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We have used information theory analogue of entropy, Shannon entropy, forestimating the variations during the isotropic and anisotropic AuNP fractalgrowth process. We have firstly applied the Shannon entropy on the simulatedfractal aggregates obtained from DLA model with noise reduction scheme. Inconventional noise reduction scheme used in past, the growth process ofidentical particles was performed and no effect of the evolving cluster on theincoming particle was considered, hence the noise is reduced in discrete amountand do not account for the noise fluctuations present during the morphologicalevolution of the fractals. The Shannon entropy is shown to capture theemergence of the anisotropic morphological evolution. The imaging tool wasfurther found to be promising for capturing the readjustment effects duringcluster-cluster aggregation.

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