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Chemical Imaging of Thermoplastic Olefin (TPO) Surface Architecture Hannah R. Morris,† John F. Turner II,‡ Branka Munro,§ Rose A. Ryntz,§ and Patrick J. Treado*,| School of Pharmacy, Duquesne University, Pittsburgh, Pennsylvania 15282, Center for Light Microscope Imaging and Biotechnology, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, Visteon Automotive Systems, An Enterprise of Ford Motor Company Plastics, Trim, Paint Operation, Automotive Components Division, 401 Southfield Avenue, P.O. Box 6231, Dearborn, Michigan 48121, and ChemIcon Inc., 7301 Penn Avenue, Pittsburgh, Pennsylvania 15208 Received June 4, 1998. In Final Form: November 30, 1998
In the automotive industry, ethylene-propylene rubber (EPR) is mixed with polypropylene (PP) to form a thermoplastic olefin (TPO) for use as car bumpers and fascia. An adhesion promoting primer, chlorinated polyolefin (CPO), is spray coated onto the TPO surface to increase adhesion of the base and clear coat paints to the low surface free energy TPO substrate. The surface morphology of rubber domains within the CPO-coated TPO substrate contributes strongly to the material characteristics, including impact resistance and adhesion properties. However, elastomer-phase analysis is challenging using traditional microanalysis imaging techniques. We employ fluorescence and Raman chemical imaging to characterize the TPO architecture in order to better understand the surface properties of coated TPO. Fluorescence imaging makes use of Nile red (NR), a fluorescent solvatochromic dye, solvated in the primer, which is effective in differentiating rubber from polypropylene on the basis of large variations in the fluorescence quantum efficiency. Confocal fluorescence chemical imaging performed on TPO coated with NR-doped CPO shows a thin (2-3 µm) layer of elastomer that has migrated to the TPO surface. Raman chemical imaging is in direct agreement with the fluorescence experiments by measuring the intrinsic vibrational signatures of CPO, EPR, and PP without the need for dyes or stains. Raman contrast is enhanced using cosine correlation analysis, a novel multivariate processing technique that provides chemical contrast on the basis of differences in spectral shape.
Introduction Polypropylene (PP) has experienced wide commercial application since it was first discovered in the 1950s. While PP has the advantages of being inexpensive, easily processed, and chemically resistant, its utility is limited by its low impact strength and high brittleness temperature.1 Blending ethylene-propylene rubber (EPR), a toughening agent, with PP enhances the performance of the material by imparting greater ductility, improving crack resistance, and increasing impact strength.2 Composite materials comprised of PP blended with EPR are called thermoplastic olefins (TPOs) and are used widely in the automotive industry as car bumpers and fascia. TPO is lightweight, recyclable, inexpensive, and easily molded into complex geometries, making it a cost-effective alternative to traditional metallic bumper materials. Furthermore, TPO is more resilient than steel and prevents costly damage upon minor impact to the bumper.4 As a result, the use of TPO in the automotive industry has dramatically increased. * To whom correspondence should be addressed. † Duquesne University. ‡ Carnegie Mellon University. § Visteon Automotive Systems. | ChemIcon Inc. (1) New Advances in Polyolefins; Chung, T. C., Ed.; Plenum Press: New York, 1992. (2) Multicomponent Polymer Systems; Miles, I. S., Rostami, S., Eds.; John Wiley and Sons: New York, 1992. (3) Bucknall, C. B. Toughened Plastics; Applied Science Publishers Ltd.: London, 1977. (4) Ryntz, R. A.; Ramamurthy, A. C.; Holubka, J. W. J. Coat. Technol. 1995, 67, 23.
Successful toughening of the blended TPO is highly dependent on the dispersion of EPR in PP. The EPR serves as a stress concentrator, initiating and controlling the growth of crazes which are the first stage of fracturing in the blended material. Ideally, the rubber is dispersed throughout the PP as tiny microspheres having radial diameters of approximately 1-20 µm. When stress is applied, rubber, being less rigid than PP, absorbs the impact by dissipating large amounts of energy. Crazes are initiated at points of maximum principal strain, which occur near the center of the rubber particle. The craze continues to propagate until the stress concentration at the leading edge falls below the critical level for growth or is inhibited by another rubber particle.3 The addition of rubber allows large numbers of small crazes (micron size) to form rather than small numbers of large crazes (millimeter size) that are found in native PP. As a result, the integrity of the blended material is increased and it can withstand greater strains and stresses applied to the system. Topcoat paints, such as base and clear coats, are applied to the TPO substrate to enhance the cosmetic appearance of the material and to increase its longevity. Thermoplastic olefins have a low surface free energy which minimizes the adhesion of paint to the surface. A solvent-borne adhesion promoter, chlorinated polypropylene (CPO), increases the paintability of the bumper and is used to prime the surface of the TPO substrate. The TPO surface morphology influences the adhesion of the primer and determines how well the topcoats resist peeling and gouging.
10.1021/la980653h CCC: $18.00 © 1999 American Chemical Society Published on Web 02/09/1999
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Figure 1. Generally accepted model of injection molded TPO after priming with CPO. The CPO-coated TPO is microtomed in cross sections (10 µm thick) for examination using Raman chemical imaging.
The process used to mold TPO into bumpers significantly affects the degree of stratification and blending within the composite. Thermoplastic olefin is typically formed by either injection or compression molding. Injection molding creates layering in the TPO due to thermal gradients and shearing forces.5 Figure 1 shows the generally accepted model of TPO surface architecture after injection molding and application of the CPO primer. A transcrystalline PP layer is formed first due to nonideal crystallization resulting from rapid cooling between the mold and the TPO surface. Beneath this layer, PP spherulites form where the crystallization conditions are more ideal. Rubber domains form directly below these PP layers and are elongated due to the shear forces incurred during injection molding. The CPO primer is spray coated on the TPO and diffuses through the layers of PP to entangle with the EPR. This entanglement enhances adhesion of the CPO to the TPO. The rubber-rich region must lie close enough to the surface in order for the primer to adhere to the TPO substrate properly. In the model shown in Figure 1, elastomer is not localized at the surface of the TPO. However, upon spray application of adhesion promoter, the CPO solvent may swell elastomer phases localized below the PP surface layer, causing elastomer to migrate and coalesce at the TPO surface. Previously, surface elastomer phases have not been detected in CPOtreated TPO. In compression molding, no layering effects are evident, and little or no adhesion of the paint occurs. Consequently, the bumpers are typically formed by injection molding.5 In the event injection molding process conditions change, as they often do in manufacturing environments, there is an ongoing need to improve the adhesion properties of the TPO surface and to develop analytical methods that noninvasively monitor the TPO surface and near-surface morphologies. Many conventional analytical techniques have been used to investigate TPO. For example, differential scanning calorimetry and X-ray fluorescence microscopy have been employed to study PP crystallinity in TPO.7 Time-of-flight secondary-ion mass spectrometry (TOF-SIMS) has been used to map the penetration of CPO into the TPO substrate by monitoring the unique chlorine (5) Morris, H. R.; Munro, B.; Ryntz, R. A.; Treado, P. J. Langmuir 1998, 14, 2426. (6) Prater, T. J.; Kaberline, S. L.; Holubka, J. W.; Ryntz, R. A. J. Coat. Technol. 1996, 68, 83. (7) Ryntz, R. A.; Xie, Q.; Ramamurthy, A. C. J. Coat. Technol. 1995, 67, 45.
ion of the adhesion promoter at different sampling depths.6 While powerful, TOF-SIMS is inadequate for differentiating EPR from PP, since both produce similar molecular fragments. The most widely used technique to investigate TPO blending is transmission electron microscopy (TEM). In TEM analysis of TPO, ruthenium and titanium oxide stains accentuate the amorphous areas, allowing differentiation of crystalline PP from noncrystalline regions. However, rubber and amorphous PP are not readily differentiated. By etching the rubber away with formic acid, areas that contain amorphous PP can be assessed. Voids within the TPO sample that are visible after etching are assumed to have been composed of EPR. Unfortunately, etching causes the substrate to swell and alters the TPO morphology at the surface. Clearly any analytical technique that readily differentiates CPO, EPR, and PP surface distribution at high spatial resolution without affecting the TPO substrate morphology would be a powerful tool for routine TPO analysis. Recently, we have employed fluorescence and Raman chemical imaging to characterize TPO architecture in a rapid and noninvasive manner. In chemical imaging, highresolution images are collected at discrete spectral passbands that correspond to diagnostic regions of the fluorescence or Raman spectrum. With appropriate processing of the chemical image data sets, typically using multivariate analysis techniques, molecularly specific images can be obtained. In previous fluorescence chemical analysis of TPO, we have employed Nile red (NR), a solvatochromic fluorescent dye, to monitor the uniformity and film thickness of the CPO surface coatings on TPO.5 In addition, it was established that NR was effective in differentiating EPR and PP component distribution on the basis of the spectral properties of NR when solvated within EPR and PP. Despite the speed and effectiveness of fluorescence chemical imaging using NR, the technique requires a tag to reveal material architecture. In previous Raman chemical imaging studies, we evaluated the effectiveness of differentiating CPO, EPR, and PP without the use of dyes. Instead, we base our analysis on the uniqueness of the Raman signatures in the C-H stretching region of the spectrum.5 Classical least squares (CLS) analysis was performed on the chemical image data to determine the spatial distribution of the
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Figure 2. Schematic diagram of an LCTF Raman chemical imaging microscope. R is a pair of holographic notch rejection filters, M1 and M2 are front surface mirrors, L1 and L2 are focusing lenses, and D is a diffuser. For Raman point mapping, M2 redirects the light into a fiber optic, F, that is coupled to the monochromator.
three components within 200 µm of the surface in the cross-section samples. The surface properties of CPO-coated TPO are most influenced by the architecture within 25 µm of the surface. To evaluate the surface architecture, we have analyzed TPO at higher magnification than that used for our previous studies. In this manuscript, we describe volumetric confocal fluorescent studies of NR-doped CPOcoated TPO performed on intact substrates without the use of sample cross-sectioning. In addition, we describe Raman point scan mapping and wide field imaging performed in the fingerprint region. The fingerprint region reveals more chemical specificity than the C-H region, making it easier to discriminate between CPO, EPR, and PP, especially when combined with a novel and effective multivariate image analysis technique we describe as cosine correlation analysis (CCA). Experimental Section Confocal Fluorescence Microscopy. Monochromatic fluorescence excitation is provided by a 100 W mercury arc lamp (Olympus) filtered with a 514.5 nm dielectric band-pass filter (Oriel) in epi-illumination. The light is reflected into the microscope (Olympus, BH-2) with a 527 nm dichroic beam splitter (Olympus) oriented at 45° to the incident light and is focused onto the sample with a 50× objective (Olympus, 0.80 N.A.). The Stokes-shifted fluorescence emission image is collected by the objective and is transmitted through a 590 nm long-pass filter (Omega) which passes the fluorescence while rejecting the excitation wavelength. This image is collected using a 14 bit, thermoelectrically cooled (-50 °C), charge-coupled device (CCD) detector (Princeton Instruments, TE-CCD-768-k/2) having 768 pixels × 512 pixels (9 µm2). A stepper motor (Prior Scientific Instrument Ltd, H128AV3) is attached to the focusing stage of the microscope for z-axis control. In confocal operation, wide field fluorescence images are acquired at 150 nm steps from the TPO surface into the substrate. Each fluorescence image contains contributions of light (blur) from axial planes above and below the nominal focal plane. Deconvolution software (CellView, Scanalytics) employs a nearest neighbor deblurring algorithm to supress out of focus light from each image plane by subtracting a weighted amount of the image intensities immediately above and below the plane of interest. The deblurred images are recombined to form a volumetric image that is rendered for presentation.
Raman Chemical Imaging Microscope. The Raman imaging microscope is diagrammed in Figure 2. The illumination design has been described in detail elsewhere.8 Briefly, laser excitation is provided by a 647.1 nm Kr+ ion laser (Coherent, Innova 330-K) which has been defocused using an f/16 lens (ESCO Products, Inc.), so that wide field illumination is achieved at the sample. The laser light is directed to a holographic notch filter optimized for oblique illumination (Kaiser Optical Systems, HNF647-1.0) which is placed in the optical path of an infinity-corrected microscope (Olympus, BH-2). The reflected light propagates through a 50× objective (Olympus, 0.80 N.A.) and illuminates the sample. The Raman emission is collected with the same objective and is transmitted through the holographic notch filter, which rejects light at the laser wavelength. A second notch filter is positioned after the first filter and provides additional rejection of the Rayleigh line. The Raman emission is filtered with a 9 cm-1 band-pass liquid crystal tunable filter (LCTF) constructed in the Evans split-element geometry (ChemIcon, Raman LCTF). The LCTF is a compact electronically tunable filter capable of high-fidelity Raman imaging and has been described previously.9-15 Raman images are collected using a 16 bit, liquidN2-cooled (-100 °C), slow-scan charge-coupled device (CCD) detector (Princeton Instruments, LN/CCD-512TKB) having 512 pixels × 512 pixels. (20 µm2). Raman point mapping experiments are performed by removing the defocusing lens so that the laser excitation produces a 5 µm spot at the sample using the 50× objective. A swing away mirror (M2 in Figure 2) is placed before the LCTF to redirect the Raman emission to a fiber-optic bundle (CeramOptics) containing seven fibers in a circular, six around one, geometry. The other end of the fiber is configured in a linear geometry and is focused on the entrance slit of a single-stage 0.5m spectrograph (Chromex 500IS). The Raman spectrum is collected with a CCD detector (Princeton Instruments, LN/CCD-512TKB) located at the exit focal plane of the monochromator. (8) Schaeberle, M. D.; Treado, P. J. Presented at the Pittsburgh Conference, Atlanta, GA, March, 1997; Paper 913. (9) Turner, J. F., II; Treado, P. J. Proc. SPIEsInt. Soc. Opt. Eng. 1997, 3061, 280. (10) Lyot, B. C. R. Acad. Sci. 1933, 197, 1593. (11) Masterson, H. J.; Sharp, G. D.; Johnson, K. M. Opt. Lett. 1989, 14, 1249. (12) Miller, P. J. Metrologia 1991, 28, 145. (13) Morris, H. R.; Hoyt, C. C.; Treado, P. J. Appl. Spectrosc. 1994, 48, 857. (14) Evans, J. W. J. Opt. Soc. Am. 1958, 48, 142. (15) Evans, J. W. J. Opt. Soc. Am. 1949, 39, 229.
2964 Langmuir, Vol. 15, No. 8, 1999 Materials. The TPO material (Solvay Engineering Polymers) consists of 70 wt % polypropylene (PP) and 24 wt % ethylenepropylene rubber (EPR) (Exxon). The adhesion promoter consists of chlorinated polyolefin (CPO) solvated in xylene/toluene/ aromatic 100 solution (45:45:10) (Red Spot Paint and Varnish Co.). The CPO primer is spray coated onto the TPO substrate, forming a nominal film thickness of 7.5 µm, and is then baked at 121 °C for 30 min. Thin cross sections (10 µm) were cut with a sledge microtome (Jung Model 1400, Leica) and mounted on a quartz microscope slide for Raman analysis. Confocal fluorescence studies are performed on samples in which the solvatochromic fluorescent dye, Nile red (Exciton), is dissolved in the adhesion promoter at a nominal concentration of 186 µM (0.005 wt %). The fluorescently doped adhesion promoter is spray coated on the TPO substrate, and the composite is examined from the coated top surface. Multivariate Analysis. Multivariate analysis techniques applied to the study of chemistry (chemometrics) employ a large variety of mathematical approaches and are classified according to the type of analysis performed.16-18 The first step in many chemometric analyses is to remove instrument response from the data and then to format the data for additional processing. Chemical (i.e. hyperspectral) imaging experiments produce vast amounts of data and are often subjected to data reduction techniques as well. The intent of data reduction is to simplify the analysis by preserving the most important structures found within the data. Less important data structures including instrument response, sample background, and noise are ideally not considered during subsequent analyses. Factor analysis, a family of data reduction techniques, is often performed to reduce the amount of data. Customarily, principal component analysis, principal factor analysis, or common factor analysis is performed on the data to extract factors or scores that best represent either the variation or the similarity between the data populations of the variables. For example, principal component analysis reassembles the data as linear combinations of the original variables so that the largest variance in the data corresponds to the first principal component. Each subsequent principal component is orthonormal to the previous component and represents the largest remaining variance in the data. The maximum number of principal components allowed is equal to the number of variables measured and maintains the data structure but does not reduce the dimensionality of the data. Typically, the smallest set of principal components necessary to represent some large percentage of the total variance in the data is used for further analyses. A number of tests have been developed to determine the number of principal components to retain.16-18 A second class of multivariate analysis, cluster analysis, seeks to find meaningful and systematic differences among the objects measured and differs from factor analysis by assuming that the population of objects is heterogeneous.16-18 Also a data reduction technique, cluster analysis shifts attention from the reduction of the number of variables describing homogeneous objects to the sorting of objects into fewer categories on the basis of differences in their measured variables. Each group of objects is treated mathematically as a single object that represents the variable mean of the cluster to a precision described by the variance or standard deviation within the cluster.18 Once the data have been preprocessed, the next task is to estimate the composition of the sample. Conventional algorithms, such as partial least squares, classical least squares, and multiple linear regressions, employ a variety of regressions that fit data populations to a predescribed set of observations made on samples of known composition. The data describing the measurements of the known samples are collectively referred to as a training set and are necessary for most multivariate analyses.16-18 In practice, a priori information about a specific sample may be unavailable or incomplete, making conventional analysis requiring the use of training sets difficult or nearly impossible. (16) Multivariate Analysis; Dillion, W. R., Goldstein, M., Eds.; John Wiley and Sons: New York, 1984. (17) Cliff, N. Analyzing Multivariate Data; Harcourt Brace Jovanovich, Inc.: Orlando, FL, 1987. (18) Geladi, P.; Grahn, H. Multivariate Image Analysis; John Wiley and Sons: New York, 1996.
Morris et al. In complex materials, training sets may not be adequate for describing all of the complex chemical architectures present in heterogeneous samples. Even when possible, constructing spectral image training sets involves significant sample preparation and acquisition time. Cosine Correlation Analysis (CCA). Cosine correlation analysis is a multivariate technique that assesses similarity in spectral data sets and image data sets.19 Other similarity analysis techniques include Euclidian distance16 and spectral angle mapping,20 which have been used for spectral library searching and image contrast generation in remote sensing applications, respectively. CCA assesses chemical heterogeneity without the need for training sets. Cosine correlation analysis identifies differences in spectral shape and efficiently provides chemical based image contrast that is independent of absolute intensity, which makes it well suited to Raman and fluorescence chemical imaging. All multivariate analyses are performed using routines written in Matlab (Matlab 5.0, The Mathworks, Inc.). To perform CCA, chemical image data are reorganized into an n × p matrix D, where n is the number of pixels in each image frame and p is the number of frames. Since each frame is acquired at a different wavelength, p is also the number of wavelengths (λ1, λ 2, ..., λp). Typically, it is necessary to remove bias from the data before analysis by subtracting the mean of each row vector from the element in each row. The resulting matrix D* would contain the mean corrected spectrum for each pixel, but the negative intensities would be inconsistent with physical reality. A more intuitive preprocess step is to remove bias by adjusting each spectrum so that the minimum baseline intensity is zero. In CCA, a comparison is made between each spectral vector in the data set and a reference spectral vector. If we choose the jth vector as our reference vector, the statistical correlation rij between the elements of the ith and jth rows, for example, is given by
rij )
Di*‚Dj* nσiσj
(1)
where σi and σj are the standard deviations of the ith and jth rows, respectively. By substituting |Di*||Dj*| cos θ for the inner product Di*‚Dj* and expanding σi and σj, eq 1 becomes
rij ) cos θ
(2)
where θ is the angle between Di* and Dj*. Consequently, the correlation between two spectra can be thought of as the cosine of the angle between the vectors in p dimensional space. The tail of each vector resides at the origin, and the heads have the coordinates expressed by the corresponding spectral vector. Because cos θ remains unchanged if the lengths of Di* and Dj* are altered, cosine correlation is scale invariant and is immune to nonuniform illumination of the sample until the spectral signalto-noise becomes so small that the noise level begins to dominate the overall shape of the spectrum. In practice, CCA is performed pixel by pixel on chemical image data sets. For example, the correlation score CiR between the spectrum at the ith pixel Di* and a fixed reference spectrum R is calculated as p
∑D CiR )
i,k*Rk
k)1
|Di*||R|
(3)
where k is the wavelength frame number. In the resulting CCA image, CiR replaces the spectrum at each pixel, reducing the (19) Turner, J. F., II. Chemical Imaging and Spectroscopy Using Tunable Filters: Instrumentation, Methodology, and Multivariate Analysis. Doctoral Dissertation, University of Pittsburgh, 1998. (20) Kruse, F. A.; Lefkoff, A. B.; Boardman, J. B.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H. Remote Sens. Environ. 1993, 44, 145.
Thermoplastic Olefin Surface Architecture dimensionality of the data from p + 2 (p spectral dimensions and 2 spatial dimensions) to 1 + 2 dimensions. Performing CCA using one reference vector does not ensure image contrast between pixels representing different chemical compositions, since many different spectra may be reduced to the same cosine score. For example, the collection of spectral vectors having identical cos θ values can be conceptualized as lying along the periphery of the multidimensional cone (a hypercone) having the reference vector for its axis of symmetry and its vertex at the origin. To remove all symmetry, p correlations need to be performed on the image data set using p different reference vectors. Even for large p values, most of the symmetry can be removed using only a few reference vectors. As in principal component analysis, it is often unnecessary to perform all possible correlations in order to achieve a high degree of chemical specificity. Ideally, the reference vector space should span the maximum variance of the data space, so that most of the variance of the data is contained along the directions of the first few orthonormal reference vectors. The choice of reference vectors can influence the efficiency of the data reduction. It is not necessary to know the number of pure components in the sample using this method. In fact, a histogram of the scores often reveals the number of spectral types and is useful in determining the number of components in a chemical system. If the number of components is already known, CCA can be used to determine if unknown species (i.e. foreign contaminants) might be present. Even for small numbers of correlations, all wavelength information about the sample is employed in generating the correlation score, allowing unsuspected differences in spectral shape to produce image contrast. Chemical identity can be assigned by matching correlation scores from the image data set to correlation scores calculated for the spectra contained in a spectral library using the same CCA routine. If a priori information about the sample is available, CCA can be used semiquantitatively to indicate relative changes in chemical composition. For example, the pure component spectra of the CPO-coated TPO data presented here form the set of reference vectors. Each pixel score in the set of three correlation images represents the relative correlations to the three pure components, CPO, EPR, and PP. The advantage of this type of analysis is that it provides rapid chemical contrast that correlates to known chemistries. Each correlation image maps the degree of presence for one of the pure components. Chemical heterogeneities in each pure component correlation image are visualized by pseudocoloring each pixel according to its pure component correlation value. It should be recognized that reference vectors for the pure component spectra may not be orthogonal and may correlate with each other. As a result, the sum of the pure component correlation values for each pixel may not be unity. For example, each image pixel may contain a range of correlation scores that represent a population having a standard deviation about some mean value. Neighboring regions in the image may have different mean values but sufficiently large standard deviations in their correlation scores so as to overlap one another. It can be difficult to draw object boundaries in the correlation image even though clear trends in the images are observable. To determine whether regions of the image are statistically similar enough to be classified as recognizable objects, a scatter plot of the CCA data is performed. For CCA of CPO-coated TPO, a scatter plot can be generated so that each Cartesian axis corresponds to a pure component reference vector. Each image pixel is plotted according to the coordinates described by its correlation scores for CPO, EPR, and PP. Neighboring regions in the image that also appear together in the scatter plot must contain similar chemical compositions and can be considered as a single, discrete image object.
Results Confocal Fluorescence Imaging. Confocal fluorescence images are captured of NR-doped CPO-coated TPO by focusing at 150 nm axial steps from the surface into the bulk. A nearest neighbor deblurring algorithm is applied to contiguous image frames to supress image blur
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Figure 3. Confocal fluorescence volume images of NR-doped CPO-coated TPO: (A) top view; (B) oblique view; (C) side view. A 3.3 µm thick volume was sampled in 150 nm increments from the coated TPO surface. Circular spheres, 3-6 µm in diameter, correspond to EPR elastomer domains that have bloomed to the surface during penetration of the CPO primer solvent. The elastomer domains extend 2 µm into the coated TPO substrate. Microionized talc, a TPO additive, provides nucleation centers for PP crystallization and appears as tiny bright specks in the figure. The size bar in panel B corresponds to 5 µm.
and enhance the axial resolution of the microscope.21 Fluorescence-coated image contrast, shown in the volumetric images of Figure 3, is due to the increased quantum efficiency of NR-doped EPR relative to doped PP.5 The top surface and oblique volume views of coated TPO are shown in Figure 3A and B. The dark spherical region near the top of Figure 3A corresponds to a void in the CPO film, and the tiny bright specks are believed to be talc, a stabilizer added to provide nucleation centers for PP crystallization. Bright circular spheres, with 3-6 µm lateral diameters, correspond to EPR domains which have migrated to the surface during the diffusion of the adhesion promoter into the TPO bulk. A side view of the fluorescence volume data, shown in Figure 3C, displays the EPR spheres localized within the top 2 µm of the TPO substrate. The confocal images are evidence that EPR domains bloom to the surface due to swelling of the elastomer by the primer solvent during the penetration of the adhesion promoter into the TPO substrate. C-H Region Raman Image Analysis Using CCA. Chemometric analysis is an integral part of generating the Raman-based contrast of CPO-coated TPO and is necessary to discern subtle spectral differences among CPO, EPR, and PP in the C-H region of the Raman spectrum (2800-2950 cm-1). Figure 4A shows the bright (21) Russ, J. C. Image Processing Handbook, 2nd ed.; CRC Press: Boca Raton, FL, 1994.
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Figure 4. Cosine correlation analysis of Raman CPO-coated TPO images acquired in the C-H stretching region (2800-3000 cm-1) employing pure component LCTF Raman spectra as reference vectors. The Raman images were collected with a 20× objective (N.A. 0.46). (A) Bright field image; (B) CPO correlation image; (C) EPR correlation image; (D) PP correlation image of coated TPO. The size bar in panel D corresponds to 20 µm, and the air/surface interface has been accentuated with a dotted line.
field image of a 10 µm thick section of coated TPO. In a previous study, classical least squares (CLS) analysis was applied to the C-H stretching region Raman image data.5 Pure component correlation images generated using CCA are shown in parts B, C, and D of Figure 4, which reveal CPO, EPR, and PP distributions, respectively. Figure 4B reveals that CPO is localized to the first 20 µm near the surface. At the spatial resolution of Figure 4C, EPR is found colocalized with PP, shown in Figure 4D, at depths greater than 20 µm. The image magnification is not sufficient to resolve individual EPR domains which are smaller than 10 µm. However, upon careful inspection, it appears from Figure 4C that a thin layer of EPR resides at the coated TPO surface. The CCA results are consistent with the previous CLS analysis,7 suggesting that CCA is a viable chemometric tool for treatment of TPO Raman image data. Fingerprint Region Raman Point Mapping. To improve upon previous studies, Raman analysis is performed in the fingerprint region of the spectrum and at higher spatial resolution. The fingerprint region of the Raman spectrum offers greater molecular specificity
compared to the C-H stretching region. Figure 5 shows Raman microspectra of TPO components in the fingerprint region using a dispersive monochromator, where parts A, B, and C of Figure 5 show spectra of the solid commercial polymers CPO, EPR, and PP, respectively. The EPR spectrum largely overlaps the Raman bands of CPO and PP, making Raman imaging of EPR a distinct challenge, even in the fingerprint region. Raman point mapping is used to determine the extent of sample heterogeneity in the outermost 25 µm of the CPO-coated TPO cross section. A Raman point map image is collected by systematically moving the sample in 5 µm increments from the coated TPO surface into the bulk. This process was performed at 5 µm displacements on the sample surface so that a 25 × 25 µm2 sample area is measured. Figure 6 shows Raman spectra of coated TPO collected sequentially from the surface into the bulk. The band at 1600 cm-1 is associated with the end-terminating maleic anhydride group of CPO and is an effective marker band for CPO distribution, which diminishes as one goes deeper into the TPO from the surface. The band intensities
Thermoplastic Olefin Surface Architecture
Figure 5. Pure component dispersive Raman spectra acquired in the fingerprint region (900-1700 cm-1) using a 50× objective (N.A. 0.80) of (A) CPO, (B) EPR, and (C) PP.
Figure 6. Raman microprobing of a 10 µm thick CPO-coated TPO cross section. Spectra are taken from the sample surface into the bulk in 5 µm increments using a 50× objective (N.A. 0.80). The laser illumination spot size is approximately 5 µm at the sample. A decrease in the band intensity at 1600 cm-1 indicates that CPO resides mostly near the coated TPO surface. The ratio of the 1306 to 1330 cm-1 bands indicates that the PP distribution is greatest beyond 15 µm from the sample surface.
at 1306 and 1330 cm-1 indicate the relative EPR and PP content, respectively. Figure 7A shows the bright field image of the CPOcoated TPO substrate with the air interface on the left edge and a boxed region from where the point-mapped Raman spectra are acquired. Cosine correlation analysis is performed on the spectral image data collected from 1200 to 1500 cm-1 by using Raman spectra of solid commercial polymers of CPO, EPR, and PP as reference vectors. Parts B, C, and D of Figure 7 describe the distributions of CPO, EPR, and PP within the coated TPO substrate, respectively. Pure component spectral correlations are mapped to pixel color so that deep red indicates the greatest degree of correlation and dark blue denotes the least degree of correlation. The chemical contrast is based on the subtle spectral differences between 1200 and 1500 cm-1 that are attributable to the major components varying in concentration from the surface into the bulk.
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The analysis confirms that CPO resides primarily within the first 10 µm and is almost nonexistent 25 µm into the sample, as shown in Figure 7B. This is consistent with the results shown in Figure 4, previous studies employing Raman chemical imaging, and TOF-SIMS analysis on baked CPO-coated TPO.5,6 Figure 7C demonstrates that EPR is present nearly everywhere and is at the highest concentration 10-15 µm into the sample. Polypropylene (Figure 7D) is diminished at the surface and has its greatest concentration 15-25 µm into the coated TPO substrate. While point mapping provides a cursory assessment of the CPO-coated TPO heterogeneity; the spatial resolution of point mapping is limited by the spot size of the illumination, in this case 5 µm. Achieving submicron spatial resolving power with point Raman microscopy is not feasible and represents a significant limitation when micron dimension domain structures need to be assessed, as in the case of TPO. We employ wide field Raman chemical imaging, capable of diffraction-limited resolution, to visualize the component distribution in coated TPO substrates.22 Wide Field Fingerprint Region LCTF Raman Chemical Imaging. The spatial resolution of a wide field Raman chemical imaging experiment is effectively determined by the convolution of the pixel size of the CCD and the image magnification at the CCD. The microscope magnification and CCD pixel size employed have been selected so that they do not limit the spatial resolution of the instrument. Rather, diffraction is the limit to resolution, which is on the order of 300 nm. Wide field imaging is performed in less time than a comparable point mapping experiment when the number of spectral channels m is less than the number of image pixels n and the laser power density remains just below the damage threshold for the sample. The wide field time advantage is further enhanced when high-resolution images collected at only a few wavelengths provide adequate chemical information.23 The LCTF is well suited to wide field spectral imaging at discrete wavelengths and is ideal for monitoring noncontiguous spectral regions. Wide field LCTF Raman chemical imaging is performed from 1260 to 1400 cm-1 on the same spatial location of the CPO-coated TPO cross section analyzed in Figure 7. The 1260-1400 cm-1 region of the Raman spectrum, shown in the highlighted area of Figure 5, is specific enough to differentiate CPO, EPR, and PP. Even though the CPO Raman band at 1300 cm-1 significantly overlaps the EPR band, it is sufficiently broad to be detected using CCA. A bright field image of the sample is shown in Figure 8A. Cosine correlation analysis provides pure component images for CPO, EPR, and PP that are shown in Figure 8B, C, and D, respectively. The degree of pure component correlation for each image is greater for deep red features and less for dark blue regions. Each pure component image is assigned to a separate color channel of a red-greenblue (RGB) display. The red channel is assigned to PP, and the green and blue channels are assigned to EPR and CPO, respectively. The channels are merged into a single RGB composite image, as shown in Figure 9A. Color overlap corresponds to colocalization of the pure components. Four distinct regions, labeled R1 through R4, are readily discernible in Figure 9A. The region labeled R1 does not (22) Morris, H. R.; Hoyt, C. C.; Miller, P.; Treado, P. J. Appl. Spectrosc. 1996, 50, 805. (23) Schaeberle, M. D.; Morris, H. R.; Turner, J. F., II; Treado, P. J. Anal. Chem., in press.
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Figure 7. Raman point mapping. (A) High-fidelity bright field image of CPO-coated TPO. Spectra taken from the boxed region of the sample in part A are reconstructed into a spectral image data set that is analyzed using CCA by employing the pure component spectra shown in Figure 5. Pure component correlation images of (B) CPO, (C) EPR, and (D) PP in coated TPO. The size bar in panel D corresponds to 5 µm.
contain sample and represents the air interface at the CPO-coated TPO surface, R2. Figure 10 shows five representative spectra from the image pixels labeled in Figure 9A which correspond to different chemical compositions within the coated TPO substrate. The first spectrum is taken from the thin film layer, R2, at the coated TPO surface. This layer appears more homogeneous than regions deeper in the substrate and is 2-3 µm thick. The second and third spectra are from the region labeled R3 and describe elongated domains that are approximately 2.5 × 10 µm2 in size. Spectra 4 and 5, respectively, correspond to the bulk matrix and dispersed inclusions found in R4. Comparison of the spectra in Figure 10 to the spectra in the highlighted region of Figure 5 indicates that, even at 300 nm spatial resolving power, CPO, EPR, and PP are highly blended and are not discernible as discrete pure component domains. Nevertheless, trends in the pure component distributions are observable. For example, the PP distribution is less at the surface than those of CPO and EPR, which are strongly colocalized near the air interface. Figure 9B illustrates relative changes in the pure component correlation as a function of sampling depth from the coated TPO surface. Cosine correlation analysis reveals that the highest concentration of CPO is in the
thin film region, R2, at the CPO-coated TPO surface (Figures 8B and 9B). CPO is only slightly less concentrated in R3 and is dramatically decreased in R4. While the majority of the CPO resides in the first 18-22 µm, some of the CPO penetrates deeper into the sample and forms irregularly dispersed domains. The distribution of EPR closely matches the distribution of CPO in regions R2 and R3, which suggests they are colocalized. Away from the coated TPO surface, the EPR is distributed throughout the material and is colocalized with diminishing amounts of CPO. There is almost no colocalization of CPO with PP, suggesting that the EPR provides a flow network that promotes CPO penetration. The EPR domains in R4 act as stress concentrators that enhance the impact performance of the bulk material. Figures 8C and 9B indicate high EPR content in R4 along its boundary with R3 and show smaller domains throughout R4. A thin film of EPR colocalized with CPO resides at the sample surface and is consistent with our previous results. The correlation image for PP (Figure 8D) and the average correlation plot for PP shown in Figure 9B corroborate this finding and show a clear decrease in PP in the thin film region, R2. Most of the PP is distributed beyond 20 µm in R4 and acts as the foundation for CPO/ EPR entanglement that promotes adhesion of CPO to the
Thermoplastic Olefin Surface Architecture
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Figure 8. Cosine correlation analysis of high-fidelity Raman CPO-coated TPO images using the pure component spectra as reference vectors. (A) Bright field image of CPO-coated TPO. The size bar corresponds to 5 µm. Pure component correlation images of (B) CPO, (C) EPR, and (D) PP. A thin film of EPR resides at the coated TPO surface and gradually extends as elongated domains into the PP matrix, providing a channel network for the penetration of the CPO primer. Raman image collection is performed using a 50× objective (N.A. 0.80).
TPO substrate. The polar CPO coating increases the surface free energy of the substrate and enhances the chemical adhesion of the topcoats to the TPO. Analysis of the CCA scatter plot allows heterogeneities in the pixel population to be grouped into categories having similar chemical composition. Each axis of the 3-dimensional scatter plot corresponds to one of the pure component correlation results. Figure 11 shows the scatter plot of the correlation scores for the total pixel population of the sample, except for the pixels in the air region, R1, which are ignored to simplify the plot appearance. The correlation data appear clustered into two distinct categories. The first population cluster is dominated by PP. The second population cluster contains predominately CPO and EPR at equal levels. To better understand the regions described in Figure 9A, the scatter plot has been color encoded so that pixels contained within R2 are plotted in blue and those for R3 and R4 are plotted in green and red, respectively. A set of ellipses encompassing two standard deviations about the mean of each population are drawn for each principal plane of the coordinate system. The pixel data from R4 are contained almost entirely within the first cluster, suggesting that the dominant component in R4 is PP with less EPR and even less CPO content. The distinction between R2 and R3 is more complicated because the pixels from these regions intimately share the same naturally occurring cluster. A closer look reveals that the mean correlation values of R2 and R3 are statistically similar in the EPR dimension but are different along the CPO and PP axes when analyzed according to the null hypothesis for the Student’s t Test. Region 2 correlates
better with CPO than R3, which is understandable, since the CPO primer is spray coated on the TPO surface after injection molding. We assert that the average distribution of EPR domains is more uniform before the TPO is primed. After spray coating, the primer solvent front penetrates more rapidly through the EPR regions than the PP regions, since EPR is more soluble. Control studies which were performed on EPR and PP indicate that EPR is much more soluble in the primer than PP. When introduced into the primer at ambient temperature, EPR begins to swell immediately but PP remains nearly unchanged even after several days. Blooming of EPR into the CPO coating coincides with CPO penetration through the elastomer-rich regions which form a channel network into the bulk PP. Figure 9B supports our assertion and indicates that the EPR diffuses into the CPO coating at the surface to a greater extent than PP. Colocalization of CPO and EPR occurs mostly near the surface and gradually decreases into the bulk where the elastomer persists and the CPO concentration diminishes. Solvation of the EPR nearest the surface causes better blending of EPR with CPO, creating the thin film layer, R2. The standard deviation ellipses provide an indicator of the chemical heterogeneity and segregation within each region.24 For example, the least chemical heterogeneity is visible along the coated TPO surface, R2, and it has the smallest overall spreading in its pixel population. As the segregation of the pure components into domains becomes (24) Hailey, P. A.; Doherty, P.; Tapsell, P.; Oliver, T.; Aldridge, P. K. J. Pharm. Biomed. Anal. 1996, 14, 551.
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Figure 9. Cosine correlation analysis of CPO-coated TPO. (A) RGB composite image showing PP, EPR, and CPO in red, green, and blue, respectively. Color overlap indicates pure component colocalization. The size bar corresponds to 5 µm, and Raman image collection is performed using a 50× objective (N.A. 0.80). (B) Average correlation scores as a function of sampling depth for CPO, EPR, and PP. The dotted lines emphasize the surface morphology of coated TPO and help illustrate the solvent interaction of the CPO primer with EPR and PP.
more prevalent, the span of the data bounded by each region becomes broader in the direction of the chemical difference. This is clearly demonstrated for EPR and CPO in regions R2 and R3. Spreading along their pure component axes indicates that EPR and CPO become more segregated into domains in R3 than in R2 and that the
change in segregation is more pronounced for CPO. This can be visually confirmed in parts B and C of Figure 8, which show nearly homogeneous blending of CPO and EPR for R2 and increased domain structure in R3. The segregation of EPR into domains is greatest in R4, which has been least affected by CPO penetration and solvation.
Thermoplastic Olefin Surface Architecture
Figure 10. LCTF Raman spectra of CPO-coated TPO taken at the points labeled in Figure 9A. Spectral differences correspond to changes in chemical distribution in the coated TPO and are confirmed using CCA.
The segregation of PP is relatively unaffected by the application of primer. Conclusion Confocal fluorescence and Raman imaging results establish EPR blooming for the first time and bring a new insight to CPO-coated TPO architecture by indicating the
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formation of a thin film containing the adhesion promoter and elastomer at the surface. We have introduced a powerful technique for rapidly generating chemically specific image contrast, namely cosine correlation analysis (CCA). The use of CCA for characterizing chemical domains formed within CPO-coated TPO substrates has been validated by comparing CCA-generated image contrast to earlier results based on conventional CLS analysis. Confocal fluorescence imaging of NR-doped CPO-coated TPO indicates blooming of EPR to the surface after application of the adhesion promoter. Nearest neighbor reconstruction of the volumetric data shows EPR domains 3-6 µm in diameter at the surface that extend 2 µm into the coated TPO. This was further substantiated by CCA processing of point-mapped Raman spectra of coated TPO in cross section, which indicates that EPR is widely distributed, even at the coated TPO surface. In addition, Raman point mapping and CCA indicate that CPO is mostly localized near the coated TPO surface and that PP is most concentrated 15 µm below the surface. Raman point mapping provides a coarse spatial description of TPO morphology and is not adequate for identifying small chemical domains in CPO-coated TPO. High-fidelity LCTF Raman imaging and CCA performed on the same sample verify and extend the point mapping results. Cosine correlation analysis indicates colocalization of CPO and EPR throughout the first 18-22 µm. The CPO distribution is greatest at the coated TPO surface and
Figure 11. Scatter plot of cosine correlation scores. A projection of the data into the CPO-EPR plane is included to provide a better view of data spreading along the EPR correlation axis. Red data points correspond to image pixels from region R4 in Figure 9A, and green and blue data points correspond to R3 and R2, respectively. The ellipses encompass two standard deviations about the mean for the pixel populations in R2, R3, and R4. Regions having smaller variations in their pixel populations have less sample heterogeneity and better blending of the pure component chemistries.
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diminishes deeper into the sample. The EPR domains extend farther into the TPO and act as a channel network for CPO penetration, improving the adhesion of CPO to TPO. The CPO primer preferentially solvates into EPR, diminishing the segregation of elastomer into domains nearer the surface while leaving the PP matrix nearly unchanged.
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Acknowledgment. The authors extend thanks to the Ford Motor Company for financial support and to Dr. Gilbert Walker and Dr. David Waldeck (University of Pittsburgh) for their generous insight. LA980653H
