Analysing the anisotropy in morphological evolution and readjustment effects in cluster-cluster aggregation of AuNPs using Shannon entropy

We have used information theory analogue of entropy, Shannon entropy, for estimating the variations during the isotropic and anisotropic AuNP fractal growth process. We have firstly applied the Shannon entropy on the simulated fractal aggregates obtained from DLA model with noise reduction scheme. In conventional noise reduction scheme used in past, the growth process of identical particles was performed and no effect of the evolving cluster on the incoming particle was considered, hence the noise is reduced in discrete amount and do not account for the noise fluctuations present during the morphological evolution of the fractals. The Shannon entropy is shown to capture the emergence of the anisotropic morphological evolution. The imaging tool was further found to be promising for capturing the readjustment effects during cluster-cluster aggregation.