the retention index problem was not possible in earlier target FA research because the solvents were poorly characterized, complex polymers. Malinowski and Weiner (13)obtained a complete target FA solution to model the effects of solvents on the proton chemical shifts of solutes. We demonstrate the utility of eq 2 with a simple predictive procedure. Firstly, estimates are obtained for the three proportionality constants in eq 2 using known values for the retention indexes and the key basic factors of three selected solute-solvent pairs. The resulting system of three equations is then solved for the three unknown k,’s. Secondly, whenever values for all six of the basic factors for a new solute-solvent pair are known, the retention index for that pair can be predicted. To illustrate the approach, consider the following three solute-solvent pairs: pentane and HDA, 2,2-dimethylpentane and HDA, and iodopentane and HDC. For the first pair, eq 2 becomes
Writing similar equations for the remaining two solutesolvent
Anal. Chem. 1988, 58,3095-3100
pairs, and solving the three equations for the three k m k ,we find that k l = 83.73, kz = -2,263, and 123 = 30.48. Applying these results to a new solute-solvent pair, cis-3-heptene and HDI, the predicted retention index is Icis-%heptene,HDI =
(83*73)(7*1)(1)+ (-2.263)(0.356) (1.83)
+ (30.48) (21) (0.17)
= 704.7 r.i. units (experimental value = 698) The target combination model predicts the datum fairly near experimental error. Carrying out a similar calculation for 18 representative solute-solvent pairs, the average error in prediction is 9.8 r.i. units. The complete target FA model is a useful one, a satisfying conclusion considering the considerable complexity of this solute-solvent interaction problem.
ACKNOWLEDGMENT We are indebted to the Computer Center of The City University of New York for the use of its facilities. Registry No. n-Pentane, 109-66-0; n-hexane, 110-54-3; nheptane, 142-82-5;1-pentene, 109-67-1;1-hexene, 592-41-6; 1heptene, 592-76-7; 1-chloropropane, 540-54-5; 1-chlorobutane, 109-69-3; 1-chloropentane, 543-59-9; bromoethane, 74-96-4; 1bromopropane, 106-94-5;1-bromobutane,109-65-9;iodoethane, 75-03-6; 1-iodopropane, 107-08-4; trans-2-pentene, 646-04-8; cis-2-pentene,627-20-3; trans-2-hexene, 4050-45-7;cis-2-hexene, 7688-21-3;trans-2-heptene, 14686-13-6;cis-2-heptene,6443-92-1; trans-3-heptene, 14686-14-7; cis+heptene, 7642-10-6; 2methylbutane, 78-78-4; 2-methylpentane, 107-83-5; 3-methylpentane, 96-14-0;2,2-dimethylbutane,75-83-2;2,3-dimethylbutane, 79-29-8; 2-methylhexane, 591-76-4; 3-methylhexane, 589-34-4; 3-ethylpentane, 617-78-7; 2,2-dimethylpentane, 590-35-2; 2,3-
3095
dimethylpentane, 565-59-3; 2,4-dimethylpentane, 108-08-7;3,3dimethylpentane, 562-49-2; 2,2,3-trimethylbutane,464-06-2; 4methyl-1-pentene, 691-37-2; 2-methyl-2-pentene, 625-27-4; 3,3dimethyl-1-butene, 558-37-2; 2-methyl-l-hexene, 6094-02-6; 5methyl-1-hexene, 3524-73-0; 4,4-dimethyl-l-pentene, 762-62-9; 2-chluropropane, 75-29-6; 2-chlorobutane, 78-86-4; l-chloro-2methylpropane, 513-36-0; 2-chloro-2-methylpropane, 507-20-0; 2-bromopropane, 75-26-3; 2-bromobutane, 78-76-2; l-bromo-2methylpropane, 78-77-3;2-bromo-2-methylpropane, 507-19-7.
LITERATURE CITED (1) Malinowski, E. R.; Howery, D. G. Factor Analysis in Chemistry: Wllev: New York, 1980. (2) Dahlman, G.; Koser, H. J. K.; Oelert, H. H. J . Chromatogr. Sci. 1979, 17 .. , 307-313 - - . - .- . (3) Buydens, L.: Massert, D. L.; Geerlings, F. Anal. Chem. 1983, 5 5 , 738-744. (4) Gemperline, P. T. J . Chem. I n f . Comput. Sci. 1984, 24, 206-212. (5) Ramos, L. L.; Burger, J. E.; Kowalski, B. R. Anal. Chem. 1985, 5 7 , 2620-2625. (6) Weiner, P. H.; Howery, D. G. Anal. Chem. 1972, 4 4 , 1189-1194. (7) Weiner, P. H.; Liao, H. L.; Karger. 8. L. Anal. Chem. 1974, 4 6 , 2182-2190. (8) Kindsvater, J. H.; Weiner, P. H.; Klingen, T. J. Anal. Chem. 1974, 4 6 , 982-988. (9) Zielinskl, W. L.; Martire, D. E. Anal. Chem. 1976, 4 8 , 1111-1116. (10) Malinowski, E. R.; Howery, D. G.; Weiner, P. H.; Soroka, J. M.; Funke, P. T.; Selzer, R. B.; Levinstone, A. "FACTANAL", Program 320, Quantum Chemistry Program Exchange; Indiana University: Bloomington, IN, 1976. (11) Mallnowski, E. R. Anal. Chem. 1977, 4 9 , 612-617. (12) Weiner, P. H.; Malinowski, E. R.; Levinstone, A. R. J . Phys. Chem. 1970. 74. 4537-4542. (13) Weiner, 6. -H.; MalGowski, E. R. J . Phys. Chem 1971, 75, 3160-3163
RECEIVED for review September 1, 1983. Resubmitted August 7, 1986. Accepted August 28, 1986.
Detection and Spectroscopic Study of Zinc by Laser-Enhanced Ionization Spectrometry George J. Havrilla* and Kee-Ju Choi* Standard Oil Research a n d Development, 4440 Warrensville Center Road, Cleveland, Ohio 44128
Detection of rlnc by laser-enhanced lonlration spectrometry is demonstrated for both single and stepwlse excltatlon schemes Involving seven rlnc transttlons, lncludlng resonance llnes at 213.8 and 307.6 nm. Although rlnc has an lonlratlon potential of 9.4 eV, It exhlblts hlgh sensltlvlty for one-photon, hlgh oscillator strength transttlons and stepwise, low osclllator strength transltlons. Depending upon the excltatlon wavelengths utlllzed, detectlon limlts range from 10 pg/mL to 1 ng/mL. Comparison between excltatlon schemes Is given. Observation of spectral background at both steps of a stepwlse scheme Is reported.
Laser-enhanced ionization (LEI) spectrometry is a method that is based upon a two-step mechanism for the production of the ionization signal of the analyte. This two-step mechanism involves laser photoexcitation followed by collisional thermal ionization. Since the ionization step is dependent upon the thermal energy of the flame to move the atom from the excited state populated by the laser excitation, the closer the excited state is to the ionization potential the easier the ionization step will be. Until now, detection by LEI has been limited to elements with ionization potentials less than 9.2 0003-2700/86/035S-3095$0 1.50/0
eV (gold) and has used excitation wavelengths longer than 228 nm ( I ) . The present work describes the LEI detection of zinc, which has an ionization potential of 9.4 eV. In addition, we demonstrate the use of 213.8 nm for LEI spectrometry. In this report, zinc is detected directly by a laser-based method utilizing the resonance transition at 213 nm. A previous effort (2) reported zinc detection using two-photon laser-induced fluorescence. Since the 213-nm line is the strongest line in the zinc spectrum, it is the line used for most analytical emission and absorption spectrometric determinations. Several stepwise transitions have been found that have the same lower energy level as the excited level of the 213-nm transition. Comparisons will be made among the single-step and stepwise excitation schemes concerning sensitivity and selectivity for zinc determination. Of particular interest is the comparison with the second zinc resonance line at 307 nm. The transition at 307 nm has an emission intensity that is 2 orders of magnitude less than the 213-nm transition. This is expected since the transition is a spin-forbidden singlet to triplet. The sensitivity of this line should be fairly poor for LEI detection as well, since the excited state is less than half (4.1 eV) of the ionization potential. An attractive feature of the 307-nm line is that it is easier to generate; the laser energy is much higher and is less 0 1986 American Chemical Society
