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. 2012 Nov 2;23(43):435706.
doi: 10.1088/0957-4484/23/43/435706. Epub 2012 Oct 11.

Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites

Yi Gao  1 , Zhuo Li, Ziyin Lin, Liangjia Zhu, Allen Tannenbaum, Sylvain Bouix, C P Wong
Affiliations

Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites

Yi Gao et al. Nanotechnology. .

Abstract

The properties of carbon nanotube (CNT)/polymer composites are strongly dependent on the dispersion and orientation of CNTs in the host matrix. Quantification of the dispersion and orientation of CNTs by means of microstructure observation and image analysis has been demonstrated as a useful way to understand the structure-property relationship of CNT/polymer composites. However, due to the various morphologies and large amount of CNTs in one image, automatic and accurate identification of CNTs has become the bottleneck for dispersion/orientation analysis. To solve this problem, shape identification is performed for each pixel in the filler identification step, so that individual CNTs can be extracted from images automatically. The improved filler identification enables more accurate analysis of CNT dispersion and orientation. The dispersion index and orientation index obtained for both synthetic and real images from model compounds correspond well with the observations. Moreover, these indices help to explain the electrical properties of CNT/silicone composite, which is used as a model compound. This method can also be extended to other polymer composites with high-aspect-ratio fillers.

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Figures

Figure 1
Figure 1
The intensity patterns and the eigen vectors of the Hessian matrix. The lengths of the vectors indicate the magnitudes of the eigen values. (a) Small bright blob in the image gives to two large eigen-values. (b) Large bright blob furnishes to two small eigen values. (c) Tubular shape corresponds to one large and one small eigen values.
Figure 2
Figure 2
Test on synthetic images. First column: four synthetic images. Second column: identified CNTs in red. Third column: CNT dispersion density functions and the dispersion index. Fourth column: the CNT orientation distribution and orientation index. See text for detail.
Figure 3
Figure 3
The extraction of the CNT in the SEM images of model compounds prepared at various mixing intensity. The red threads in the images indicate the CNT identified by the proposed algorithm. The identification algorithm works well for various magnifications from 7K to 20K.
Figure 4
Figure 4
Validation of the dispersion analysis by comparing the dispersion of two CNT/SR composites. (a) The original image of a well mixed sample. (b) Identification of the CNTs in the image of (a). (c) The PDF of the image in (a). The inserted numbers are the calculated dispersion index. (d) The original image of a barely dispersed sample. (e) Identification of CNTs in the image of (d). (f) The PDF of the image in (d). The inserted number represent the dispersion index.
Figure 5
Figure 5
Validation of the orientation analysis by comparing the dispersion of two CNT/SR composites. (a) The original image of a VACNT/SR sample. (b) Identification of the CNTs in the image of (a). (c) The histogram of the orientation distribution of image in (a). The inserted number is the calculated orientation index. (d) The original image of a random CNT/SR sample. (e) Identification of CNTs in the image of (d). (f) The PDF of the image in (d). The inserted numbers are the calculated the dispersion index.
Figure 6
Figure 6
The calculated (a) dispersion indices and (b) orientation indices of CNT/SR composites prepared at different rolling cycles.
Figure 7
Figure 7
The ratio of the resistivity measured at direction 1 over the resistivity measured at direction 2.
See this image and copyright information in PMC

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