Measuring the Size and Shape of Silicones: The Utilization of

Chapter 11

Measuring the Size and Shape of Silicones: The Utilization of Chemometrics and Spectroscopy 1,3

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Martyn J. Shenton , Henryk Herman *, and Anthony C. Dagger Downloaded by UNIV LAVAL on July 11, 2016 | http://pubs.acs.org Publication Date: March 10, 2003 | doi: 10.1021/bk-2003-0838.ch011

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Polymer Research Centre, School of Physics and Chemistry, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom Smith and Nephew GRC, York Science Park, Heslington, York YO10 5DF, United Kingdom Current address: A G Fluoropolymers, P.O. Box 4, Thornton, Lancashire FY5 4QD, United Kingdom 3

When describing a polymer, two fundamental parameters often used are its molar mass and topology, i.e. what is its size and shape? Traditional routes for measuring these properties, although generally accurate, are often time consuming and unsuitable for real-time on-line processing. However it is possible to use molecular spectroscopy and chemometrics to probe molar mass and molecular topology information of some polymers. In the case of linear polydimethylsiloxanes (PDMS) the number average molar mass () may be measured by Raman spectroscopy using this approach in a few seconds once chemometric models have been constructed. In addition, distinguishing between PDMSs of the same but different topology, i.e., linear versus cyclic, is also possible. In this chapter an account of the preparation of approximately monodisperse linear and cyclic PDMS, their spectroscopic analysis and how the chemometric models to predict their size and shape are developed is given. Finally, some possibilities of this approach of polymer analysis are discussed. n

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© 2003 American Chemical Society

Clarson et al.; Synthesis and Properties of Silicones and Silicone-Modified Materials ACS Symposium Series; American Chemical Society: Washington, DC, 2003.

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Downloaded by UNIV LAVAL on July 11, 2016 | http://pubs.acs.org Publication Date: March 10, 2003 | doi: 10.1021/bk-2003-0838.ch011

Introduction When describing a polymer, two fundamental parameters that are often quoted include its size (molar mass) and shape. Traditional routes for measuring these properties, (e.g., viscometry (7), gel permeation chromatography (GPC) (2) and trapping (3) etc.) although generally accurate, are often time consuming and unsuitable for real-time on-line processing. In addition, environmentally unfriendly solvents at elevated temperatures may be required to dissolve the polymer. Hence our desire to develop a rapid, remote, precise and accurate methodology to address these questions. We outline a methodology that uses Raman spectroscopy and chemometrics to yield molar mass and molecular shape information (4). Spectroscopy is a widely used, rapid and non-destructive analytical tool that can probe structural information about materials; when coupled to the powerful data-handling characteristics of chemometrics (5) (multivariate data analysis), correlations between spectral features and property data may be modeled. Previous examples where spectroscopy coupled to chemometrics has been used includes gasoline blending (6,7) real-time quantitative monitoring of filler amount in extruded polymer (8) and condition monitoring of insulating materials (9,10). In this paper, we demonstrate that spectroscopy coupled to chemometrics can give molar mass and topology information for polydimethylsiloxanes (PDMS) within a few seconds, once a model has been constructed. This makes on-line monitoring of molar mass characteristics of siloxanes, and potentially other polymers possible.

Experimental Preparing a series of pure, well characterized and approximately monodisperse polymeric materials can be a difficult task. However, the ability to use preparative GPC (77) tofractionatea polydisperse polymer can make this task at least manageable; this was the approach used in these studies with silicones. Preparation of Linear PDMS Fractions A polydisperse PDMS with trimethyl end groups supplied by Dow Corning Ltd. (DC 200 series) wasfractionatedby preparative GPC. After purification, this resulted in a series offifteenapproximately monodisperse PDMS fractions with molar masses between 1000 and 31000; see Table 1. These clear liquid samples were purified and stored in glass vials.


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Table 1. Molar Mass Data from GPC Analysis for the Fractionated Linear PDMS


Linear Fraction

/g mot

/

1070 1430 2285 3370 5135 7090 9430 10525 10935 12400 14005 16146 22029 25396 30510

1.01 1.02 1.04 1.08 1.05 1.10 1.03 1.04 1.07 1.05 1.08 1.06 1.05 1.07 1.03

1

w

n

LF01 LF02 LF03 LF04 LF05 LF06 LF07 LF08 LF09 LF10 LF11 LF12 LF13 LF14 LF15

n

Adapted with permissionfromreference 4. Copyright 2000 John Wiley

Preparation of Cyclic PDMS Fractions As with the linear PDMS, a polydisperse cyclic PDMS melt was fractionated by preparative GPC. However, as high polydisperse cyclic PDMS is not commercially available, aring-chainequilibration reaction was performed (72). The formation of large cyclic polymers by the ring-chain equilibration reaction relies on the random cleavage and reformation of backbone bonds. When this is performed in dilute solution, a distribution of polymers may be formed. One starting material for preparing cyclic PDMS is octamethylcyclotetrasiloxane (D ). In the presence of a strong acid (e.g. trifluoromethanesulfonic acid), hydrolysis of the Si-O backbone occurs to form silanol and silyl ester end groups, see step (i) in Figure 1. The silyl ester is readily hydrolysed to form another silanol-terminated chain, step (ii). The random recombination of silanol terminated chains results in an increase in the average molar mass of the silicones in solution, step (iii). If silanol end groups on the same molecule condensate, which is relatively likely in dilute solution, then a cyclic molecule is formed. These reactions result in a mixture of linear and cyclic PDMS that may be separated by solutionfractionation.Full details of the preparation are available elsewhere (13,14). Afterfractionationand purification, a series of fifteen approximately monodisperse cyclic PDMS fractions with molar masses between 1100 and 29000 were available; see Table 2. These clear liquid samples were purified and stored in glass vials. Small cyclics may be obtained by vacuum distillation of the ring fraction prior to fractionation. n

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3

+

HO \

3

OH,

3

I Si—

3

CF S0 H 3

3

CH

il Π

CH

3

CH,

^ i - O - S i -

fH

CH,

— — O H

CH

Γη

- S . - C — S C

F

+

3

+

CH 3

OH.

CF3SO3H

HO—Si— \ CH,

Figure L Mechanism ofpolymerization of silicones during the Chojnowski and Wilczek ring-chain equilibration reaction (12f

r Ο

Β

3

CH. I -SiCH


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FT-Raman Spectroscopy The PDMS samples were analyzed in the liquid phase using a Perkin-Elmer System 2000 FT-Raman instrumentfittedwith a Nd:YAG laser (λ = 1064 nm). Spectra were recorded from 200 to 3500 cm" at a resolution of 4 cm" with 16 scans co-added. Analysis time was typically 30 seconds per sample. Glass is effectively invisible to Raman spectroscopy; hence spectra were recorded through the glass vials negating further sample preparation. The range normalized Raman spectra of the linear and cyclic PDMS fractions listed in Tables 1 and 2 are illustrated in Figures 2 and 3 respectively. Note that the spectra of each fraction are not distinguished; by eye they are all similar. The main spectroscopic features were assigned according to Table 3 (15). Downloaded by UNIV LAVAL on July 11, 2016 | http://pubs.acs.org Publication Date: March 10, 2003 | doi: 10.1021/bk-2003-0838.ch011

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There are two notable features in the spectra in Figures 2 and 3. Firstly, and most importantly is that all of the main spectral features have similar positions and shapes. Hence by eye, it is impossible to allocate a spectrum to a molar mass, let alone a topology. However, this is where one of the strengths of chemometrics is realized, as the mathematics is able to model small but significance variances between the data sets. The second feature of the spectra is the appearance of a baseline variation at lower wavenumbers. This is due to sample (and impurity) fluorescence. Taking thefirstdifferential can negate this or, as in this study, use of a spectral region where the fluorescence background is negligible, i.e. above 2500 cm" . 1

Results

Chemometric Analysis When using chemometrics, a well-characterized calibration sample set is required. In this case, a series of linear and cyclic PDMS fractions with a in the range of 1000 to 31000 were accessible. In all cases, the polydispersities were less than 1.25. These molar masses and whether the PDMS was linear or cyclic were used as the property data. The chemometrics can extract "hidden" information about the spectra and seek correlations with the property data; this process is known as principal components regression (PCR) and results in the formation of a model. The chemometric analysis was performed using commercially available software called Unscrambler v7.5 from Camo Ltd. (16). n


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Table 2. Molar Mass Data from GPC Analysis for the Fractionated Cyclic PDMS


Cyclic Fraction CF01 CF02 CF03 CF04 CF05 CF06 CF07 CF08 CF09 CF10 CF11 CF12 CF13 CF14 CF15

/

1

/g mot

W

1.23 1.20 1.14 1.15 1.18 1.12 1.19 1.12 1.15 1.15 1.11 1.19 1.13 1.13 1.15

1119 1526 1800 2290 2890 3732 4832 6223 7911 10331 14007 17169 21143 24619 29036

1.0 H

3200

2800

2400 2000 1600 1200 800 wavenumber /cm" Figure 2. FT Raman spectra of linear PDMS fractions Adapted with permissionfromreference 4. Copyright 2000 John Wiley 1


400


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Table 3. Peak Assignments for the FT-Raman Spectra of Linear and Cyclic PDMS (4). (Asym. - asymmetric; sym. - symmetric; def. - deformation; str. - stretch and r. -rock) Assignment

1

Peak /cm' 2966 2906 2501 1412 1264 1200-1000

CH asym. str. CH sym. str. C H def. overtone C H asym. def. C H sym. def. Si-O-Si asym. str. 3

3

2

3

3

1

Peak /cm' 863 845, 792 757 710 646 491

Assignment rSi(CH,),lr. Asym. rSi(CH ) ] r. Sym. rSi(CH ) ] r. Si-C sym. str. Asym [Si(CH ) ] r. Si-O-Si sym. str. 3

3

2

3

3

3

Adapted with permissionfromreference 4. Copyright 2000 John Wiley



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Ο

10000

20000 30000 Measured Figure 4. Predicted vs. measured plot for linear PDMS (adaptedfrom Adapted with permissionfromreference 4. Copyright 2000 John Wiley η

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The C-H region (2750 - 3250 cm") of the spectra of the linear PDMS was used in the PCR analysis. This resulted in a model that had 4 significant principal components (PCs). The PCs describe the variance across the data set; a more detailed description of chemometrics and the role of PCs see the text by Esbensen et al. (5). Figure 4 contains the output from the chemometric modeling in the form of a predicted versus measure plot for . In a perfect model, the slope and the regression coefficient would both be unity. However, if both of these values are about or in excess of 0.9, then a good model is generally formed, as is the case here. The model must be validated with independent samples; in this case, further DC200 siloxanes were used. It was found that for siloxanes with a > 5k, then the model was good. However, for the smaller siloxanes, massesfromthis method tended to be over-estimated. We believe this is due to end-group effects that can be accounted for in further iterations of the modeling process. By generating a model containing all of the data for the linear and cyclic PDMS fractions, a cluster type analysis may be performed as illustrated in Figure 5. n

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From thisfigure,it is clear that the linear and cyclicfractionsform different clusters as the weighting of principal components differ. On the left-hand side of thefigure,the linear siloxanes are clustered in a triangle; the cyclic species are clustered in an arc on theright-handside. This means that topologically different, yet chemically and even mass identical PDMS fractions may be distinguished by spectroscopy when processed by this model. More recently, preliminary investigations on commercial silicone elastomers have been performed. Correlations between the spectra of a silicone containing medical device and the number of sterilization cycles it has been subjected to, have been found, thereby making a rapid and non-destructive method of condition assessment for this device available. Furthermore, in Clarson et al.; Synthesis and Properties of Silicones and Silicone-Modified Materials ACS Symposium Series; American Chemical Society: Washington, DC, 2003.


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Figure 5. PC plot showing linear and cyclic grouping another study, correlations between mechanical properties of silicones and their spectra have also been found. These results complement our earlierfindingson cellulose containing insulating materials where correlations between spectral features and age (17) and mechanical strength (9,10) were modeled. The powerful combination of spectroscopy and chemometrics has been used for many years in the oil and gas (6-8) industry and for pharmaceutical (18) and medical analysis (19,20). and with improvements in computer processing speeds and the availability of commercial chemometric software, the authors hope and expect many other applications to develop.

Conclusions Molar mass and topology of PDMS can be determined using spectroscopy when coupled to the powerful multivariate data processing algorithms that encompass chemometrics. The use of chemometrics has enabled these correlations between spectral features and molar mass to be obtained from the CH region of the Raman spectrum, even though by eye, no significant variation between the spectra of siloxanes of differing molar masses can be seen. These preliminary investigations suggest that extracting Tiidden' information from easy to measure data, in this case Raman spectroscopy is possible when robust chemometric models are generated. The authors are currently trying to gain a greater understanding of the scientific meanings of these correlations before applying this approach to other polymeric systems.


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References 1. 2. 3. 4. 5.


6. 7. 8. 9.

10.

11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

Dodgson, Κ.; Sympson, D.; Semlyen, J.A., Polymer 1977, 18, 1265-1268. Moore, J.C., J. Polym. Sci., Part A 1964, 2, 835-843. Garrido, L.; Mark, J.E.; Clarson, S.J.; Semlyen, J.A., Polym. Commun. 1985, 26, 53-55. Shenton, M.J.; Herman, H.; Stevens, G.C., Polym. Int. 2000, 49, 10071013. Esbensen, K.; Schönkopf, S.; Midtgaard, T., "Multivariate Analysis - In Practice." Camo ASA, Oslo, Norway, 1994. Lambert, D.; Descales, B.; Llinas, J.R.; Espinosa, Α.; Osta, S.; Sanchez, M.; Martens, A. Analusis Magazine 1995, 23, 4, M9-M13. Espinosa, Α.; Sanchez, M.; Osta, S.; Boniface, C.; Gil, J.; Martens, Α.; Descales, B.; Lambert D. Oil and Gas Journal 1994, October, 49-56. Fischer, D.; Bayer, T.; Eichhorn, K.-J.; Otto, M. Fresenius J. Anal. Chem. 1997, 359, 74-77. Shenton, M.J.; Herman, H.; Stevens, G.C.; Heywood, R.J., Proc. 8th Int. Conf. Dielect. Mat. Meas. Appl. 2000, IEE Conf. Publication 473, 346351. Shenton, M.J.; Herman, H.; Stevens, G.C.; Heywood, R.J. In Ageing Studies and Lifetime Extension of Materials; Mallinson, L.G. Ed.; Kluwer Academic, New York, 2000; 359-365. Dodgson, K.; Sympson, D.; Semlyen, J.A., Polymer 1978, 19, 1285-1289. Chojnowski, J.; Wilczek, L., Makromol. Chem. 1979, 180, 117-130. Shenton, M.J. D.Phil. Thesis, University of York, UK, 1995. Dagger, A.C. D.Phil. Thesis, University of York, UK, 1999. Lipp, E.D.; Smith, A.L. In The Analytical Chemistry of Silicones; Smith, A.L. Ed.; John Wiley & Sons Ltd., New York, 1991; Ch. 11. http://www.camo.no Ali, M.; Emsley, A.M.; Herman, H.; Heywood, R.J., Polymer 2001, 42, 2893-2900. Lee, K R; Zuber, G; Katrincic, L., Drug Development and Industrial Pharmacy 2000, 26, 2, 135-147. Abuzaruraloul, R.; Gjellan, K.; Sjolund, M.; Lofqvist, M.; Graffner, C.; Drug Development And Industrial Pharmacy 1997, 23, 8, 749-760. Gambart, D.; Cardenas, S.; Gallego, M.; Valcarcel, M.; Analytica Chimica Acta1998,366, 1-3, 93-102.


Measuring the Size and Shape of Silicones: The Utilization of

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