An image analysis algorithm is applied to materials - for characterization of solid-state structure on a nanometer scale using model carbon materials. Nanoscale carbons in the form of "soots" offer ease of demonstration while being relevant to human health and climate concerns. Demonstrated here is an image analysis algorithm applied to three nanoscaled carbon materials: a disordered soot, and two highly ordered soots featuring flat or curved atomic layer planes. Nanostructure parameters consisting of graphene layers' length and tortuosity are extracted from high-resolution transmission electron microscopy images. The algorithm is composed of two major parts: (a) image processing that generates a skeletonized binary image, and (b) characterization that generates statistics on length and tortuosity based on the skeletonized image of the graphene layers. Algorithm robustness for variations in image processing parameters of contrast and threshold is demonstrated by similarity of output distributions of disordered diesel engine produced soot. Algorithm processing range was illustrated using highly ordered soots with flat (graphitic) and curved lattice nanostructure. Time resolved image analysis of an image sequence for polyhedral onions under electron irradiation demonstrate algorithm utility for tracking solid-state transformations.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence