Nanoparticle shape evolution identified through multivariate statistics.

Precise morphological control of nanoparticles (NPs) has been impeded by the lack of in situ techniques enabling the observation of instantaneous growth steps. Fundamentally, understanding in NP nucleation and growth kinetics has yet to achieve. In the present research, morphological characterization is demonstrated using a novel image detection statistical approach for gold NPs. This multivariate statistical technique enhances the recognition of NPs by successfully identifying their morphology in addition to their growth stages. Thermodynamic analysis of those stages is presented relating surface energies to the growth kinetics. Preferred growth of NPs was seen to take place on specific crystallographic surfaces in a correlated manner. Furthermore, the growth steps are dominated by the adsorption of surfactants and the local surface energies. The present approach enabled detailed observation of NP growth kinetics and can be applied to other metallic NPs.