2820
Anal. Chem. 1986, 58, 2820-2824
Identification of Organic Mixture Components without Separation: Quantitative and Edited Carbon- 13 Nuclear Magnetic Resonance Spectrometry Data for the Analysis of Petroleum Distillates David A. Laude, Jr., and Charles L. Wilkins*
Department of Chemistry, University of California, Riverside, California 92521
A new method for the analysls of unknown mlxtures whlch Incorporates unlque features of quantitative and edlted ‘‘C nuclear magnetic resonance (NMR) spectra Is descrlbed. Chromatographicseparalbn and subsequent ldentlflcatbn by match with a spectroscopic data base spectrum Is not required for quantltative analysts of mixture components. Instead, equlvalent Informath Is obtalned by creating subspectra from resonances with equlvalent peak areas wlthln a quantitative NMR spectrum. IdentHbaUon Is accompRshed by comparison of edited spectra with mu#ipric#y data derlved dlrectly from the chemical structure of potentlal components. Analysis of petroleum drstWlates wlthhr the class of low bofflng (C,-C,) hydrocarbon compounds is Considered here. A 207-component library Is created and utilized In the analysls of two fractlons for which 7 and 14 compounds are successfully identlfied.
In general, analysis of organic mixtures is a two-step procedure requiring isolation of mixture components followed by identification of the pure materials. Computer-assisted methods employing chromatographic separation, followed by detection using infrared or mass spectrometers, which provide information-rich spectra for library searches of spectral data bases, are common. However, gas chromatography/mass spectrometry (GC/MS) and gas chromatography/infrared spectrometry (GC/IR), which are among the more successful implentations, suffer from a number of shortcomings. GC separation is not always possible because of sample nonvolatility or lack of sufficient chromatographic selectivity. Furthermore, present spectral libraries are incomplete and often contain spectra obtained under nonstandard conditions. In this paper an alternate approach to mixture analysis, incorporating unique features of 13Cnuclear magnetic resonance (NMR) spectrometry to eliminate the need for chromatographic separation and library data bases, is described. Instead, individual components are “separated” within a quantitative 13CNMR spectrum of the mixture and identified by comparison of quantitative and multiplicity-containing 13C NMR data with a library of “predicted” NMR spectra, derived solely from chemical structure. Initial studies of the method as applied to the analysis of petroleum distillates were successful; a total of 7 and 14 components were unambiguously identified for two low-boiling fractions. The separation of components by 13C NMR is dependent upon the equivalent response achieved for all carbon nuclei under quantitative NMR conditions. Under these conditions, peak areas for all resonances within a molecule must equal some integer multiple of the area corresponding to a single nucleus. The minimum number of components in a mixture then quickly can be determined from the quantitative spec-
trum by counting the number of peak subsets with dissimilar peak intensities. The reliability of such a procedure is dependent upon the accuracy of peak integration and minimization of peak overlap. The contributions to integration error from both spectral and data processing parameters are well characterized so that, depending upon signal-to-noiseratio ( S I N ) ,errors can be reduced to 1-4% (1-3). Increased dispersion a t higher magnetic fields coupled with narrow linewidths of decoupled 13C resonances permits some certainty in the separation of peaks even for moderately complex mixtures of low molecular weight compounds. The well-defined relationship between 13CNMR data and chemical structure is ideally suited to the development of computer-aided identification schemes. Library search algorithms ( 4 4 , pattern recognition techniques (7, 8), and spectral simulation (9-11) are among the categories of analysis methods that exploit the unique features of 13C NMR. The proposed identification scheme combines features of spectral simulation and library retrieval in an algorithm that emphasizes quantitative and multiplicity information. Chemical shift data are deemphasized because, despite the success of spectral simulation based upon library retrieval or parametric methods, there is uncertainty in the assignment of shift values to unknowns. The effects of sample environment on chemical shift contribute to the complexity of any procedure attempting to simulate 13C NMR spectra. In contrast, quantitative and multiplicity data are extracted solely from chemical structure, independent of sample environment, yet define much about a compound’s carbon backbone and symmetry. Simulated libraries of predicted multiplicity data are easily constructed from structural tables and, provided the unknown is contained in the library, permit certain identification of the unknown with simple library search algorithms. The inferior information content of multiplicity libraries, when compared to chemical shift data, increases the ambiguity of compound identification because essentially only information about the carbon backbone is considered. Application of the technique is therefore limited to the analysis of mixtures from well-defined smaller libraries of several hundred components. This paper considers, as an example, the set of low-boilinghydrocarbons likely to be collected from petroleum fractions. A library of quantitative and multiplicity data was created for 207 C, to C7 hydrocarbons likely to be found in the fractions. To decrease redundancy among compounds in the library some chemical shift information was also considered; all carbons were subdivided by chemical bond type with single bonds designated for resonances below 70 ppm, triple bonds designated for resonances between 70 and 100 ppm, and double bonds designated for resonances above 120 ppm. Although these data are determined empirically rather than directly from chemical structure, certainty of assignment based upon the type of compounds being considered permits their inclusion.
0 1986 American Chemical Society 0003-2700/86/0356-2820$01.50/0
ANALYTICAL CHEMISTRY, VOL. 58, NO. 13, NOVEMBER 1986
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Table I. Composition of 207-Component Simulated Hydrocarbon Library C,
c5
compound type
no.
pairs
triplets
noncyclic alkanes noncyclic alkenes noncyclic alkynes noncyclic dienes noncyclic diynes cyclic alkanes cyclic alkenes cyclic alkynes cyclic dienes
3 5 3 3 2 1
0 0 0 0 0 0 0
0 0 0 0 0 0
totals
1 1
0 0
20
0
1
no.
pairs
triplets
no.
5
20 8 6
0
0 0 0 0 0 0 0 0 0
8 23
2 5 2 7
0 0 0 2 1 0 0 0 1
0
56
4
0
131
0 0
EXPERIMENTAL SECTION Three hydrocarbon mixtures were selected to test performance of the proposed analysis scheme. A three-component mixture containing by volume 35% 3-methylpentane, 44% 1-heptane, and 21 % 2-methyl-1-butene was used to demonstrate the technique. Two petroleum distillates with boiling point fractions of 35-60 "C and 30-75 OC, obtained from Aldrich, were also characterized. The lower boiling fraction was previously analyzed by GC/MS and confirmed by NMR to contain primarily seven C5 and C6 hydrocarbons. The higher boiling fraction, although uncharacterized, could be assumed from its boiling point range to contain only C, through C, hydrocarbons. Both petroleum fractions were diluted 1:l in CDCl, for the analysis. A Nicolet 300-MHz wide-bore superconducting magnet with unmodified commercial 5-mm 13Cprobe was used in all experiments. All data processing was performed with standard Nicolet-developed software. Quantitative spectra were obtained with a gated decoupled pulse sequence to ensure suppression of nuclear Overhauser effects (NOE). Spin-lattice relaxation was leveled ~ )a by adding 0.05 M chromium triacetylacetonate ( C r ( a ~ a c )as relaxation reagent. A recycle time of 10 s between scans was found to permit quantitative measurement of samples. The total experiment time varied depending upon the level of detection required, but to achieve a S I N of 10 to 1 for components at -1 mg in the observe region, run times of 2-4 h were necessary. To ensure accuracy in processing of quantitative data, at least 8-10 points should define each peak. This easily can be achieved by applying a line broadening parameter and zero-filling, although this is often deleterious when maximum resolution is required to base line resolve all spectral peaks. Inaccuracy in software integration also occurs for small peaks with base lines distorted by nearby large resonances. A cut-and-weigh procedure is often more reliable in these cases. Despite these difficulties, the errors in integrated areas are less than 2 % for peaks with SIN of 20-100. Peaks with S I N outside this range have errors of up to 5-10%. Multiplicity data were obtained from edited spectra acquired with the distortionless enhanced polarization transfer pulse sequence (DEPT) (12). A proton-carbon coupling constant of JC+, = 132 Hz was assumed for all samples. To obtain the required multiplicity data, DEPT spectra were acquired at 8 = 3x/4 and 8 = 1r/2. At 8 = 3x14, the CH3 and CH resonances are upright, the CH2 resonances are inverted, and quaternary carbons are suppressed. At 8 = x/2 the CH resonances appear and all other nuclei are suppressed. CH2and CH resonances were determined directly from 8 = 3x14 and x/2, respectively,while CH3resonances are inferred by their presence at 0 = 3x14 and absence at 8 = a/2. Quaternary carbons were inferred by their presence in the quantitative 13C NMR spectra and absence at 8 = 3a/4. A total of three 13C NMR spectra were required for each analysis, with total experiment time dependent upon the amount and concentration of sample available. Edited spectra were rapidly acquired, not only because of the signal enhancement afforded by polarization transfer but also because quantitative conditions are unnecessary. Even at low milligram levels, spectra were obtained within an hour. Quantitative experiments were more time-intensive because of the need to level TIand NOE values in the mixture. Recycle times in excess of 1 min would prohibit data acquisition at the 1-mglevel: however, addition of relaxation reagents substantially decreases recycle times, at the expense of
13 7
10 5
13
19 10 24
C, pairs 0 5 2 2 0 0 2 1
triplets 0 0 0 1
3
1 0 2 0 4
15
8
increased linewidths ( I ) . In nonsample limited cases, as with the petroleum fractions, flow NMR is superior to relaxation reagents, offering decreased recycle times without severe line broadening (3, 13). RESULTS AND DISCUSSION Simulated Library. Essential to the success of the proposed algorithm is the creation of a predicted library containing all possible components to be found in the mixture to be analyzed. For example, in the analysis of low boiling petroleum distillates to be described, only saturated and unsaturated C6, C6, and C7 hydrocarbons are assumed to be present in the mixtures. A total of 207 compounds found t o meet the constraints imposed are included in the library; Table I is a breakdown of the compounds by size and functional group. Table I1 lists a subset of the library containing all C5 hydrocarbons and includes the multiplicity data used to identify each component. Although the library was created by inspection of individually drawn structures, the creation of larger libraries would be facilitated by an automated topological procedure. As evident from Table 11, three sources of information are used to create library entries from the structure alone. In addition to multiplicity and degeneracy data obtained by inspection, the strong correlation between 13C NMR chemical shift and bond type in hydrocarbons permits further distinction of carbon nuclei attached to double or triple bonds. This seemingly limited information is sufficient to uniquely identify 70% of the library compounds. Each of the 19 pairs and 8 triplets with identical library features are from a particular compound type, thus assuring a t the least some information as to the compound's identity. As an example, compounds I, 11, and 111,which yield the equivalent library information, l , l / l , l / l , l * / l * , are all classified as dimethylcyclopentenes. (Note: line notation efficiently describes the information for each compound in the simulated library by separating multiplicities with slashes, CH,/CH,/CH/Q, and assigning an asterisk to double-bonded carbon resonances and a superscript x t o triple-bonded resonances).
I
I1
I11
Search Algorithm. Simulated 13C NMR library analysis mimics the evaluation of GC/MS data by a reverse library search. As seen in Figure 1, quantitative and edited NMR data are first processed to determine relative peak areas and multiplicities arising from hydrogen coupling. Separation of carbon resonances by compound is performed by using quantitative data t o determine peak areas with ratios corresponding to equivalent carbons within a molecule. In particular, a minimum difference in peak intensities is arbitrarily
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ANALYTICAL CHEMISTRY, VOL. 58, NO. 13, NOVEMBER 1986
Table 11. Subset of Simulated 13C NMR Data Base Containing C5 Hydrocarbons
mu1tip1icit no. 1 2
3 4 5
6 7 8
9 10 11 12
13 14 15 16 17 18 19 20
name
no. of carbons
no. of NMR peaks
pentane methylbutane 2,2-dimethylpropane 1-pentene 2-peptene 3-methyl-1-butene 2-methyl-2-butene 2-methyl-1-butene 1-pentyne 2-pentyne 3-methyl-1-butyne 1,3-pentadiene l,4-pentadiene 2-methyl-1,3-butadiene 1,3-pentadiyne 1,4-pentadiyne cyclopentane cyclopentene cyclopentyne cyclopentadiene
5
3
CH,
CH, 2s 1
4 2
5 5
l,l,l*
4 4
1*
5 5 5
1,1*
1
191 1
4 5
1* 2*,1 1*,1*
3 5 5 3
1
5 2s 2J
1
3 3 3
1
9
CH 1
1* 1*,1* 1,1* 1*
1 2
1* 1*
1'
11 1=,1x
l',l
1 '
1*,1*,1* 2* 1*
1 ' 2'
1* 1x,1=,1= 2'
2* 2" 2*,2*
An asterisk corresponds to a double bond, an x corresponds to a triple bond. ACQUIRE OUANTITATIVE I3C SPECTRUM
ACQUIRE EDITED
PEAK AREAS
MULTIPLICITIES
I SELECT
1
RANK PEAKS
AT RATIOS OF
SEARCH SIMULATED LIBRARY FOR
TOLERANCE
A M B IGUOUS MASK IDENTIFIED PEAKS
Flgure 1. Flow chart of logic for 13C NMR mixture analysis algorithm.
chosen (1% for this work), and beginning with the largest peak, a set of resonances with ratios within the tolerance window are selected. The simulated library is subjected to a reverse search procedure in which all possible subsets of library multiplicity data are determined. Should a match not occur, the tolerance is increased and the process repeated. Use of an increasing tolerance window, as opposed to a constant maximmum value applied to all data, significantly decreases the number of possible matches within the library. Thus, a single subset match to the library typically is obtained. Should multiple subset matches occur within a tolerance window, progression of the algorithm will ultimately eliminate ambiguities by requiring a conflicting resonance be present in a subsequent match. Ideally, the algorithm should match all resonances collected into subsets so that a unique group of compounds matches the quantitative and multiplicity data
of the original mixture. Certainly, peak overlap or poor signal-to-noise ratio will contribute increasingly to errors in peak identification for less abundant components in the mixture. However, this does not detract from the reliability of search results for more abundant mixture components. As with any limited data set, certain unique features of the multiplicity library contribute to a minimization of search time. Several features specific to the C5-C7 hydrocarbon set are useful: because each compound must contain five to seven carbons, a subset is easily classified as containing too few peaks, or perhaps as a combination of several compounds; an examination of the library yields only three single-resonance compounds (the cycloalkanes), four with two resonances, and 18 with three resonances; a carbon ratio of 1:4 occurs for just two compounds and must include a CH3 multiplicity; a ratio of 1:3 occurs 11 times and must include three CH3 and one Q resonances. Although particular to this 207 hydrocarbon set, utilization of comparable information for other mixtures will improve the speed and reduce the complexity of analysis for the algorithm. Three-Component Analysis. To demonstrate the method, a model three-component mixture containing 44% 1heptene, 35% 3-methylpentane, and 21 % 2-methyl-1-butene was analyzed. The three I3C NMR spectra shown in Figure 2 contain a total of 16 resonances including five methyl, eight methylene, two methine, and one quaternary carbons. These data are summarized in Table 111, along with a separation of resonances in accord with the algorithm outlined in Figure 1. Three independent subsets with window tolerances of 1, 4, and 5% were isolated and subjected to a search of the library. For each of the three subsets a unique search result is obtained. In particular, peaks 1,2,10, and 11in a 1:1:2:2 ratio suggest a C6 hydrocabon; peaks 3-9 are of equal intensity and are indicative of a C, hydrocarbon; peaks 12-16 are of equal intensity and suggest a C5 hydrocarbon. The correct library match for peaks 12-16, with a multiplicity of l,l/l,l*/O/l*, is actually present in Table 11, position 8, and is correctly identified as 1-methyl-2-butene. In similar fashion, the other subspectra are found to uniquely represent 3-methylpentane and 1-heptane. Petroleum Distillates. A more rigorous test of the simulated library algorithm involved the examination of two petroleum distillates with boiling point ranges that suggested
ANALYTICAL CHEMISTRY, VOL. 58, NO. 13, NOVEMBER 1986
2823
a)
'riI
b)
d
1
d, 140
100
60
20
ppm
Figure 2. 13C NMR spectra of three-component mixture including (a) quantitative spectrum with 100 scans acquired at a recycle time of 10 s, (b) DEPT spectrum with JC-H = 132 Hz and 6 = 37/4to invert CH, resonances and suppress quaternary carbons, and (c) DEPT spectrum with 0 = r/2to yield only CH resonances. Data extracted from the spectra are found in Table 111.
Table 111. Quantitative and Multiplicity Data for the Simulated Library Analysis of a Three-Component Mixture
peak no.
chem shift, ppm
1
30.2
2
12.1
3
139.5 35.0 29.9 14.7 114.9 23.7 32.6 37.4 19.5 23.0 109.5 147.5 31.6 13.0
4 5 6 7 8 9 10 11
12 13 14 15 16
mult peak area 280 278 165 165 165 163 162 161 159 141 141 104 101 100 100 99
1:l
132
Figure 3. Quantitative NMR spectra of (a) petroleum distillate I and (b) petroleum distillate 11. Both fractions were diluted 1:l with CDCI, and 0.05 M Cr(acac), was added to reduce recycle times to 10 s for quantitative measurement. For both spectra, 1200 scans were collected over 3.3 h to achieve a maximum S I N of 10 for components at the 1-mg level.
Table IV. Separation of Components for Petroleum Distillate I Based upon Quantitative I3C NMR Data
chemical shift, peak ppm mult area
12 13 14
14.0 22.7 34.5 26.0 22.5 19.4 29.4 11.3 20.7 14.2 41.7 28.0 28.8 34.1
15 16 17 18 19 20 21 22 23
18.7 36.5 22.1 36.6 30.4 8.7 31.9 30.1 11.6
1
1:2
2
10,llb
3 4
3*,4,5,6,7*,8,gC
"Tolerance windows required to fit data are 1% for peaks 1,2,10,11; 40% for peaks 3-9; 5% for peaks 12-16. *Peaks 1,2,10,11 yield 2,1/2/1/0 which corresponds to 3-methylpentane. CPeaks3-9 yield l/l,l,l,l,l*/l* which corresponds to 1-heptene. dPeaks 12-16 yield l,l/l,l*/O/l* which corresponds to 2-methyl1-butene. the presence of only C5-C7 hydrocarbons. Figure 3 shows the quantitative I3C NMR spectra for the distillates. Petroleum distillate I previously was characterized by GC/MS and was found to contain seven major components (14). Table IV details the information for 23 peaks including ten methyl, nine methylene, three methine, and one quaternary carbons. Again subspectra of associated resonances were determined and indicated the presence of at least seven components. A single ambiguity occurred in the assignment of individual peaks to subspectra. Peak 13 was at first included with peak 4, creating the subspectrum 1/3/0/0; examination of the 207-compound library yielded only a match for the subset 0/3/0/0 which corresponds to a cyclic alkane. Chemical shift data eliminated cyclohexane and cycloheptane and confirmed cyclopentane as a possible match. Peak 13 was subsequently included with peaks 18-20 to create the subspectrum 3,1/1/0/1, which matched with 2,2-dimethylbutane.
5 6 7 8 9 10 11
CH3 1381 CH, 1339 679 276 201 148 108 107 101 101 100 100 96 73
Q
CH3 CHZ CH CH3
1:l
1:2 1:3
identity
3
12
pentane cyclopentane
9-12
5
2-methylpentane 40
13
14
6
2,3-dimethylbutane 3-methylpentane
54 15,16 7,8 53 44 32 18.19.20 13 2.2-dimethvl, , butane 31 31 2-methyl22 21,22,23 17 22 butane 22
"Because 1/3/0/0 is not found in the library, a subset 0/3/0/0 was selected and found to correspond to cyclopentane. Peak 13 was subsequent with peaks 18-20 to 3,1/1/0/1. The results for petroleum distillate I, summarized in Table
V, agree with GC/MS search results obtained earlier. As seen from the table, an obvious advantage of the NMR procedure is that quantitative data are acquired concurrently without need for either calibration or standarization. This information would be significantly more difficult to obtain by GC/MS or GC/IR for which nonuniform signal response requires standardization. Provided quantitative NMR conditions are achieved, the accuracy of analysis is directly related to data processing procedures and may be reduced to less than 2 %. Petroleum distillate 11, with a wider boiling point range, was a considerably more complex mixture. Subjected to the NMR analysis, a total of 43 resonances with 17 methyl, 17 methylene, 7 methine, and 2 quarternary carbons were observed. A total of 14 compounds were assigned, encompassing
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ANALYTICAL CHEMISTRY, VOL. 58, NO. 13, NOVEMBER 1986
T a b l e V. Identification and Q u a n t i t a t i v e A n a l y s i s of P e t r o l e u m F r a c t i o n s I and I 1 by U s i n g S i m u l a t e d Library Analysis
com-
fraction I
I1
pound no. 1 2 3 4 5 6 7 1 2 3 4 5 6 8
9 10 11 12 13 14 Orng in 250 p L
mole
mg/250
name
fraction
gLa
pentane 2-methylpentane cyclopentane 3-methylpentane 2,3-dimethylbutane 2,2-dimethylbutane 2-methylbutane hexane 2-methylpentane 3-methylpentane methylcyclopentane 2-methylhexane 3-methylhexane 2,4-dimethylpentane 2,3-dimethylbutane 2,2-dimethylpentane 2,2-dimethylbutane 3,3-dimethylpentane cyclohexane cyclopentane
0.693
66.8
0.103
11.6
0.056
5.3 6.2 4.3
0.055
0.038 0.032 0.023 0.257 0.199
0.159 0.094
3.6 2.2
23.5 21.1
16.9
0.022
8.5 9.0 2.9 2.4 2.4 2.3
0.017
1.6
0.015
1.6
0.014 0.009
0.7
0.085 0.032
0.023 0.023
1.3
observed region assuming 100 mg of sample in
CDC1,.
all 43 observed resonances. These are listed, along with quantitative data, in Table V. GC/MS was used in an attempt to confirm the NMR results for distillate 11. A total of 20 chromatographic peaks were observed of which 12 major peaks yielded matches of mass spectral search results in the first or second search position that agreed with NMR results. Of the eight remaining minor components, five are identified as unsaturated hydrocarbons, conflicting with 13C NMR spectra which exhibit no peaks above 50 ppm. The independent separation procedure permitted by the NMR analysis thus appears to be reliable, offering an excellent source of complementary information for interpretation of GC/MS or GC/IR data. CONCLUSIONS The detection limits of the method are dependent upon the length of time available for analysis. However, quantitative 13CNMR experiment times become prohibitively long below a 1-mg level for each component. The dynamic range of the technique is ultimately limited by the size of the analog-todigital converter employed. As seen from the data in Table V, a two order of magnitude range in relative sample concentrations was accommodated. As with chromatographic separations, reliability of the analysis is dependent upon minimization of peak overlap. Unfortunately, the ability t o effect separation of peaks in a manner analogous to changing chromatographic parameters is not as readily achieved by the NMR analysis. Variations in chemical shift associated with use of different solvents and chemical shifts reagents can be used advantageously, however,
to resolve overlapping resonances. As an example, two unresolved methylcyclopentane resonances a t 35.0 ppm in a neat sample of petroleum distillate 11, were isolated when CDC1, was added. Actually, the inflexibility of the analysis is offset by the universal applicability of 13C NMR for organic compounds. This single procedure for NMR analysis of mixture components is amenable to all organic mixtures and is easily automated. The reliability of the identification also depends upon the presence of each compound in the predicted library. Thus, although the technique is of little value for true unknowns, it is easily applied to any limited set of compounds. Specific examples of data bases to which the analysis could be applied include the EPA priority pollutants or chemicals analyzed for industrial process control applications. Extension of the method to distinguish functional groups, by incorporating chemical shift and chemical coupling constant data, is in progress. ACKNOWLEDGMENT We thank John Cooper for GC/MS analysis of the petroleum distillates. We gratefully acknowledge support from the National Science Foundation under Grant CHE-85-19087 and a Department Research Instrument Grant, CHE-82-03497. D.A.L. received partial support from the UCR Toxic Substances Research and Training Program. R e g i s t r y No. Pentane, 109-66-0;methylbutane, 68923-44-4; 2,2-dimethylpropane, 463-82-1; 1-pentene, 109-67-1;2-pentene, 109-68-2;3-methyl-1-butene,563-45-1;2-methyl-2-butene, 51335-9; 1-pentyne,627-19-0; 2-pentyne,627-21-4; 3-methyl-l-butyne, 598-23-2; 1,3-pentadiene, 504-60-9; 1,4-pentadiene, 591-93-5;2methyl-1,3-butadiene, 78-79-5; 1,3-pentadiyne, 4911-55-1; 1,4pentadiyne, 24442-69-1;cyclopentane, 287-92-3; cyclopentene, 142-29-0; cyclopentyne, 1120-58-7; cyclopentadiene, 542-92-7; 1-heptene,592-76-7;3-methylpentane,96-14-0;2-methyl-l-butene, 563-46-2;2-methylpentane, 107-83-5;2,3-dimethylbutane,79-29-8; 2,2-dimethylbutane, 75-83-2; 2-methylbutane, 78-78-4; 2,2-dimethylpentane, 590-35-2; 3,3-dimethylpentane,562-49-2; hexane, 110-54-3;cyclohexane, 110-82-7; methylcyclopentane, 96-37-7; 2-methylhexane, 591-76-4; 3-methylhexane, 589-34-4; 2,3-dimethylpentane, 565-59-3; 2,4-dimethylpentane, 108-08-7. LITERATURE C I T E D (1) Shoolery. J. N. Prog. NMR Spectrosc. 1977, 7 7 , 79-93. (2) Cookson, D. J.; Smith, B. E. J . Magn. Reson. 1984, 5 7 , 355-368. (3) Laude, D. A,, Jr.; Lee, R. W.-K.; Wilkins, C. L. Anal. Chem. 1985, 5 7 , 1286-1 290. (4) Jezi, B. A.; Dalrymple, D. L. Anal. Chem. 1975, 47, 203-207. (5) Schwarzenbach, R.; Meili, J.; Konitzer, H.;Clerc, J. T. Org. Chem. Reson. 1976, 8 , 11-16. (6) v. d. Lieth, C. W.; Seil, J.; Kohler, I.; Opferkuch, H. J. Magn. Reson. Chem. 1985, 23, 1048-1055. (7) Wilkins, C. L.; Brunner, T. R. Anal. Chem. 1977, 4 9 , 2136-2141. (8) Sjostrom, M.; Ediund, U. J . Magn. Reson. 1977, 25, 285-297. (9) Bremser, W.; Klier, M.; Meyer. E. Org. Magn. Reson. 1975, 7 , 92-105. (IO) Shelley, C. A,; Munk, M. E. Anal. Chem. 1982, 5 4 , 516-521. (11) Small, G. W.; Jurs, P. C. Anal. Chem. 1983, 55, 1121-1127. (12) Benvdall, M. R.; Pegg, D. T. J . Magn. Reson. 1983, 5 3 , 272-296. (13) Laude, D. A,, Jr.; Lee, R. W.-K.; Wilkins. C. L. Anal. Chem. 1985, 5 7 , 1282- 1286. (14) Laude, D. A,, Jr.; Cooper, John R.; Wilkins, C. L. Anal. Chem. 1986, 5 8 . 1213-1217.
RECEIVED for review April 1, 1986. Accepted July 7, 1986.
