Quantitative differentiation of poorly ordered soot nanostructures: A semi-empirical approach

• Published in: Fuel (Guildford) 2012-09, Vol.99, p.1-8
• Main Authors: Toth, P., Palotás, Á.B., Lighty, J., Echavarria, C.A.
• Format: Article
• Language: English
• Subjects: Applied sciences; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Fuels; HRTEM; Image processing; Nanostructure; Soot
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2012.04.013

► A new method is proposed for obtaining sensitive quantitative structural parameters from soot HRTEM images. ► The method is tested on artificial fringe images. ► The method is tested on real HRTEM images of soot samples originating from different fuels. ► The proposed parameters are more robust and sensitive than standard parameters. A novel, sensitive technique is presented yielding expressive semi-empirical order parameters (distance deviation parameter Ω and junction parameter υ) describing the nanostructure of combustion derived carbonaceous materials. This method is purposively developed to enable the measurement of slight changes in structural properties of young soots originating from the combustion of different fuels – most of which are left undetected by conventional algorithms – by utilizing digital image processing of high resolution transmission electron micrographs (HRTEM images). The study has been motivated by a demand for better understanding the evolution of soot nanostructure. This paper presents the details of the proposed method, along with the comparison with the conventional methodology. Through simulations, the correlation between previously defined order parameters and our new structural indices is demonstrated. As an example of practical utilization, the nanostructural evolution of different fuels with residence time and the extent of oxidation is shown by monitoring changes in Ω and υ. The structural development was analyzed using a specific sampling procedure: soots were collected at various heights above the burner surface.