Volume 133, Issue 12 pp. 6741-6746
Forschungsartikel

Growth of Colloidal Nanocrystals by Liquid-Like Coalescence**

Dr. Bin Yuan

Dr. Bin Yuan

Department of Materials Science & Engineering, Iowa State University of Science and Technology, 2220 Hoover Hall, Ames, IA, 50011 USA

Department of Chemical & Biological Engineering, Iowa State University of Science and Technology, Sweeney Hall, Ames, IA, 50011 USA

Current address: Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213 USA

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Prof. Ludovico Cademartiri

Corresponding Author

Prof. Ludovico Cademartiri

Department of Materials Science & Engineering, Iowa State University of Science and Technology, 2220 Hoover Hall, Ames, IA, 50011 USA

Department of Chemical & Biological Engineering, Iowa State University of Science and Technology, Sweeney Hall, Ames, IA, 50011 USA

Ames Laboratory, U.S. Department of Energy, Ames, IA, 50011 USA

Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze, 17/A, 43124 Parma, Italy

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First published: 16 December 2020
**

A previous version of this manuscript has been deposited on a preprint server (https://arxiv.org/abs/2005.11775).

Abstract

We here describe, model, and predict the growth kinetics of amine-capped PbS colloidal nanoparticles in the absence of supersaturation. The particles grow by coalescence rather than by Ostwald ripening. A comparison of different models indicates that the effective activation energy of coalescence (67.65 kJ mol−1) is associated with two terms: a term proportional to the contact area between the ligand shells of two colliding particles, and a constant term. Our Brownian dynamics simulations show (i) how the remarkably low activation energy (or large rate constants) are most likely due to the large difference in size between the particles and their mean free path of diffusion, and (ii) how the low polydispersity is the likely result of the suppression of collision rates between rare populations due to crowding. The model successfully predicts the growth kinetics of nanoparticles, therefore enabling the precise control of the average particle size without the need of supersaturation.

Conflict of interest

The authors declare no conflict of interest.

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