Determination of Polymer Type and Comonomer Content in

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Anal. Chem. 1999, 71, 866-872

Determination of Polymer Type and Comonomer Content in Polyethylenes by Pyrolysis-Photoionization Mass Spectrometry David L. Zoller, Stephen T. Sum, and Murray V. Johnston*

Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716 Galen R. Hatfield† and Kuangnan Qian‡

W. R. Grace & Co. & Conn. Washington Research Center, 7500 Grace Drive, Columbia, Maryland 21044

Rapid microanalysis of a wide variety of polyolefins was performed using pyrolysis-photoionization mass spectrometry (py-PI-MS). Solid samples (∼10 µg) were pyrolyzed on a heated probe in the source region of a timeof-flight mass spectrometer. Pyrolysates were “softly” ionized using coherent vacuum ultraviolet radiation (118 nm). The resulting mass spectra were clearly different for low-density polyethylene, high-density polyethylene, and several ethylene/r-olefin copolymers. A combination of principal component analysis and linear discriminant analysis was used to classify polyolefin samples directly from their photoionization mass spectra. The compositions of ethylene-butene and ethylene-octene copolymers were predicted using partial least-squares analyses. The values obtained using py-PI-MS were in good agreement with the measured 13C NMR values. Samples of ethylene-octene containing 30 wt % carbon black and ethylene-butene containing 20 wt % silica were correctly classified and compositionally analyzed using py-PI-MS. Samples containing these additives are typically not amenable to study by solution-state 13C NMR or IR. The type, frequency, and distribution of branches within a polymer can play a major role in determining its physical properties. This is particularly true for polyethylenes. For example, homopolymer polyethylene can commonly be found in two forms, high-density polyethylene (HDPE), which is linear, and lowdensity polyethylene (LDPE), which is highly branched. Aside from the density differences that give rise to their names, HDPE melts at ∼137 °C while LDPE melts across a temperature range of 105-120 °C. Efforts to tailor the branch characteristics of polyethylenes have led to a wide range of materials and are the focus of current studies using metallocene and other single-site catalysts. The ability to advance such studies is closely tied to an ability to fully characterize molecular architecture. Solution-state 13C NMR is the preferred method for characterizing branching in †

Current address: Sealed Air Corp., 7500 Grace Drive, Columbia, MD 21044. Current address: Exxon Research and Engineering Co., Rt. 22E, Annandale, NJ 08801. ‡

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polyethylenes since it is direct, is quantitative, and can readily differentiate between different branch lengths.1-3 However, solution-state 13C NMR suffers from two main limitations. First, NMR is not particularly sensitive, which necessitates long analysis times (typically several hours) and large sample sizes (typically 250 mg). This sample size can be prohibitive, especially in trial polymerizations with developmental catalysts. Second, solution-state NMR is limited by its reliance on solubilizing the polymer. Some polyethylenes are highly cross-linked or loaded, rendering them insoluble. For these samples, melt-state NMR4 has been offered as an alternative but it remains limited by the same sensitivity issues. In certain limited cases, IR can be used to characterize branching.5,6 However, applications of IR in this area are largely limited to process control and in some cases analyses of unknowns. Pyrolysis is a robust fragmentation method commonly used for the analysis of synthetic polymers.7-10 Fragmentation of the polymer chain is required to determine the microstructure of polyolefins by mass spectrometry. Primary decomposition mechanisms, such as random chain cleavage, produce pyrolysates most representative of the original polymer chain. Measurement of these pyrolysates is desired when the microstructure of polymers is being studied. Microstructural information is lost when further degradation or recombination of the primary products occurs within the pyrolysis zone. These secondary pyrolysis products can be minimized if the sample is quickly transformed into gas-phase products and rapidly transported away from the pyrolysis zone. Therefore, small sample sizes (micrograms) and fast heating (1) Randall, J. JMS-Rev. Macromol. Chem. Phys. 1989, C29, 201-313. (2) Cheng, H. N. J. Appl. Polym. Sci.: Appl. Polym. Symp. 1989, 43, 129-163. (3) DePooter, M.; Smith, P. B.; Dohrer, K. K.; Bennett, K. F.; Meadows, M. D.; Smith, C. G.; Schouwenaars, H. P.; Geerards, R. A. J. Appl. Polym. Sci. 1991, 42, 399-409. (4) Hatfield, G. R.; Killinger, W. E.; Zeigler, R. C. Anal. Chem. 1995, 67, 30823085. (5) Maddams, W. F.; Woolmington, J. Makromol. Chem. 1985, 186, 16651670. (6) Usami, T.; Takayama S. Polym. J. 1984, 16, 731-738. (7) Schulten, H.-R.; Lattimer, R. P. Mass Spectrom. Rev. 1984, 3, 231-315. (8) Montaudo, G. Br. Polym. J. 1986, 18, 231-235. (9) Kyranos, J. N.; Vouros, P. J. Appl. Polym. Sci.: Appl. Polym. Symp. 1989, 43, 211-240. (10) Qian, K.; Killinger, W. E.; Casey, M.; Nicol, G. R. Anal. Chem. 1996, 68, 1019-1027. 10.1021/ac9807159 CCC: $18.00

© 1999 American Chemical Society Published on Web 01/20/1999

ramps (>1 °C/s) are needed to study polymer microstructure. By using consistent sample preparation and a constant heating rate, pyrolysis may be performed with highly reproducible results. Photoionization mass spectrometry may be used to quantitatively analyze the microstructure of a polymer from its pyrolysates. This technique has been previously used to determine the composition and microstructure of acrylonitrile-butadiene copolymers.11 Pyrolysis-photoionization mass spectromety (py-PIMS) has many advantages including rapid analysis (a few minutes), small sample sizes (10 µg or less), and analysis of solid samples. Polymer analysis is performed by pyrolyzing the sample on a heated probe inside the mass spectrometer. Since the sample is under a high vacuum, vaporized pyrolysates are rapidly transported from the end of the probe to the source region of the mass spectrometer where they are photoionized with radiation just above their ionization potentials. Photoionization is a “soft” ionization technique that produces molecular ions with little subsequent fragmentation.12-14 The mass spectra are then analyzed using chemometric models to determine the type and composition of the polyolefin sample. In this study, a wide variety of polyolefins were analyzed using the py-PI-MS technique. Chemometric models were developed to classify “unknown” polyolefin samples according to their composition and microstructure. In addition, polyolefin samples were analyzed that are typically not amenable to study by solutionstate 13C NMR or IR methods. EXPERIMENTAL SECTION Pyrolysis was performed directly in the source region of a reflectron time-of-flight (RETOF) mass spectrometer (R. M. Jordan Co., Grass Valley, CA) using an insertion probe (Vacumetrics, Ventura, CA) and a temperature-programming system (Omega, Stamford, CT). In the experiments reported here, a piece of polymer was cut with a small knife to a mass of 40 ( 5 µg. This piece was then further sliced into four equal size pieces approximately 10 ( 5 µg in size. The 10-µg samples were not weighed directly due to difficulties in sample handling and the limits of the balance. Each sample was loaded into the bottom of a Pyrex vial and placed in the end of the insertion probe. Polymer degradation was performed by heating at a rate of 4.2 °C/s to a final temperature of 450 °C. The RETOF mass spectrometer had an open source design and was held at room temperature. As the samples were pyrolyzed, gaseous products effused into the center of the source region where they were photoionized. The distance from the end of the probe to the photoionization laser beam was 1.25 cm. Mass spectra (32K record length) were continuously averaged at 50 Hz with a 500-MHz transient digitizer (model 9846, Precision Instruments, Knoxville, TN) mounted in a personal computer. An averaged mass spectrum was recorded every 32 laser shots. Figure 1 shows a plot of the total ion signal for each mass spectrum recorded during one sample run. Data analysis was performed using the summation of six consecutive mass spectra (11) Zoller, D. L.; Johnston, M. V. Anal. Chem. 1997, 69, 3791-3795. (12) Van Bramer, S. E.; Johnston, M. V. Anal. Chem. 1990, 62, 2643-2646. (13) Van Bramer, S. E.; Johnston, M. V. Org. Mass Spectrom. 1992, 27, 949954. (14) Van Bramer, S. E.; Ross, P. L.; Johnston, M. V. J. Am. Soc. Mass Spectrom. 1993, 4, 65-72.

Figure 1. Total ion signal for each mass spectrum recorded from 250 to 400 °C during the pyrolysis of high-density polyethylene. Each data point represents the average signal over 32 laser shots. The dashed lines indicate the interval from which mass spectra were used for further data analysis.

(192 laser shots) recorded during the peak of the ion signal. For the sample run shown in Figure 1, the six mass spectra between the dashed lines were chosen for further analysis. These mass spectra correspond to a 4-s interval during the pyrolysis of the polyolefin. Ion peak areas were calculated using a modified array basic program in GRAMS/32 (Galactic Industries Corp., Salem, NH). This program calculates the approximate location for each peak in the mass spectrum using a previously calibrated mass spectrum. The exact location of the peak maximum is then determined by iteratively checking the intensities of adjacent points. The peak edges are determined by calculating the slope between consecutive data points starting at the peak maximum. A change in the slope from negative (intensity decreasing) to positive (intensity increasing) determines the location of each peak edge. A maximum peak width of 24 ns is specified to eliminate inordinately large peak areas for peaks broadened by metastable decay. A baseline is drawn between the two data points at the edges of the peak and a trapezoid algorithm is used to calculate the total peak area. Chemometric models to determine the polyolefin type and composition were developed using principal component analysis, linear discriminant analysis, and partial leastsquares analysis programs (PLS Toolbox 1.5.1, Eigenvector Technologies, Manson, WA) written using the MATLAB language (Mathworks, Inc., Natick, MA). Photoionization was performed with radiation derived from the third harmonic of an Infinity 40-100 Nd:YAG laser (Coherent Inc., Santa Clara, CA). Radiation at 355 nm was frequency-tripled in a phase-matched mixture of xenon and argon to yield vacuum ultraviolet (VUV) radiation at 118.2 nm (10.49 eV). An input ultraviolet pulse energy of 10 mJ was used to produce a VUV pulse energy of 1.6 µJ (1 × 1012 photons/pulse) at a repetition rate of 50 Hz. Physical separation of the ultraviolet and VUV radiation was accomplished by off-axis focusing of the ultraviolet radiation into the frequency-tripling cell. Additional experimental details concerning VUV generation are discussed elsewhere.15 Analytical Chemistry, Vol. 71, No. 4, February 15, 1999

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Table 1. Polyethylene Samples Used To Construct the Type Model with Their Measured 13C NMR Compositions Where Applicable sample no.

polyolefin type

R-olefin (%)

1 2 3 4 5 6 7

high-density polyethylene low-density polyethylene ethylene-propylene ethylene-butene ethylene-hexene ethylene-octene ethylene-propylene-butene

naa naa 19.6 6.8 3.7 5.4 5.0 P, 4.8 B

a

na, not applicable.

Table 2. Results from the Type Model for the Polyethylene Samples in the Independent Test Set with Their Measured 13C NMR Compositions Where Applicablea sample no. known test set 8 9 10 11 12 13 14 15 16 17 18 19 unknown test set 20 21 22 23

polyolefin type

R-olefin (%)

predicted type

LDPE E-B E-B E-B E-H E-H E-O E-O E-O E-O E-O E-P/E-B melt

na 5.1 8.2 11.2 0.8 9.6 2.5 4.3 6.2 6.5 7.4 na

LDPE E-B E-B E-B HDPE E-H E-O E-O E-O E-O E-O E-P-B

E-O E-B LDPE E-VA

6.6 7.1 na na

E-O E-B LDPE na

Figure 2. Pyrolysis-photoionization mass spectra of (a) a lowdensity polyethylene and a (b) a high-density polyethylene.

To test reproducibility and long-term stability, three samples of each polymer were analyzed on five different days during a three-month period (15 total measurements for each sample). Samples of high-density polyethylene, low-density polyethylene, ethylene-propylene, ethylene-butene, ethylene-hexene, ethylene-octene, and ethylene-propylene-butene were used to develop models to classify “unknown” samples according to type and composition. Tables 1 and 2 list the samples analyzed with their measured 13C NMR composition values where applicable. 13C NMR data were obtained at 100.6 MHz on a Bruker DMX400 using conditions commonly employed to characterize poly(Rolefins).1-3 The samples were dissolved in trichlorobenzene, and benzene-d6 was used as a lock solvent. NMR spectra were acquired using a 10-s pulse delay,3 a 90° pulse width,3 and inverse gated decoupling to avoid nuclear Overhauser enhancement (NOE) effects. The effect of additives in the polymer was studied using an ethylene-octene copolymer containing 30 wt % carbon black and an ethylene-butene copolymer containing 20% silica.

RESULTS AND DISCUSSION Pyrolysis-Photoionization Mass Spectra. All of the polyolefins analyzed in this study contained similar features in their mass spectra. The mass spectra are dominated by a series of peaks attributed to alkene pyrolysis products (CnH2n). A series of peaks corresponding to dialkene pyrolysates (CnH2n-2) is also observed with lower intensity relative to the alkene series. In addition, a series of peaks due to alkane pyrolysates (CnH2n+2) is identified in the mass spectra predominantly at m/z values above 100. Peaks at odd m/z values (CnH2n-1 and CnH2n+1) are due to a combination of 13C isotopic substitution, radicals generated during pyrolysis, and fragmentation of less stable pyrolysates. The broadening of peaks near the base indicates that some metastable decay of ions occurs within the source region. Peaks are detected in the mass spectra up to 300 m/z with the most intense peaks below 100 m/z. Previous studies using gas chromatography16 and field ionization mass spectrometry17have also observed alkane, alkene, and dialkene products from the pyrolysis of polyolefins. However, in the field ionization mass spectra, peaks are identified for masses up to 2000 m/z with the most intense peaks between 400 and 600 m/z. Little qualitative difference in the intensities of these highmass ions was observed between the field ionization mass spectra for a high-density polyethylene and a low-density polyethylene. The different ion distributions detected by field ionization and photoionization may be attributed to two factors. First, field ionization is known to favor production of ions in the range of 400-600 m/z18 while VUV photoionization generally favors production of ions below 300 m/z. Second, larger samples sizes (up to 1 mg) were used for the field ionization study, which may enhance the production of higher mass pyrolysates. Figure 2 shows the pyrolysis-photoionization mass spectra for both low-density and high-density polyethylene. Low-density

(15) Van Bramer, S. E.; Johnston, M. V. Appl. Spectrosc. 1992, 46, 255-261. (16) Wampler, T. P.; Levy, E. J. Analyst 1986, 111, 1065-1067.

(17) Lattimer, R. P. J. Anal. Appl. Pyrolysis 1995, 31, 203-225. (18) Schulten, H.-R.; Simmleit, N.; Muller, R. Anal. Chem. 1987, 59, 2903-2908.

a Abbreviations: E ) ethylene, P ) propylene, B ) butene, H ) hexene, O ) octene, VA ) vinyl acetate, LDPE ) low-density polyethylene, HDPE ) high-density polyethylene, melt ) melted mixture, and na ) not applicable.

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Figure 4. Pyrolysis of an ethylene-hexene copolymer by (a) scission at the branch to form butene (m/z ) 56) and (b) scission at the β(C-C) bonds to form 2-methyl-1-hexene (m/z ) 98).

Figure 3. Pyrolysis-photoionization mass spectra of (a) an ethylene-butene copolymer containing 11.2% butene, (b) an ethylenehexene copolymer containing 9.6% hexene, and (c) an ethyleneoctene copolymer containing 6.5% octene.

polyethylene is a highly branched polyolefin whereas high-density polyethylene is linear. These two types of polyolefins are clearly differentiated using their pyrolysis-photoionization mass spectra. For low-density polyethylene, the most intense peaks in the mass spectrum are attributed to the alkene series (m/z ) 56, 70, 84, 98, ...). Similarly, the mass spectrum for high-density polyethylene contains intense peaks for the alkene series. However, peaks at odd m/z values (m/z ) 57, 71, 83, 97) have much higher relative intensities for high-density polyethylene than for low-density polyethylene. Ionization of many different types of alkanes and alkenes using VUV radiation indicates, relative to the molecular ion, peaks due to fragment ions are minimal at a photon energy of 10.49 eV.12-14 Therefore, these peaks are most likely due to radical species generated during pyrolysis. Since both pyrolysis and evolution of pyrolysates into the source region of the mass spectrometer are rapid, radical species generated by pyrolysis may be photoionized. These radical species have lower ionization potentials, and the potential for fragmentation may be greater. Figure 3 shows the pyrolysis-photoionization mass spectra for ethylene-butene, ethylene-hexene, and ethylene-octene. For ethylene-hexene, the peaks at m/z ) 56 and 98 have the highest relative intensities in the mass spectrum. Figure 4 shows how these pyrolysates may be formed from the pyrolysis of ethylenehexene. As shown in Figure 4a, scission of the C-C bond between the branch and the main polyolefin chain produces a linear alkene, 1-butene. The high relative intensity of the peak at m/z ) 56 may be attributed to this pyrolysate. Figure 4b shows that scission of the two β(C-C) bonds relative to the branch produces a methylalkene, 2-methyl-1-hexene. This pyrolysate may be responsible for the high relative intensity of the peak at m/z ) 98 in the ethylene-hexene mass spectra. The production of 2-methyl-1hexene from the pyrolysis of ethylene-hexene agrees with previous studies using pyrolysis-hydrogenation gas chroma-

tography.19-21 These studies identified methylalkane products (hydrogenation of the double bond) from the pyrolysis of several different ethylene/R-olefin copolymers. Similarily, intense peaks due to two β(C-C) scissions are observed for ethylene-butene (m/z ) 70) and ethylene-octene (m/z ) 126). In addition, an intense peak due to scission at the branch is observed in the mass spectrum of ethylene-octene (m/z ) 84). Scission at the branch is not observed in the mass spectrum for ethylene-butene (m/z ) 28). This pyrolysate, ethylene, has an ionization potential of 10.51 eV above the photon energy of 10.49 eV. Type Model. A method was developed to classify different polyolefin microstructures from their corresponding pyrolysisphotoionization mass spectra. For each sample, peak areas were calculated using the computer program described previously. Areas for 31 peaks ranging from m/z ) 42 to m/z ) 156 were used to generate the type model. These peaks were present in all of the polyolefin mass spectra, exhibited the greatest variability between different types of polyolefins, and encompassed the most important products labeled in Figures 2 and 3. The peak areas for each of six mass spectra from the maximum of the total ion signal plot were then summed and each peak area was normalized to the total ion signal. Normalization was required in order to adjust for slight differences in sample size and day-to-day variation in VUV photon production. First, a principal component analysis22,23 was used to preprocess and transform the data. The subsequent principal components were then used in a linear discriminant analysis,24 which constructed the type model and classified unknown samples. Principal component analysis with linear discriminant analysis classified the different polyolefin types using all of the calculated peak areas simultaneously. This chemometric model is more rigorous and accurate than probability-based matching25 or calculation of a “similarity” index.26 With the latter methods, the peak areas are compared between (19) Sugimura, Y.; Usami, T.; Nagaya, T.; Tsuge, S. Macromolecules 1981, 14, 1787-1791. (20) Haney, M. A.; Johnston, D. W.; Clampitt, B. H. Macromolecules 1983, 16, 1775-1783. (21) Ohtani, H.; Tsuge, S.; Usami, T. Macromolecules 1984, 17, 2557-2561. (22) Wold, S.; Estansen, K.; Geladi, P. Chemom. Intell. Lab. Syst. 1987, 2, 3752. (23) Jackson, J. E. A User’s Guide to Principal Components, 2nd ed.; WileyInterscience: New York, 1991. (24) James, M. Classification Algorithms, 3rd ed.; Wiley: New York, 1985; Chapter 4. (25) McLafferty, F. W.; Turecek, F. Interpretation of Mass Spectra, 4th ed.; University Science Books: Mill Valley, CA, 1993; Chapter 10.

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Table 3. m/z Values Important for Classifying Polyolefin Types principal component

high positive loading

high negative loading

strong classification

1 2 3 4 5

56, 57 56, 57, 70 56, 84, 98 98, 99 42, 56

70, 71, 84 82, 83, 84, 97 57, 71, 83, 97 42, 84, 126 57, 112, 126

E-P, E-B LDPE HDPE E-O E-H

an unknown mass spectrum and a reference mass spectrum and the matching probability is calculated based on a predefined set of empirically determined rules. With chemometric modeling, these rules are mathematically determined and optimized for each reference set in order to maximize the prediction accuracy. Chemometric modeling provides a significant advantage until the reference data set becomes much larger (>100 different classes) and recalculation of the model for each new reference sample becomes cumbersome. Table 1 lists the seven polyolefins used to generate the type model. Samples of ethylene/R-olefin copolymers were chosen from the middle of the range of compositions analyzed in this study. In the final model, five principal components were used to classify the different polyolefin microstuctures. This model explained 96.7% of the variance in the mass spectra data. The m/z values that contribute most to these principal components (high magnitude of loading) are listed in Table 3. By comparing the loadings to the polyolefin mass spectra, the type model is clearly based on true differences between the polyolefin microstructures and not the result of different additives or background signals. The first principal component strongly classified ethylenepropylene and ethylene-butene based on their β(C-C) bond scission products, m/z ) 56 and m/z ) 70, respectively. Classification of low-density polyethylene was based largely on the second principal component with large positive loadings for the alkene series peaks (m/z ) 56 and 70) and large negative loadings for the odd mass peaks (m/z ) 83, and 97). The large negative loadings of odd mass peaks (m/z ) 57, 71, 83 and 97) for the third principal component separate high-density polyethylene from the rest of the polyolefins. Ethylene-octene and ethylene-hexene were clearly separated by the fourth and fifth principal components based on their products resulting from scission at the branch and scission of the β(C-C) bonds. The large positive loadings at m/z ) 56 and 98 classified ethylene-hexene while the large negative loadings of m/z ) 84 and 126 classified ethylene-octene. Figure 5 shows a plot of the scores for two of the principal components. This figure shows all of the runs for a particular polyolefin sample cluster together. In this plot, some samples appear close to other polyolefin types (e.g., ethylene-octene and ethylene-hexene). However, these samples are clearly distinguished from each other in a plot of the scores of two of the other principal components. It should be noted that the two-dimensional score plots are used only to visually interpret the model. The true model uses all five principal components simultaneously. The accuracy of the type model was validated by several methods. First, the samples used to construct the model were (26) Lay, J. O., Jr.; Gross, M. L.; Zwinselmann, J. J.; Nibbering, N. M. M. Org. Mass Spectrom. 1983, 18, 16-21.

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Figure 5. Scores for the first and third principal components plotted for each sample run used in the calibration set.

reclassified. All of the analyses for these seven polyolefins were correctly classified using the type model. Second, 12 polyolefin samples with known microstructures and compositions were analyzed using the pyrolysis-photoionization technique. These 12 samples constitute an independent test set that was not used to build the model. Table 2 lists the samples analyzed and the results of their classification. All but one polyolefin was correctly classified according to the type of polyolefin. The one exception was for an ethylene-hexene copolymer with 0.8% hexene which was classified as a high-density polyethylene. This is not a surprising result given the similarities between the microstructures of HDPE and ethylene/R-olefin copolymers with low R-olefin compositions. The misclassification of these ethylene/R-olefin copolymers may be corrected by dividing these types into two classes: “low” and “high” composition. Of course, a limit exists for how slight of a change in microstructure may be differentiated by this technique. Finally, four “unknown” samples of known composition (via NMR) were characterized in a “blind” fashion by the analyst. For three of the samples, chemometric analyses yielded satisfactory matches and the samples were classified correctly (Table 2). In these cases, linear discriminant analysis predicted the probability of correct classification at higher than 90%. The fourth unknown was strategically chosen to be an outlier from the data set used to build the type model described above, in this case, a copolymer of ethylene and vinyl acetate (EVA). EVA copolymers are produced using the same polymerization process as low-density polyethylene. The result is that the two polymers have the same polyethylene-based branching architecture which may have been expected to result in a false identification using the chemometric model. However, for this sample, multiple analyses yielded different classifications, with the result being a poor match to the type model and a probability of correct classification below 70%. Thus it appears that the type model developed here is robust and capable of avoiding false positives. Composition Model. Partial least-squares analyses27 were used to predict the compositions of ethylene-butene and ethyl(27) Geladi, P.; Kowalski, B. R. Anal. Chim. Acta 1986, 185, 1-17.

Figure 6. Pyrolysis-photoionization mass spectra of ethyleneoctene copolymers containing (a) 6.5% octene and (b) 2.5% octene.

ene-octene copolymers. The same data set of ethylene-butene and ethylene-octene copolymers used in the type model was also used in the composition model. Figure 6 shows the mass spectra for two ethylene-octene copolymers, one with 6.5% octene and the other with 2.5% octene. The peak at m/z ) 84 increases relative to the peaks at m/z ) 82 and 83 as the octene composition increases. The peak at m/z ) 84 corresponds to chain scission at the branch. For the ethylene-octene composition model, five samples in the range from 2.5 to 7.4% octene were used to calibrate the model. This technique has the potential to determine lower R-olefin content (such as below 1%); however, the model was only calibrated to 2.5% octene based on the samples available for this study. Three latent variables were used in construction of the final ethylene-octene composition model based on leave-one-out crossvalidation. The standard method of assessing the predictive ability of a chemometric model is to report the root-mean-square error of prediction (RMSEP). RMSEP is defined as

x∑ Ip

RMSEP )

(xi - yi)2/Ip

i)1

where Ip is the number of samples in the prediction set, yi is the measured (MS) value for each run and xi is the actual (NMR) value for each run. An RMSEP value of 0.64% was calculated for the calibration set, or in other words, the predictions have an average error within 0.64% of the ideal calibration shown as a solid line in Figures 7 and 8. The most important masses were identified from the weightings of these latent variables. The relative peak areas at m/z ) 82, 83, and 97 have an inverse correlation with the octene composition. As determined previously, the relative intensities of these peaks are higher for more linear polyolefins such as high-density polyethylene. In addition, the normalized peak areas at these m/z values decrease due to the increased

Figure 7. Predicted octene compositions for ethylene-octene copolymers using py-PI-MS and 13C NMR. Error bars reflect one standard deviation from the mean. The solid line shows an ideal calibration (slope 1; intercept 0).

Figure 8. Predicted butene compositions for ethylene-butene copolymers using py-PI-MS and 13C NMR. Error bars reflect one standard deviation from the mean. The solid line shows an ideal calibration (slope 1; intercept 0).

signal at m/z ) 84. The peak at m/z ) 126 has a direct correlation to the octene composition. This peak corresponds to scission of the β(C-C) bonds relative to the branch position. Figure 7 shows a plot of the predicted py-PI-MS vs 13C NMR composition values. The error bars represent one standard deviation from the mean, which is the RMSEP with Ip - 1 substituted for Ip. It should be noted that the NMR values were used to calibrate the compositional model. Therefore, any errors in the NMR compositional values will be propagated in the py-PI-MS values. Peak area data for an ethylene-octene copolymer with unknown composition was then analyzed using the composition model. The model predicted an average composition of 6.5% octene (RMSEP ) 0.30%). Subsequent 13C NMR analysis determined the composition was 6.6% octene, in agreement with the predicted py-PI-MS value. Analytical Chemistry, Vol. 71, No. 4, February 15, 1999

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For the ethylene-butene model, four samples were used with compositions in the range from 5.2 to 11.2% butene. Figure 8 is a plot of the predicted py-PI-MS vs 13C NMR compositional values. Based on a leave-one-out cross-validation, two latent variables were used in the ethylene-butene model. An RMSEP value of 0.93% was determined for the calibration set. The weightings of the latent variable indicate the peak at m/z ) 70 has a direct correlation with butene composition. This peak corresponds to scission of the β(C-C) bonds between relative to the branch position. As with ethylene-octene, the peak at m/z ) 97 in the mass spectra for ethylene-butene has an inverse correlation with the butene composition. Peak area data for an ethylene-butene copolymer with an unknown composition was then analyzed using the composition model. The model predicted an average composition of 6.4% octene (RMSEP ) 0.84%). Subsequent 13C NMR analysis determined the composition was 7.1% octene, within one standard deviation of the predicted py-PI-MS value. The ethylene-butene model was less precise than the ethylene-octene model. One explanation for this decrease in precision is observed in the mass spectra for these two copolymers. For ethylene-octene, two peaks attributed to chain scission both at the branch and around the branch are observed. However, for ethylene-butene, only one peak due to cleavage around the branch is observed in the mass spectrum. A reduction in the number of peaks correlated with butene composition decreases the precision of the model. Also, it should be noted that the error in determining comonomer content in ethylene-butene copolymers by NMR is higher than that observed for ethylene-hexene and ethylene-octene.3 This error is likely to have propagated into the mass spectral analysis done here. Polyolefins Containing Additives. Two LLDPE samples containing additives were analyzed using the py-PI-MS technique. The first LLDPE sample contained 30 wt % carbon black. When analyzed by NMR, the presence of carbon black degrades the resolution of the spectrum and makes quantitative analysis more difficult. However, the mass spectra for the LLDPE sample with carbon black was virtually indistinguishable from the same LLDPE sample without carbon black. Mass peaks due to carbon black (a series of peaks separated by 12 DA) are only detected near the end of the heating ramp (>400 °C) and not during polymer degradation. Using the type model, the sample was identified unambiguously as an ethylene-octene copolymer. The sample was then analyzed using the composition model and predicted to be 4.6% octene. This value was 1.7% higher than the 13C NMR value of 2.9% octene for the LLDPE samples without any carbon black. Instrumental changes may have altered the calibration since the samples discussed previously were analyzed several months

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Analytical Chemistry, Vol. 71, No. 4, February 15, 1999

prior to analysis of the samples containing additives. Also, the probe had recently been tuned for experiments requiring different heating ramps. Therefore, a recalibration was performed to adjust for any instrumental changes. Each ethylene-octene sample used in the original composition model was reanalyzed three times each on two separate days. Using the new calibration, the composition of the ethylene-octene sample containing carbon black was found to be 3.3% (RMSEP ) 0.55%), in agreement with the 13C NMR value for the ethylene-octene sample without carbon black. The prediction is further improved when data for the LLDPE sample without carbon black is included in the composition model. The composition for the sample containing carbon black was then predicted to be 3.0% octene (RMSEP ) 0.38%). An LLDPE sample containing 20 wt % silica was also analyzed. The presence of silica in the polymer prevents microstructural analysis for this sample using conventional infrared spectroscopy. However, using py-PI-MS, no mass peaks attributable to silica were detected in the mass spectra. The sample was clearly identified as an ethylene-butene copolymer. A new calibration for the ethylene-butene samples was also required. Using the new calibration, the composition of the ethylene-butene sample containing silica was found to be 4.0% (RMSEP ) 0.55%), in agreement with the 13C NMR value of 4.1% butene. CONCLUSIONS In this work, pyrolysis-photoionization mass spectrometry was used to rapidly analyze the microstructures of a wide variety of polyolefins. A linear discriminant analysis with principal component preprocessing was used to develop a model that classified a sample according to polymer type. An independent test set of 16 polyethylene samples was used to confirm the accuracy of the model. Partial least-squares analyses were used to develop models to determine the composition of ethylene-octene and ethylenebutene copolymers. The py-PI-MS composition values were in good agreement with 13C NMR values. Similarly, the polymer type and comonomer content for samples containing additives such as silica and carbon black were determined. Preliminary results indicate this technique may also be useful in characterizing polyethylene blends. ACKNOWLEDGMENT This research was supported in part by the National Science Foundation under Grant number CHE9629672. Received for review July 2, 1998. Accepted December 9, 1998. AC9807159