The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 13, 2020

Filed:

Nov. 30, 2016
Applicant:

Huazhong University of Science and Technology, Hubei, CN;

Inventors:

Ming Xu, Hubei, CN;

Mengmeng Zhang, Hubei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 21/41 (2006.01); H01J 37/22 (2006.01); G01N 1/38 (2006.01); G02B 21/36 (2006.01); G01B 11/24 (2006.01); G01N 15/02 (2006.01); G01N 21/17 (2006.01); G01N 15/00 (2006.01);
U.S. Cl.
CPC ...
G01N 21/41 (2013.01); G01B 11/24 (2013.01); G01N 1/38 (2013.01); G01N 15/0227 (2013.01); G02B 21/365 (2013.01); H01J 37/222 (2013.01); G01N 2015/0038 (2013.01); G01N 2015/0042 (2013.01); G01N 2021/1765 (2013.01); G01N 2021/4113 (2013.01); G06T 2207/10061 (2013.01);
Abstract

A numerical characterization method for the dispersion state of carbon nanotubes based on fractal dimension is invented. In this method, a SEM image of the dispersion state of carbon nanotube is obtained first, and then is binarized by the ImageJ software to extract the boundary of individual carbon nanotubes or carbon nanotubes agglomerates, and thereby calculating the fractal dimension of the processed image with the assistance of the box-count algorithm. The value of fractal dimension represents quantitatively the abundant information contained in the dispersion state of carbon nanotubes, which is capable of realizing the numerical characterization of the dispersion state of carbon nanotubes. The invention quantifies the dispersion state of carbon nanotubes, and provides a powerful strategy for controlling, comparison and prediction of macro-properties of carbon nanotube based composites.


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