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Automated Dispersion and Orientation Analysis for Carbon Nanotube Reinforced Polymer Composites

1Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA, USA.
2School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
3Departments of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
4Departments of Electrical and Computer Engineering and Biomedical Engineering, Boston University, Boston, MA, USA.
5College of Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
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Nanotechnology. 2012 Nov 2;23(43):435706.
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Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Li Z., Lin Z., Zhu L., Tannenbaum A., Bouix S., Wong C.P. Automated Dispersion and Orientation Analysis for Carbon Nanotube Reinforced Polymer Composites. Nanotechnology. 2012 Nov 2;23(43):435706. PMID: 23060008. PMCID: PMC3492886.
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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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