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Title:Fractal analysis - a surrogate technique for material characterization
Authors:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author)
ID S, Sankararaman, UNIVERSITY OF KERALA (Author)
Files: This document has no files that are freely available to the public. This document may have a physical copy in the library of the organization, check the status via COBISS. Link is opened in a new window
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:Fractal analysis has emerged as a potential analytical tool in almost all branches of science and technology. The paper is the first report of using fractal dimension as a surrogate technique for estimating particle size. A regression equation is set connecting the soot particle size and fractal dimension, after investigating the Field Emission Scanning Electron Microscopic (FESEM) images of carbonaceous soot from five different sources. Since the fractal dimension is an invariant property under the scale transformation, an ordinary photograph of the soot should also yield the same fractal dimension. This enables one to determine the average size of the soot particles, using the regression equation, by calculating the fractal dimension from the photograph. Hence, instead of frequent measurement of average particle size from FESEM, this technique of estimating the particle size from the fractal dimension of the soot photograph, is found to be a potentially cost-effective and non-contact method.
Keywords:fractals, FESEM, carbon nanoparticles, particle size, box-counting
Publication version:Version of Record
Year of publishing:2017
Number of pages:7
Numbering:6, 8
PID:20.500.12556/RUNG-7428 New window
COBISS.SI-ID:113422083 New window
DOI:10.17586/2220-8054-2017-8-6-809-815 New window
NUK URN:URN:SI:UNG:REP:B1JYHUCH
Publication date in RUNG:30.06.2022
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Record is a part of a journal

Title:Nanosystems: Physics, Chemistry, Mathematics
Year of publishing:2017
ISSN:2305-7971

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:29.06.2022

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