Quantitative analysis of rubber triblends by pyrolysis-mass spectrometry

Journal of Analytical and Applied Pyrolysis, 8 (1985) 95-107 Elsevier Science Publishers B.V.. Amsterdam - Printed in The Netherlands

QUANTITATIVE ANALYSIS OF RUBBER TRIBLENDS PYROLYSIS-MASS SPECTROMETRY

ROBERT

P. LATTIMER

* and KENNETH

95

BY

M. SCHUR

The BFGoodrich Research and Development Center, Brecksville, OH 44141 (U.S.A.) WILLEM

WINDIG

and HENK

L.C. MEUZELAAR

Biomaterials Profiling Center, University of Utah, Salt Lake City, UT 84108 (U.S.A.)

SUMMARY Quantitative analysis of the components of rubber vulcanizates is a difficult, yet very important, process in industrial polymer chemistry. Infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy are the techniques most often used. These methods generally involve time consuming pretreatment steps, but in favorable cases these methods yield results with l-5% accuracy. In a preliminary study, we acquired Curie-point pyrolysis-mass spectra for several uncured rubber blends as well as some compounded vulcanizates. Qualitative examination of the data indicated the potential for the derivation of quantitative data from the spectra. Now we have examined a series of rubber triblend samples by pyrolysis-mass spectrometry (Py-MS) to assess the quantitative aspects. The three components were styrene-butadiene rubber (SBR), cis-polybutadiene rubber (BR), and natural (polyisoprene) rubber (NR). The blends were analyzed in triplicate or quadruplicate by Curie-point Py-MS from toluene suspensions of samples obtained by prolonged grinding under liquid nitrogen. The spectra were normalized with the NORMA program and subsequently analyzed by target rotation factor analysis using the SPSS program. Finally, response factors were calculated for each of the components, and these were used to predict the concentrations of the individual rubber components in various training or test sets of data. The response factors were found to be relatively constant for each of the blends, indicating that interactions between components were minimal. The average errors for the three components were in the 2-5X range. The error for BR was slightly larger since the butadiene monomer is common to both SBR and BR. The results demonstrate the feasibility of the I’-MS approach to polymer blend analysis. The results are comparable in accuracy to those obtained by IR and NMR spectroscopy.

INTRODUCTION

Quantitative analysis of the components of rubber vulcanizates is a difficult, yet very important, process in analytical polymer chemistry. Applications include the identification of rubber copolymers and blends used in

96

manufactured products, the verification of the composition of compounded rubber stocks, and the investigation of new rubbers prepared in the laboratory. Most analytical procedures for vulcanizates involve a pretreatment of the sample to yield a polymeric component free of organic additives and perhaps other components (e.g., fillers) [l]. The detailed solubilization procedure of Dinsmore and Smith [2] is most often used for sample preparation. In this the vulcanizate is first shredded with a laboratory mill and then exhaustively extracted with acetone or an acetone-chloroform mixture to remove oil and organic additives. The rubber is then solubilized in boiling o-dichlorobenzene (ODCB), and inorganic fillers and carbon black are removed by centrifugation and filtration. The ODCB solubilization step involves breaking some of the sulfur crosslinks, and it has been reported that the addition of a devulcanizing chemical (2,2’-dibenzamidodiphenyl disulfide) aids the solubilization process [3]. Variations of the basic Dinsmore-Smith procedure may involve the use of different solvents and modified extraction/separation procedures. The main difficulties are that the sample preparation is timeconsuming (on the order of six man-hours per sample [2]) and the possibility that the composition of the solubilized rubber may be somewhat different than that of the starting vulcanizate. Infrared (IR) spectroscopy is most often used for analysis of the solubilized rubber [2,3]. The ODCB solution of the rubber is concentrated, and films are cast on salt plates for both qualitative and quantitative IR analysis. Nuclear magnetic resonance (NMR) is an alternative method for analysis of the solubilized rubber. Proton NMR was originally used [4], but more recently 13C NMR has been shown to yield more complete results [5]. 13C NMR has the advantage that it can be a primary method, i.e., it does not require calibration with standard compounds as does IR spectroscopy. 13C NMR also does not require the removal of fillers prior to analysis, and NMR is reported to give better repeatability and sensitivity than IR [5]. One problem that may be encountered is incomplete solubilization of highly cured samples [6]. Accuracies for quantitative analysis are typically in the l-5% range for either IR or NMR. Pyrolysis is an alternative sample pretreatment step that is often used for vulcanizate analysis [7,8]. The sample is first extracted with acetone, and the dried rubber is heated in order to decompose the polymer. This produces a liquid pyrolyzate that can be placed between salt plates for IR analysis. Pyrolysis-gas chromatography (Py-GC) has also been used for vulcanizate analysis [9,10]. The rubber is first extracted with methanol and then pyrolyzed using either a tube furnace [9] or Curie-point apparatus [lo]. A valve switching arrangement is used to transfer the volatile pyrolyzate onto the GC column. The concentrations of monomeric or dimeric pyrolyzates are used for quantitative determinations. Poor repeatability of the pyrolysis step may be troublesome in Py-IR and

97

Py-GC analysis, particularly with the relatively large sample sizes (milligram to gram level) and long temperature rise times that are typically used. Another concern is that the oligomers produced by pyrolysis may not be representative of the composition of the original polymer. Nevertheless, accuracies of 2-5s have been reported for quantitative determinations by Py-IR and Py-GC procedures. Pyrolysis procedures have the advantage that they are generally less time-consuming than methods involving ODCB solubilization. In the ideal situation, one would prefer to use direct methods to analyze intractable vulcanizates. Elimination of sample pretreatment steps would be cost effective and would alleviate problems introduced by altering the chemical or physical state of the material prior to the analysis step. Previous attempts at direct compound analysis have involved IR or NMR spectroscopy, thermoanalytical methods, and pyrolysis-mass spectrometry (Py-MS). Several years ago Corish reported the direct analysis of black-loaded vulcanizates by IR spectroscopy [ll]. It was necessary to use very thin microtomed sections (2-5 pm thick) in order to avoid total absorption of IR radiation by the carbon black. The spectra (obtained on a grating instrument) were not entirely satisfactory, but the feasibility of the technique was demonstrated. More recently it was shown that much better signal-to-noise ratios could be obtained with microtomed sections using a Fourier-transform IR spectrometer [12]. Another approach to direct vulcanizate analysis is solids 13C NMR. Komoroski [13] demonstrated the feasibility of this approach in an analysis of a series of cured, carbon-black filled blends. Solids NMR analysis yielded results with an average deviation of ca. 4% from the expected values. Solids NMR has the advantage that it does not need to rely on calibration curves derived from standard blends. One disadvantage is that long run times (several hours) may be necessary for adequate signal averaging [13]. Thermoanalytical methods have also been used for analysis of cured rubber blends. A combination of thermogravimetric analysis (TGA), differential thermogravimetric analysis (DTG), and differential’ scanning calorimetry (DSC) can provide a variety of compositional details [14]. DSC gives information on cure characteristics, sulfur, and accelerator levels. TGA and DTG can be used to determine the amounts of various compounding ingredients. Initial weight loss upon heating is due to volatilization of solvent, oil, and plasticizer. The polymer decomposes at higher temperatures, leaving a residue of carbon black and inorganic fillers. By running the samples under an inert atmosphere and then switching to an oxidizing atmosphere, the carbon black level can be determined separately from the level of fillers. If the various elastomer components decompose at sufficiently different temperatures, TGA/DTG can be used to gain information on the blend composition. Since many common elastomers have very similar thermal stabilities, however, this technique is rather limited in its applica-

98

bility. Auxiliary (spectroscopic) methods are required to more definitively characterize rubber blends. In this paper we discuss the applicability of another direct method for vulcanizate analysis - pyrolysis-mass spectrometry (Py-MS). Several years ago, the use of indirect Py-MS for the identification of unknown polymers was reported [15]. Although this early paper did not give quantitative analysis results for blends, the feasibility for this type of application was mentioned. In a preliminary report from our laboratories, we acquired Curie-point pyrolysis-mass spectra for several uncured rubber blends as well as some compounded vulcanizates [16]. Qualitative examination of the data indicated the potential for the derivation of quantitative data from the spectra. Now we have examined a series of rubber triblend samples by Py-MS to assess the quantitative aspects. The average errors for the three blend components were in the 2-5% range, which is comparable in accuracy to the concentrations obtainable by IR and NMR spectroscopy. We have thus demonstrated the feasibility of the Py-MS approach to polymer blend analysis.

EXPERIMENTAL

Triblend samples Twenty-one rubber vulcanizates were prepared (Table 1). The elastomers were styrene-butadiene rubber (SBR, 23.5% styrene), cis-1,4-polybutadiene rubber (BR), and natural rubber (polyisoprene, NR). Banbury mixes 1, 2, and 3 (Table 1) were compounded masterbatches of 100% SBR, BR, and NR, respectively. Triblends (7-12) and diblends (13-21) were prepared by blending small samples from these mixes. Banbury mixes 4, 5, and 6 represent higher black/oil loading, emulsion polybutadiene (EBR), and peroxide cure versions of recipe 10. All compounds were slightly overcured to ensure complete vulcanization. Pyrolysis-mass

spectrometry

The triblend samples were analyzed by Curie-point Py-MS using an Extranuclear 5000-l system (University of Utah). A small amount of the vulcanizate was ground into fine pieces at liquid nitrogen temperature, after which a suspension was formed in toluene with sonification. About 40 pg of rubber was then applied to the pyrolysis wire and air-dried with gentle rotation of the wire. Each sample was analyzed in triplicate or quadruplicate. Py-MS conditions were as follows: Curie-point temperature 510°C temperature rise time 150 ms, total heating time 4 s, expansion chamber temperature 150°C electron energy setting 12 eV, scan rate 1000 a.m.u./s, mass

99

range scanned m/z 50-220, total scan time ca. 25 s. Approximately spectra were obtained for each sample, and the results are reported single spectrum that is the integration of these individual spectra.

200 as a

Data analysis Prior to multivariate analysis, spectra were normalized with regard to total intensity in order to correct for sample size [17,18]. Large peaks with a poor reproducibility were excluded from the calculation of the 100% total ion intensity value, because of their strong influence on the relative intensities of the other peaks in the spectrum. As a consequence, the total ion intensity exceeds 100%. Poorly reproducing spectra (19 out of a total of 73) were deleted prior to multivariate analysis. The resulting spectra were subjected to factor and discriminant analysis as

TABLE 1 Rubber compounds a. Banbury mixes

SBR Suspension BR (BR) NR Emulsion BR (EBR) Carbon black Processing oil Stearic acid Antiozonant Tackifier Antioxidant Zinc oxide Sulfur Accelerator Dicumyl peroxide

1

2

3

4

5

6

100 _ 60 15 0.9 2 2 2 3 1.7 1.5 _

100 _

_ 100 _

40 20 40 _ 80 40 0.9 2 2 2 3 1.7 1.5 _

40 40 20 60 15 0.9 2 2 2 3 1.7 1.5 -

40 20 40 _

10

11

12

40 20 40

20 40 40

20 20 60

b. Mill blends of compounds

60 15 0.9 2 2 2 3 1.7 1.5 -

SBR (1) BR (2) NR (3)

8 60 20 20

c. Mill blends of compounds

60 15 2 2 2 3 4.5

1,2, and 3 (triblends)

7 SBR (1) BR (2) NR (3)

60 15 0.9 2 2 2 3 1.7 1.5 -

9 40 40 20

20 60 20

1,2, and 3 (diblends)

13

14

15

16

17

18

19

20

21

80 20 -

50 50 -

20 80 -

20 80

50 50

80 20

80 20

50 50

20 80

loo

described previously [19-211. In principle, discriminant analysis calculates linear combinations of the mass variables. The first discriminant function describes the maximum of the between-group variance to within-group variance ratio. The between-group variance is the difference between groups of samples, in our case the various rubber triblends. The within-group variance represents the differences within the groups (as shown by replicate analyses). The second orthogonal discriminant function describes the maximum of the residual between-to-within group variance. The linear combinations described by discriminant analysis can be plotted in the form of bar-graphs, which facilitates comparison with the original of the original spectra on the discriminant spectra [20]. The “intensities” functions are called discriminant scores. Canonical variate analysis [19,20] compares two data sets (preferably sets of factors for reasons of mutual independence). Linear combinations of the variables describing the data sets are constructed in such a way as to obtain a maximum correlation between the scores of the two data sets. Linear combinations calculated in this way are called canonical variates. In this study, the canonical variate method was used to find the maximum correlation between the concentrations of the constituents in the triblends and the pyrolysis data. For the mixture analysis two procedures were used. The first approach was supervised target rotation [22]. This method calculates the composition of the mixtures by constructing three component axes. Since this method uses preknowledge, the spectra of the pure components, it is called “supervised”. For many materials analyzed by Py-MS, pure reference spectra are not available. Therefore, unsupervised mixture analysis methods have been developed that do not need reference spectra [20,23]. The principle is that mass variables “characteristic” for a certain component will show a correlated behavior with the component axes. The result is a clustering of “mass axes” near the component axes. Scanning the discriminant space with a rotational procedure that quantifies the clustering of the variables results in a so-called “ variance diagram”. Local maxima in this variance diagram point in the direction of the component axes, even when the pure components are not present in the data set 1231. The mathematically derived mass spectral patterns show high similarities with related chemical components.

RESULTS

AND DISCUSSION

Supervised target rotation Typical pyrolysis-mass spectra of the three constituents of the triblends are presented in Fig. 1. The spectra show clear contributions from monomers, dimers, trimers and fragment ions. The spectrum of styrene-butadiene

101

rubber shows a dominant styrene monomer peak at m/z 104, but also a clear contribution from butadiene at m/z 54. If the pyrolysis-mass spectrum of a ternary mixture can be regarded as a linear combination of the spectra of the three individual compounds, and if the sum of the three components is normalized to lOO%, then the data points representing the different triblends should all plot in a two-dimensional space. However, if significant chemical interaction occurs between the com-

STYRENE-BUTADIENE

RUBBER

B 54

5.0 BUTADIENE RUBBER z-. C r” 0 .E 2.5- B s 54 .-

BB 108

II’ 6

T

0 1

(

I!

60

* Solvent

-1111 I 11 I 1

80

100

" 11.I

120

140

NATURAL RUBBER

81.;

,'S

160

, I' ,

180

'I

204 ,'I ,

200

r

220 m/z

Fig. 1. Pyrolysis-mass spectra of the original components in the triblends. Styrene-butadiene rubber: styrene (S) at m/z 104, butadiene (B) at m/z 54, dimers at m/z 108 and 158, trimer at m/z 162; butadiene rubber: monomer, dimer, and trimer at m/z 54, 108, and 162, respectively; natural rubber: monomer, dimer, and trimer at m/z 68, 136, and 204, respectively. The ion at m/z 92, marked with an asterisk, is residual toluene (the suspension medium).

(a)

ORIGINAL

DATA

SBR I

(b)

CORRECTED

DATA

Fig. 2. (a) This triangular representation of the normalized three component system is derived from the scores of the first two discriminant functions. The comer points represent the spectra points of the pure components. The divisions in the triangle represent 20% concentration differences. The actual (0) as well as the calculated values (0) are indicated. (b) Triangular plot after correcting for the relatively high “response factor” of NR.

103

ponents during the Py-MS procedure, then the intrinsic dimensionality of the data space may be expected to be higher than two. Our discriminant analysis results show that 93% of the relative between-group variance is explained by two discriminant functions, with the third function accounting for only 3%. This indicates that there are no significant chemical interactions between the components during the pyrolysis process. Apparently, under the experimental conditions used, the pyrolysis process is characterized by the occurrence of unimolecular decomposition reactions and the virtual absence of recombination reactions. The results derived from discriminant analysis, are presented in Fig. 2a. The concentrations of all components can be derived from the spectral points. The differences between the actual values and the values as given by discriminant analysis show a systematic difference in the direction of natural rubber. This indicates that the mass spectral response of this component is higher than is to be expected on the basis of its weight. With simple mathematical means it is possible to calculate the response factor of natural rubber, i.e., the ratio between its relative weight in the original mixture and the relative total ion current in the spectrum. This response factor appeared to be rather constant for all mixtures. After correcting for this response factor, the plot presented in Fig. 2b results. It is obvious that this correction improves the results; the averaged error for the three components is less than 4%. Although the correction is based on the response factor as calculated from all spectra, the results for data sets of “unknowns” will be in the same range. This can be concluded from the fact that the response factor for all mixtures in this and another similar data set (analyzed two months before, not shown) did not differ significantly. The averaged errors for the three components are SBR 3X%, BR 4.5%, and NR 2.9%. Note in our analysis that the diblends (samples 13-21) were treated the same as the triblends (samples 4-12). This accounts for the appearance of small negative percentages in some cases (see Fig. 2). Certain samples (6, 7, and 13 in particular) tended to give larger prediction errors than the others. Samples 7 and 13 were high in actual SBR content and low in BR. Since butadiene is a common monomer for both SBR and BR, there are extensive spectral overlaps between the SBR and BR pyrolyzates. It thus seems reasonable that it might be more difficult to predict the individual component concentrations in blends that are high in SBR content and low in BR. The error for sample 6 was also somewhat large. In this case the difficulty can likely be attributed to the fact that this was a peroxide cure. Thus the rubber network for sample 6 has different types of crosslinks than are present in the other samples (with sulfur cures). This no doubt results in a somewhat different pattern of pyrolyzates. Sample 4 differed from the others in the levels of carbon black and oil that were used. Sample 5 substituted emulsion BR for solution BR. The errors in prediction for these samples were similar to those of the other

104

rubber blends. It is pleasing that the compounding changes in samples 4 and 5 did not significantly alter the quantitative results. It might be noted that the substitution of emulsion BR in sample 5 did not cause any significant change in the pyrolysis-mass spectrum. Thus Py-MS is apparently not very sensitive to this change. Microstructural effects in Py-MS of technical polymers have not yet been studied in any detail. Nonsupervised

variance diagram

In cases where pure reference components are not available, the nonsupervised variance diagram method can be useful. In the variance diagram in Fig. 3, the natural rubber axis is found to lie in the same direction as the axis determined by canonical variate analysis. The butadiene rubber axis, however, lies in a different direction. That is, while the butadiene axis as determined by canonical variate analysis has an angle of 115” with respect to Dl, the axis as determined by the variance diagram has an angle of 150” with respect to Dl, a difference of 35”. This apparent inconsistency can be explained, since the “real” butadiene axis not only has contributions from the butadiene rubber component in the mixtures, but also from the

,.NR NRCV

Fig. 3. Variance diagram in the space described by the first two discriminant functions. The local maxima at O”, HO’, and 220’ are the component axes of natural rubber (NR), cis-1,4-polybutadiene rubber (BR), and styrene-butadiene rubber (SBR), respectively. The projections of the discriminant scores (not shown) on these axes give the relative concentrations of the pure components as determined by canonical variate analysis. These are indicated as follows: NRCV, BRCV, SBRCV, and RBRCV (Real Butadiene Rubber CV, see text).

105

styrene-butadiene component. In order to check this, the real butadiene concentration was calculated from the contributions of the butadiene rubber and the styrene-butadiene rubber component. The direction of the real butadiene axis as determined by canonical variate analysis appears at 162” with respect to Dl, which gives a difference of 12” between the direction of this component as determined by the variance diagram. Since the variance diagram is calculated in steps of lo”, this difference is within the experimental error. Finally, the styrene-butadiene axis as determined by the variance diagram lies at 220” with respect to Dl, while the axis as determined by canonical variate analysis lies at an angle of 236” with respect to Dl. The difference is

S

104

l.O-

STYRENE (BUTADIENE) RUBBER AXIS

1.0 is

BUTADIENE RUBBER AXIS

I

1.0

16 6

1’

NATURAL RUBBER AXIS

II

136

0.

93 I

I, I40

III

1

160

I

I

180

I

204 9

200

I

1

220 m/z

Fig. 4. Mathematically extracted patterns associated with the local maxima in the variance diagram in Fig. 3. Comparison with the original spectra in Fig. 1 shows clear similarities.

106

16”. This relatively large difference may be caused by a styrene contribution from sources other than the styrene-butadiene rubber, since styrene is a common contaminant in pyrolysis-mass spectra [17]. The spectral patterns associated with the directions as determined by the variance diagram are shown in Fig. 4, while the spectra of the original components are shown in Fig. 1. The similarities between the mathematically extracted spectra and the original spectra are obvious.

CONCLUSIONS

From the results presented here, it appears that Py-MS can be used for quantitative as well as qualitative analysis of technical polymer blends. The supervised target rotation method showed a precision of better than 5% which is comparable to the results obtainable by NMR and IR methods. The nonsupervised variance diagram method showed good quantitative results, although the butadiene rubber axis had to be corrected for a contribution of styrene-butadiene rubber. The qualitative results (i.e., the discriminant spectra as derived from the variance diagram) clearly showed that spectra of pure components can be extracted mathematically from a data set of mixtures, without any preknowledge. The identification of the extracted spectra, however, is not yet done by the computer. Overall these results are quite encouraging. The feasibility of the Py-MS approach to multiblend rubber analysis has certainly been demonstrated.

ACKNOWLEDGEMENTS

Appreciation is expressed to The BFGoodrich Company for support of this work. The expert technical assistance of Tim Miller and Tony Schurtz in sample preparation and analysis is acknowledged. Alice Harper, Rich Komoroski and Hugh Diem provided helpful discussion and advice. The encouragement and support of Jerry Pausch and Kris Baranwal are also appreciated.

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