Anal. Chem. 1995, 67, 4316-4321
Raman Chemical Imaging: Noninvasive Visualization of Polymer Blend Architecture Michael D. Schaeberle,t Costas 0. Karakatsanis,* Clifford J. Lau,* and Patrick J. Treado**t Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, and Bayer Corporation, 100 Bayer Road, Pittsburgh, Pennsylvania 15205
A generally applicable methodology for routine, noninvasive chemical image characterization of materials is described. The methodology is based on Raman imaging microscopy employing a solid-state acoustooptic electronically tunable imaging spectrometer integrated within a laser-excitedoptical microscope. Multichannel chargecoupled device detection is employed to capture the Raman images. Chemical imaging with electronically tunable filters can be performed automatically under computer control, making analysis of unknowns routine. The Raman chemical imaging system is applied to the visualization of polypropylene/polyurethane blends. Raman chemical imaging is complementary to electron microscopy for polymer blend characterization; however, Raman imaging does not require as extensive sample preparation. Raman imaging provides images having molecular specificityat the spatial resolution of the optical microscope, without the use of dyes or stains. Chemical image contrast is provided by the intrinsic vibrational signature of the polymer constituents in the blend. On a macroscopic scale, many multicomponent materials can be thought of as homogeneous. When investigated at the microscopic level, these same materials often show distinct chemical heterogeneity. It is important to characterize how chemical heterogeneity is distributed in solid-state materials because the chemical composition and architecture dictate material function. Understanding the distribution/function relationship of heterogeneous systems is fundamental to the fabrication of advanced composite materials. Chemical imaging, including Raman chemical imaging, is a means to visualize and quantitate material heterogeneity. The field of chemical imaging is an emerging but vital area in chemistry because chemical imaging methods, based on Raman, infrared, and/or fluorescence imaging, are proving capable of probing material chemical heterogeneity rapidly at high spatial resolution and with molecular specificity.’ Chemical imaging microscopy based on Raman spectroscopy can noninvasively determine the molecular composition and spatial distribution of constituents. A host of Raman imaging microscopy methodologies have been devised since the Raman microprobe was first reported in 1975.2 University of Pittsburgh. Bayer Corporation. (1) Treado, P. J.; Morris, M. D. In Spectroscopic and Microscopic Imaging ofthe Chemical State; Morris. M. D.. Ed.: Marcel Dekker: New York. 1992; Chapter 3. (2) Delhaye, M.; Dhamelincourt, P. J. Raman Spectrosc. 1975, 3, 33-43.
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In general, Raman imaging methods rely on image recon~truction,~~~ or tunable filters5-” to simultaneously collect spatial/Raman spectral information. In this paper, we describe the use of electronically tunable filters for noninvasive imaging of polymer blend domain architecture. Several tunable filter approaches have been demonstrated, including fixed filter/tunable excitation ~ a v e l e n g t h ,rotating ~ dielectric filters,6 liquid crystal tunable filters O,cTFs),’ and acoustooptic tunable filters (AOTFs).8-11 The multispectral information is acquired by capturing images at wavelengths selected by the tunable filter under computer control. In general, tunable filter methods employ wide field illumination, in conjunction with two-dimensional detection. The two spatial dimensions of the image are recorded directly by the multidimensional camera. Image fidelity is limited by the number of pixels in the camera, and the use of high-definition detectors allows the efficient collection of high-definition images. The tunable filter approach provides significant advantages relative to scanning or reconstruction methods. In comparison to scanning approaches, tunable filter methods allow much larger initial laser power to be used because the power density is distributed over the entire sampling area, not focused to a localized spot4 This decreases the chance of the laser initiating sample degradation over the course of the experiment. A time advantage is realized when high-fidelity images (having many pixels) are collected. The laser is no longer required to scan the sample since data from all spatial points are collected simultaneously at each filter bandpass. In scanning approaches, as well as reconstruction methods, movement of the sample, excitation beam, or an encoding mask must be very precise to avoid the introduction of artifacts that will degrade the image. This problem can be eliminated in tunable filters. Since wide field illumination is used, movement of the sample or excitation beam is unnecessary. AOTFs are employed as the imaging spectrometer in the Raman microscope. The AOTF is a novel, compact, solid-state device that is capable of functioning from the W to the mid-IR, (3) Bowden, M.; Gardiner. D. J.; Rice, G.: Gerrard, D. L. J. Raman Spectrosc. 1990, 21, 37-41. (4) Treado, P. J.; Govil, A: Moms, M. D.; Sternitzke, K. D.; McCreery, R. L. Appl. Spectrosc. 1990, 44, 1270-1275. ( 5 ) Puppels, G. J.; Grond, M.: Greve, J. Appl. Spectrosc. 1993, 47, 1256-1267. (6) Batchelder, D. N.: Cheng, C.; Pitt, G. D. Adv. Mater. 1991, 3, 566-568. (7) Morris, H. R.; Hoyt. C. C.: Treado, P. J. Appl. Spectrosc. 1994, 48, 857866. (8) Treado, P. J.; Levin, I. W.; Lewis, E. N. Appl. Spectrosc. 1992. 46, 12111216. (9) Treado, P. J.: Levin. I. W.: Lewis, E. N.Appl. Spectrosc. 1992,46, 553-559. (10) Lewis, E. N.; Treado. P. J.; Levin. I. W. Appl. Spectrosc. 1993, 47, 539-543. (11) Schaeberle, M. D.; Turner, J. F., 11; Treado, P. J. Proc. SHE-Int. Soc. Opt. Eng. 1 9 9 4 , 21 73. 11-20. 0003-270019510367-4316$9.0010 0 1995 American Chemical Society
depending on the choice of the filter's crystal material. AOTFs are employed in the visible for multispectral imaging,l2-I4 fluorescence spectro~copy,~J~ thermal lens spectroscopy,16 visible absorption spectroscopy,g remote sen~ing,1~8'8 and Raman spectro~copy.'~J Mid-IR devices are used for stack gas emission characteri~ation,~~ while near-IR AOTFs are used in process monitoring applications. In contrast to existing methodologies employed for multispectral imaging based on filter wheels, prisms, or gratings, AOTFs have significant advantages. AOTFs provide high optical throughput (diffraction efficiencies > SO%),moderate spectral resolution (50 cm-I), wide tuning range (0.4-5.5 pm for TeOz crystals), rapid tuning ability (25 ps) , random accessibility, and an angular field of view of f5". AOTF principles are well established and have been described in detai1.20-22Operation of the AOTF is based on the interaction of light with a traveling acoustic sound wave in an anisotropic crystal medium. The incident light is d ~ a c t e dwith a narrow spectral bandpass when a radio frequency (rf) signal is applied to the device. By changing the applied rf frequency under computer control, the spectral passband can be tuned rapidly with the benefit of no moving parts. In addition, the diffracted light intensity is proportional to the rf signal power applied. By varying the rf power level, the intensity of the diffracted light can be controlled. AOTF Raman chemical imaging has been applied to studies of model polymer systems.I1 Model studies are important in validating the performance of the Raman instrumentation. Of greater importance is the application of AOTF Raman chemical imaging to industrially relevant polymer systems. In this paper, we describe the application of Raman chemical imaging to the study of polymer blends comprised of polypropylene (PP) and polyurethane (PU). The combination of these polymers produces a material with lower density, better flow at lower processing temperatures, higher stiffness, improved heat deflection temperature (HDT), and reduced moisture absorption compared to thermoplastic polyurethane (TPU). The understanding of the morphology of a polymer blend is important because the morphology determines the material's properties. The morphology of the blend is iduenced by how the polymers are processed. Knowing how the morphology changes with processing conditions allows the polymer chemist to produce the architecture with the desired properties. Generally, bulk morphology is important for bulk properties, but for particular applications, surface morphology is more significant because properties such as adhesion, paintability, wear resistance, solvent resistance, and weatherability are influenced by surface architecture. The components were melt blended in various proportions (20%PP/SO% PU, 40%PP/60% PU, and 60%PP/40% PLJ). In the (12) Chang, I. C. Proc. SPIE-Int. Soe. Opt. Eng. 1992,1703, 24-29. (13) Cheng, L. J.; Chao, T. H.; Dowdy, M.; LaBaw, C.; Mahoney, C.; Reyes, G.; Bergman, K. Proc. SPIE-Int. Soc. Opt. Eng. 1993,1874, 224-231. (14) Cui, Y.;Cui, D.; Tang, J. Opt. Eng. 1993,32, 2899-2903. (15) Kurtz. I.; Dwelle, R; Katzka, P. Rev. Sci. Instrum. 1987,58 (11). 19962003. (16) Tran, C. D.; Simianu, V. Anal. Chem. 1992,64, 1419-1425. (17) Smith. W. M. H.; Schempp, W. V.; Conner, C. P.; Katzka, P. Publ. Astron. SOC.Pac. 1987,99, 1337-1343. (18) Chao, T. H.; Yu, J.; Cheng, L. J.; Lambert, J. Proc. SPIE-Int. SOC.Opt. Eng. 1990,1347. 655-663. (19) Taylor, L. H. OE Rep. 1994,120, 2. (20) Chang, I. C. Appl. Phys. Lett. 1974,25, 370-372. (21) Goultzoulis, A P.; Pape, D. R Design and Fabrication ofAcoust0-OpticDevices; Marcel Dekker: New York, 1994; Chapter 4. (22) Tran. C. D. Anal. Chem. 1992,64, 971A-981A
polymer blends, the individual components can be differentiated by employing signature Raman bands specific to each polymer molecule, and the spatial distribution of the individual polymers within the blend can be visualized readily. As a result, phase information including domain sue and distribution can be discerned. Electron microscopy (TEM and SEM) is typically employed to visualize polymer blends but, especially TEM, often requires the use of contrast agents such as Os04 to visualize domains. These agents are expensive and toxic. In comparison, the AOTF Raman chemical imaging system makes use of intrinsic molecular vibrations to noninvasively probe the sample and unambiguously characterize the blend component distribution. No sample preparation beyond conventional microtomy is required for the AOTF Raman imaging studies. EXPERIMENTAL SECTION Instrumentation. The AOTF Raman chemical imaging system has been described in detail? Briefly, laser epiillumination is provided by a 647.1 nm krypton laser (Coherent 330) coupled to an intinity-corrected microscope (Olympus BHSM-2). In operation, the magnified image is coupled through the AOTF and collected on a red-enhanced (back-thinned), high dynamic range (16 bits) chargecoupled device (CCD) detector (Princeton Instruments, TK512B) having 512 x 512 pixels, each 20 pmz square. Due to the inverse relationship between rf frequency and the resultant diffracted wavelength, 40- 140 MHz corresponds to a spectral filtering range of 1200-550 nm. For 647.1 nm excitation, the full AOTF tuning range provides Raman spectral coverage from -2728 (anti-Stokes region) to 7121 cm-' (Stokes region). Across the free spectral range, the device has a bandpass of -50 cm-I and an achievable tunability of 2.4 cm-I with a wavelength repeatability of better than 1.2 ~ m - l .For ~ AOTF calibration and collection of Raman microspectra a single stage, 0.5 m monochromator (Chromex 5001s) is fiber-optically coupled to the microscope. Raman microprobe spectroscopy is performed for reference on bulk samples of polymer blend pure components, namely polypropylene (PPI and polyurethane (PV). Raman microprobe spectroscopy and chemical imaging are performed on microtomed (10 pm) cross sections of injection molded plaques having varying percentages of PP/PU (20/80, 40/60, and 60/40 wt %). A 50 MHz 80486based computer (Gateway 2000) is used for Raman image collection and processing. Camera control is provided by commercial software (Princeton Instruments, WinView 1.3B). Software written in the C programming language (Borland, C++ 3.1) is used to integrate image acquisition and AOTF control. Raman chemical image visualization and processing are performed using commercial Windows software (ChemIcon, ChemImag 1.0) written to store and enhance the 32 bit floating point spectral image file format (SPIFF) data files. For publication, the Raman images are processed on a Silicon Graphics workstation employing multispectral image rendering software get Propulsion Laboratory, Linkwinds Z.0)23and are printed on a dye sublimation printer Qektronics Phaser SDX). Methodology. The advantage of electronically tunable filters resides in their ability to automate the Raman image analysis process. Automated analysis will provide its greatest impact in (23) Jacobson, A S., Jet Propulsion Laboratory. Personal communication. March 5, 1994.
Analytical Chemistry, Vol. 67, No. 23, December 1, 7995
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Figure I. Dispersive Raman microspecta of the polymer blend pure components: (A) polypropylene and (B) polyurethane.
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Figure 2. AOTF (low resolution) Raman microspecta of the polymer blend pure components: (A) polypropylene and (B) polyurethane
making the evaluation of unknown materials routine. Automation will rely on a rigorous chemical image analysis protocol. We describe here the general methodology for automating the Raman chemical imaging process. In particular, we apply the protocol to polymer blend analysis. The first step in the Raman image analysis involves the collection of a library of reference spectra of the individual blend components. Reference spectra are collected using the Raman microscope as a conventional microprobe by focusing the incident laser to a diffraction-limitedspot at the sample. The Raman scatter is either fiber-optically coupled to a dispersive monochromator or coupled to the AOTF for moderate- and low-resolution spectral discrimination, respectively. Raman spectral detection is provided with the CCD detector. The moderate-resolution (3 cm-l) spectra of the pure components are analyzed for spectral features that can be used to differentiate the components. The low-resolution (50 cm-l) spectra are collected through the AOTF. In general, if the marker spectral bands identified at high resolution are distinguishable at low resolution, individual frames selected by the AOTF can be employed to visualize the unknown sample 4318 Analytical Chemistry, Vol. 67, No. 23, December 1, 7995
constituents. In the event that spectral features are not resolved, spectral image processing strategies such as resolution enhancement, curve fitting, or chemometric processing of the Raman images must be employed. The second step in the analysis involves identifying the extent of sample chemical heterogeneity. To do so, the unknown blend samples are imaged with conventional bright field microscopy. Bright and dark regions of the sample are then sampled with the Raman microprobe in an attempt to correlate microscopic appearance with chemical heterogeneity. In the event that microscopic appearance, as seen in bright field, is indicative of chemical composition, Raman image analysis, while informative, is not essential. However, polymer blend components and many other classes of materials cannot be visualized uniquely with conventional optical microscopy, and Raman chemical image analysis becomes essential. Once it has been established that the sample is heterogeneous, Raman chemical image analysis is performed by systematically tuning the AOTF over a spectral range at discrete intervals. The resulting data set comprises not only a series of Raman images
but also a medimensional matrix of Raman spectra. Collecting both Raman image information and spectral information simultaneously is critical in correlating chemical composition with material morphology. From the unprocessed Raman chemical image data set, optimal Raman foreground and background spectral regions can be identified that provide Raman image contrast that is unique to the molecular species of interest. To enhance Raman image quality, the images are processed, which typically consists of background subtraction or image ratioing (foreground/background), to remove instrument response as well as image artifacts that are not related to the sample but are due to nonuniform illumination, dust on the optics, and detector defects. While image contrast can readily be generated in a Raman experiment, generating chemically significant Raman image contrast can often be problematic. For example, Raman scatter is often superimposed on background autofluorescence. Having a Raman spectrum associated with each pixel in the image makes compositional analysis straightfonvard. even in the presence of significant background. Once the optimal sampling parameters have been identified, high-fidelity Raman images at the identifying peak maximum and background can be acquired and processed to enhance Raman image contrast. When collecting high-fidelity images, significant time saving is realized by limiting the image collection only to those image frames that correspond to the Raman peak maximum and the background. The full Raman data set is no longer acquired, but rather, only those spectral bands providing compcnent differentiation are sampled. It is recognized that relying on unique spectral features to generate chemical image contrast will have limited success when complex, multicomponent systems are imaged. Unique spectral bands may not be readily identified, and multivariate statistical image processing will play an essential role in enhancing image contrast.
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Raman chemical image analysis is applied to the study of polymer blends. Moderate resolution spectra collected by the Raman microscope in microprobe mode are shown, in Figure 1, of the pure, individual components, polypropylene (PP) and polyurethane (PU). By comparing the moderateresolution Raman spectra of the pure components, unique Raman spectral bands are identified that can be used for component identification. The PP spectrum possesses a chain expansion peak at 398 cm-l and skeletal stretches at 806 and 844 cm-I that are readily resolved. The PU spectrum contains a ring vibration at 871 cm-l and a distinctive aromatic ring C-C stretching peak at 1615 cm-L?d For comparison, low-resolution Raman spectra obtained using the AOTF are shown in Figure 2. The 50 cm-' resolution achievable with the AOTF reduces the number of spectral markers that can be used to generate image contrast that is unique to the individual blend components. For example, the PP peaks at 806 and 8-44 cm-I and the PU peak at 871 cm-l are broadened by the AOTF and overlap, making them poorly suited to generate unique Raman image contrast. The Raman band at 398 cm-1 is suited for visualizing PP. while the 1615 cm-I band is suited for visualizing PU. (24) tin-Wen. D.:Collhup. N. B.: Fateley. W.G.: Grasselli.J. G. 7hr Handbook of lnfrored ond Ronon Choroetmistic F h q u e m i s of Orgnnir Molecules: Academic Press: Sa" Diego. 1991, Appendix 3.
Figure 3. (A) Bright field image of the 20/80 wt % blend. Microspecta taken from the blend showing polypropylene ( 6 ) and polyurethane (C).
Bright field microscopy is used in conjunction with Raman microprobing to substantiate that the blends are heterogeneous. A bright field image of the 20/80 (PP/PU) w t % blend is shown in Figure 3A Although contrast is observed in the bright field image, the contrast cannot be amibuted uniquely to the individual blend components. The bright field image contrast is due primarily to sample roughness, which does not uniquely correlate with sample chemism. Raman microspectra indicating the presence of PP (Figure 3B) and PU (Figure 3C) can both be obtained from relatively dark regions of the sample, marked B and C. respectively, in Figure 3A Figure 3, parts B and C, demonstrates that the sample is chemically heterogeneous on the spatial scale of the laser sampling volume. PP features are clearly observable in Figure 3B, but the PU features dominate the spectrum due to the higher average concentration of PU in the laser sampling volume. The polymer blend, while sectioned to 10pm thickness. is still a three Analyfical Chemistry, Vol. 67, No. 23,December 1, 1995
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I Wavenumber (cm-') Figum 4. (A) Bright field image of the 40160 W % blend. Microspecta taken from the blend showing polypropylene (B) and polyurethane (C). dimensional volume, and contributions from beneath the sample surface contribute to the Raman microspectra. A similar analysis was performed on the 40/60 (PP/PLT) wt % blend, shown in Figure 4, and the 60/40 wt % blend, shown in Figure 5. Bright field images of the 40/64 and the 64/40 wt % samples are shown in Figures 4A and SA, respectively. As in the 20/80 wt % blend. the image contrast seen in Figures 4A and 5A cannot he amihuted to the individual blend components. Raman microspectra were taken from random points in each sample to indicate the presence of PP (Figures 4B and 5B) and PU (Figures 4C and 5C). Figure 4, parts B and C. demonstrates the heterogeneous nature of the 40/64 wt % blend. PP features are clearly visible in Figure 48. indicating the presence of PP. The presence of PU is shown in Figure 4C. where the PU features are clearly visible. Figure 5, parts B and C . shows the heterogeneity of the 64/40 wt % blend. The PP features can he observed in Figure 5B, while the PU features are present in Figure 5C. The three-dimensional nature of the sample and the sampling volume of the laser source 4320 Analytical Chemistry, Vol. 67, No. 23, December 1, 1995
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Wavenumber (cm~') Figum 5. (A) Bright field image of the 60140 W % blend. Microspecta taken from the blend showing polypropylene (E) and polyurethane (C). give rise to the PP feature that also appears in Figure 5C. The spectra in Figures 3-5 show that regions of heterogeneity exist in all three blends, and those regions can he distinguished using Raman chemical imaging microscopy. For each of the sectioned blend samples, a Raman image data set was acquired, and spectral image analysis was performed. An exposure time of 4 min was employed to collect each Raman image frame that made up the chemical image data set. A composite of Raman images taken from the data sets is shown in Figure 6. The 20/80 wt %blend can he seen in parts A and B, the 40/64 blend in parts C and D. and the 64/40 wt % blend in parts E and F. The images in Figure 6, parts A, C . and E, are taken at the PP peak maximum (398 cm-I) and ratioed against a background frame (300 cm-I). while the images in parts B, D, and F are of the PU peak maximum (1615 cm-') ratioed against a background frame (1680 cm-I). Because the blend is a twocomponent system, the PP and PU images of each sample in Figure 6 are expected to be comple
A
E Iwo IS00 500 Iwo I5W wsvmumber(cm') wavenumber (cm') Figure 7. AOTF Raman image spectra of polymer blend samples: (A and B) 20/80wl % blend, (C and D) 40/60 wt % blend, and (E and F) 60140 wt % blend. (A, C. and E) Areas of high polypropylene concentration. (6. D.and F) Regions of high polyurethane concentration. The spectra are taken from the numbered regions in Figure 6. Ho
Figure 6. Raman images of blended polymers (A and B) 20180 wl 46 blend, (C and D) 40160 wt % blend, and (E and F) 60140 wt % blend (A, C. and E) Polypropylene images at 398 cm-' ratioed against a background at 300 cm-'. (6, D, and F) Polyurethane images at 1615 cm-' ratioed against a background at 1680 cm-'.
mentary images. Areas that have a high concenkation of PP. the brighter domains in Figure 6, parts 4 C, and E, correlate with the dark areas in parts B. D, and F, which indicates low concentration of PU. The complementary nature of the PP and PU images, for a given wt b blend, are visible in Figure 6. Examination of the Raman images reveals domains within the blend. The domains valy in size but are generally on the order of 3-5pm. Better estimation is difficult because of contributions due to the thickness of the sample. To confirm the identity of the individual components, Raman spectra are shown in Figure 7, plotted through specikic PP and PU domains that correspond to the labeled areas in Figure 6. The Raman microspectra clearly identify that PP and PU are present in local concentrations within the domain regions. Anticipated trends in the spectra can be observed, as well. The peak intensity of the PP band relative to that of the PU band increases with increasing percentage of PP. Decreasing the percentage of PU in the blend gives rise to a decreased intensity of the 1615 cm-' PU peak relative to that of the 398 cm-l PP peak. These differences in relative intensities are exploited to generate chemically specific image contrast, as seen in Figure 6. CONCLUSIONS AND FUTURE DIRECTIONS
A methodology for routine Raman chemical imaging analysis of unknowns has been developed. The methodology consists of (25)C m a . F. 1.. Bayer Colpoation, Pittsburgh, PA P e ~ n a communication, l June 29.1995.
the following: creation of a library of reference spectra, optical microscopy inspeaion of the unknowns: use of Raman microspectroscopy to determine sample heterogeneity; acquisition of AOTF chemical image data; and acquisition of high-fidelity AOTF Raman images. " m l i i t i o n of polymer blend components has been successfully demonstrated. Polymer domains on the micrometer scale can readily be visualized with this technique. Polypropylene and polyxethane domains have been differentiated and are estimated to be in the 3-5 pm range. AOTF Raman chemical imaging microscopy compares favorably with scanning elemon microscopy (SEM) for analysis of these polymer A more thorough study of the blend systems requires that larger areas be examined to provide a more statistically relevant estimation of bulk domain size. Many other questions about PP/PU polymer blends remain that Raman chemical imaging can help to answer. For example, the surface distribution of the blended polymers govems the paintability of these materials. To better understand the surface architecture of blended materials, the Raman microscope will be applied in our ongoing studies to characterize thin (1pm), crosssectioned polymer blends. ACKNOWLEDGMENT
We acknowledge partial financial support of this work from the Society of Analytical Chemists of Pittsburgh (SACP) and the Central Research and Development Fund, University of Pittsburgh. Received for review August 1, 1995. Accepted September 12. 1995." AC950767N e Absbact published in Adoonce ACS Absfmcfr. October 15.
1995.
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