Imaging Open-Cell Polyurethane Foam via Confocal Microscopy

Chapter 11

Imaging Open-Cell Polyurethane Foam via Confocal Microscopy 1

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Rida Hamza , Xiaodong D. Zhang , Christopher W . Macosko , Robert Stevens , and M a r k Listemann 3

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Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418 Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue Southeast, Minneapolis, MN 55455 Air Products and Chemicals, Inc., 7201 Hamilton Boulevard, Allentown, PA 18195

Downloaded by UNIV OF MONTANA on April 7, 2015 | http://pubs.acs.org Publication Date: June 1, 1997 | doi: 10.1021/bk-1997-0669.ch011

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Flexible polyurethane foam is based on a 3-dimensional cellular network. The mechanical properties of foam material depend upon cell structure and cell size distribution. In this work, we use laser confocal microscopy to image the foam cells and recover its 3-dimensional cellular network. Based on this technique we provide a statistical analysis and compare several foam samples. Confocal microscopic images are also used to visualize foam compression. Images for foam network structure under different mechanical compressions are also obtained. Limitations of confocal microscope are discussed and a new method - nuclear magnetic resonance imaging is proposed. Polyurethane foam (PUF) is a widely used cushioning materials with a complex threedimensional structure (Figure 1). It is well understood that the mechanical properties of the foamed material depends on cell structure and cell size distribution (7,2). Recent attempts to characterize cell structure involved two dimensional image analysis (3). The conventional methods for foam imaging includes two-dimensional images obtained through either optical or scanning electron microscopy (SEM)(7). However, foam cells are multidimensional and resemble an interconnected polyhedron network. Hence accurate characterization of cell structure from such images faces many difficulties. In addition, conventional optical microscopy techniques require a thin slice of material. Using S E M , only the surface of the sample can be observed. In both scenarios, the imaged cells can be damaged during sample preparation. A technique is therefore desired to image the foam in three-dimensions (3D), and observe undamaged material inside of a thicker sample. Three dimensional structural information can then be extracted for foam samples with different cell size distribution and different mechanical properties. To perform 3D image processing, practical solutions to display 3D images are required. There exists several image rendering techniques and commercial image processing software. However, none of these software appear practical for our application since they all require a large amount of memory to process complex 3D images and the memory requirement overcomes most of the systems limits. Automated algorithms for 3D reconstruction are under development with encouraging results especially in biomedical applications (5-7). However, none of these previous attempts was able to reduce the three dimensional image of a complex feature, such as foam, to a concise set of rules and procedures. 4

Corresponding author

© 1997 American Chemical Society

In Polymeric Foams; Khemani, K.; ACS Symposium Series; American Chemical Society: Washington, DC, 1997.

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Figure 1. A typical PUF cellular structure: overlay of 23 confocal microscope 2D images (image size 37mm χ 25mm).

Figure 2. System diagram of Laser Confocal Microscope (LCM).


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This is the goal of this work. A new technique, laser confocal microscopy is used for image acquisition to provide sharp 2D images used for 3D reconstruction. 2D Foam Image Acquisition V i a Confocal Microscopy


Laser confocal microscopy (LCM) is used to collect two-dimensional images at different depths in foam samples. This technique is proved to be advantageous over the conventional techniques. Before we compare these techniques, we describe the size, type and preparation of our foam samples. The foaming process is an important issue in sample preparation which is, however, beyond the scope of this paper. PUF Samples. Since the microscopy imagery is based on a laser light that excites fluorescein, a dye coding was necessary to image the foam cell network. When excited with a blue laser (496nm), Fluorescein emits at 518 nm. Foam samples (1 c m 3 ) were stained in a solution of 10 mg of fluoresceinamine in 5 ml of tetrahydrofuran. The portion of the sample observed in this experiment is about 3.7x2.5x2mm. For foam ΠΙ (see Table I), this represents an average of 14 cells. L C M Technique The Bio-Rad L C M , MRC600, was used to conduct all this work. This work is conducted using a different image acquisition approach. We collect PUF images using L C M . The L C M imagery is based on a laser light which passes through a barrier filter that selects the peaks to be used in exciting the fluorophone (see Figure 2). A dichroic mirror reflects the laser wavelength to the foam sample and allows returning wavelengths that are greater than a threshold value. For the PUF case, we use 510 nm wavelength as a threshold. The software package with this instrument has limited functionality except for noise smoothness, stacking images and making stereo pairs. Most of the images taken by the Bio-Rad L C M were of high quality and only few image processing operations were needed. Each image section had a resolution of 512 χ 768 pixels, with 4.875μΓη/ρΐχβ1. Figure 3 presents a sequence of frames captured at different scanning depths. These captured images are arranged accordingly to present the variations in the cellular network projection planes that will be used to detect information about the network. We varied the spacing between slices to focus on planes where more structural information resides. Due to out-of-focus planes shielding, (i.e. see Figure 3f), the maximum scanning depth was typically limited to 2mm. In some applications, it is required to store these images for later analysis. In most cases, this is not reliable due to the tremendous space needed to store all these captured images. Instead of storing vast amount of possibly redundant images, shared image database can be provided to different analyzers. Statistical Analysis The advantage of this imaging process concerns the possibility to analyze the PUF cellular network and relate its learned distributions to mechanical response of the sample. Using the software capabilities in the Bio-Rad L C M , Khoros modules (these modules are created from image process, signal processing and morphology tool boxes of Khoros), and some manual editing we were able to conduct a statistical analysis on three foam samples with different cellular sizes. Among the various statistical parameters possible from this imaging technique, the following statistical structural parameters have been selected: the mean μι and the standard deviation of the length of the struts σι, the number of vertices n , the number v

of windows n , and finally the means \i{ and standard deviations σι (i =a,b or c) of diameters a, b, and c, that define the elliptic dimension of a typical foam cell. A l l these statistics are based on a volume of 16.82mnA In Table I, three foam samples, having w



POLYMERIC FOAMS

Figure 3. Confocal microscope images at increasing depth into the foam (image size 37mm χ 25mm). (a) 114 (b) 320 μτη; (c) 812 μτη; μ Γ η ;

(d) 1400 μτη; (e) 1720μτη; (f) 1860 μιη. A Kalman filter was applied on these images to smooth and smear out some of the background noise.


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different cell sizes, were considered. Within the specified volume there were not enough cells to measure an accurate diameter standard deviation for foam I and Π. It is worth noting that other statistics can be easily deduced from these measurements. Moreover, other statistical parameters, such as distributions of vertices, window surface and shape and cell volume were considered but found to be highly correlated with the provided list using Euler's law on the dependence of faces, vertices and cells. This list of parameters is sufficient to characterize the foam cellular distribution. Table I. Statistical distribution of three foam samples with different cellular sizes: 1 - length of cell struts; n „ number of windows i n a cell; n - number of vertices i n a cell; a, b, & c - diameters that define the elliptic dimension of a cell; μ, & σ - mean and standard deviation w


v

Parameters W (μπι)

Imaging Open-Cell Polyurethane Foam via Confocal Microscopy

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