Characterization of gold and silver nanoparticles using it's color image segmentation and feature extraction using fuzzy C-means clustering and generalized shape theory

We present a systematic study of the effect of size and shape on the spectral response of individual silver and gold nanoparticles. When developing nanoparticles as catalysts, their shape is very important. For a certain volume of material, nanoparticles make the best catalysts when they have a large surface area. It is a challenge to find the shape that has the largest surface area for its volume. The main focus of this paper is the interesting change in properties of the materials due to increase surface area to volume ratio. This type of characterization helps the researchers in size-based spectral tuning, biological labeling, and toxicity studies and suggest general protocols to address these problems.