Predicting metallic nanoparticle morphologies from DFT calculations of surface-ligand interactions

Abstract : Nanoparticles are one of the most important families of functional materials due to their nanometric size. This size reduction, associated to their composition, surfaces orientation and morphology has contributed to the emergence of new important properties such as electronic, magnetic, catalytic, optic, etc. To control the morphology of NPs, many efforts have been devoted to understand their formation mechanism and the origin of their stability. Among metallic nanoparticles, cobalt, with its hexagonal closed-packed (hcp) structure, is particularly interesting because of the possibility to grow "naturally" anisotropic shaped nanocrystals. Using chemical synthesis in liquid environment, various morphologies such as disks, plates, rods, wires and cubes have been obtained by controlling the precursor type, the reducing agent, the stabilizing ligands as well as their concentration, the temperature or the rate of precursor injection. Even if these synthesis conditions have been rationalized, few is known concerning the growth mechanisms at the atomic scale. In this work, we have developed two quantitative morphology prediction models, one based on the final thermodynamic equilibrium state, while another is controlled by the kinetics. These models require the knowledge of the adsorption behaviors of stabilizing molecules as a function of surface coverage on preferential facets of NPs. To this end, density functional theory (DFT) calculations were performed on a series of stabilizing molecules (CH3NH2 , CH3COO C5H11OO and C11H23COO) adsorbed on the different Co and Ni surfaces. The shape of the Co NPs obtained by these two models was compared to experimental morphologies and other theoretical results from the literature. The variety of forms predicted by the kinetic model agrees better with the NPs morphologies obtained under the different synthesis conditions. This confirms that the morphology control of NPs is mostly driven by the kinetics.
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van Bac Nguyen. Predicting metallic nanoparticle morphologies from DFT calculations of surface-ligand interactions. General Physics [physics.gen-ph]. Université Paul Sabatier - Toulouse III, 2016. English. ⟨NNT : 2016TOU30299⟩. ⟨tel-01688076⟩

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