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Soot particles emitted from modern diesel engines, despite significantly lower total mass, show higher reactivity and toxicity than black-smoking old engines, which cause serious health and environmental issues. Soot nanostructure, i.e. the internal structure of soot particles composed of nanoscale carbon fringes, can provide useful information to the investigation of the particle reactivity and its oxidation status. This study presents the nanostructure details of soot particles sampled directly from diesel flames in a working diesel engine as well as from exhaust gases to compare the internal structure of soot particles in the high formation stage and after in-cylinder oxidation. Thermophoretic soot sampling was conducted using an in-house-designed probe with a lacy transmission electron microscope (TEM) grid stored at the tip.

Some soot particles emitted from common-rail diesel engines are so small that can penetrate deep into the human pulmonary system, causing serious health issues. The analysis of nano-scale internal structure of these soot particles sampled from the engine tailpipe has provided useful information about their reactivity and toxicity. However, the variations of carbon fringe structures during complex soot formation/oxidation processes occurring inside the engine cylinder are not fully understood. To fill this gap, this paper presents experimental methods for direct sampling and nanostructure analysis of in-flame soot particles in a working diesel engine. The soot particles are collected onto a lacey carbon-coated grid and then imaged in a high-resolution transmission electron microscope (HR-TEM). The HR-TEM images are post-processed using a Matlab-based code to obtain key nanostructure parameters such as carbon fringe length, fringe-to-fringe separation distance, and fringe tortuosity.

The major challenge of the post-processing of soot aggregates in transmission electron microscope (TEM) images is the detection of soot primary particles that have no clear boundaries, vary in size within the fractal aggregates, and often overlap with each other. In this study, we propose an automated detection code for primary particles implementing the Canny Edge Detection (CED) and Circular Hough Transform (CHT) on pre-processed TEM images for particle edge enhancement using unsharp filtering as well as image inversion and self-subtraction. The particle detection code is tested for soot TEM images obtained at various ambient and injection conditions, and from five different combustion facilities including three constant-volume combustion chambers and two diesel engines.