  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.
