Mechanistic insights into the anisotropic growth of ZnO nanoparticles deciphered through 2D size plots and multivariate analysis - Archive ouverte HAL
Article Dans Une Revue Nanoscale Advances Année : 2021

Mechanistic insights into the anisotropic growth of ZnO nanoparticles deciphered through 2D size plots and multivariate analysis

Résumé

The control and understanding of the nucleation and growth of nano-objects are key points for improving and/or considering the new applications of a given material at the nanoscale. Mastering the morphology is essential as the final properties are drastically affected by the size, shape, and surface structure. Yet, a number of challenges remain, including evidencing and understanding the relationship between the experimental parameters of the synthesis and the shape of the nanoparticles. Here we analyzed jointly and in detail the formation of anisotropic ZnO nanoparticles under different experimental conditions by using two different analytical tools enabling the analysis of TEM images: 2D size plots and multivariate statistical analysis. Well-defined crystalline ZnO nanorods were obtained through the hydrolysis of a dicyclohexyl zinc precursor in the presence of a primary fatty amine. Such statistical tools allow one to fully understand the effect of experimental parameters such as the hydrolysis rate, the mixing time before hydrolysis, the length of the ligand aliphatic chain, and the amount of water. All these analyses suggest a growth process by oriented attachment. Taking advantage of this mechanism, the size and aspect ratio of the ZnO nanorods can be easily tuned. These findings shed light on the relative importance of experimental parameters that govern the growth of nano-objects. This general methodological approach can be easily extended to any type of nanoparticle.

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Chimie
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hal-03400345 , version 1 (22-11-2021)

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Zhihua Zhao, Yinping Wang, Céline Delmas, Christophe Mingotaud, Jean-Daniel Marty, et al.. Mechanistic insights into the anisotropic growth of ZnO nanoparticles deciphered through 2D size plots and multivariate analysis. Nanoscale Advances, 2021, ⟨10.1039/d1na00591j⟩. ⟨hal-03400345⟩
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