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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.

In-flame soot sampling based on the thermophoresis of particles and subsequent transmission electron microscope (TEM) imaging has been conducted in a diesel engine to study size, shape and structure of soot particles within the reacting diesel jet. A direct TEM sampling is pursued, as opposed to exhaust sampling, to gain fundamental insight about the structure of soot during key formation and oxidation stages. The size and shape of soot particles aggregate structure with stretched chains of spherical-like primary particles is currently an unknown for engine soot modelling approaches. However, the in-flame sampling of soot particles in the engine poses significant challenges in order to extract meaningful data. In this paper, the engine modification to address the challenges of high-pressure sealing and avoiding interference with moving valves and piston are discussed in detail.