Doctoral position in theoretical modeling of nanocrystal growth

100%, Zurich, fixed-term

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The Optical Materials Engineering Laboratory (Prof. David J. Norris) in the Department of Mechanical and Process Engineering (D-MAVT) at ETH Zurich investigates the synthesis, growth, structure, and optical properties of semiconductor nanomaterials. Our interdisciplinary and international team combines materials chemistry, optical spectroscopy, electron microscopy, theoretical modeling, and numerical simulation to understand and control materials at the nanoscale.

Nanometer-scale semiconductor crystallites exhibit optical properties (e.g., absorption and emission spectra) that are strongly dependent on their size. Because this property is useful for creating tunable optical materials for optoelectronic applications ranging from displays and infrared cameras to nanophotonics and quantum technologies, chemical syntheses have been developed that produce nanocrystals from various semiconductors. The most advanced protocols are those that lead to quasi-spherical particles (known as colloidal quantum dots).

However, even state-of-the-art nanocrystal samples contain distributions in particle size and morphology that limit their optical performance.

Two classes of semiconductor nanocrystals have been discovered as exceptions to this rule. They display an unusual form of "discrete" growth, jumping between a series of specific sizes. Semiconductor nanoplatelets can be synthesized with atomically uniform thicknesses, while so-called "magic-sized" nanocrystals grow through a sequence of well-defined sizes. These observations suggest that nanocrystals with exceptionally precise dimensions may be possible. Nevertheless, the mechanisms that govern whether nanoplatelets, magic-sized nanocrystals, or conventional colloidal quantum dots form from a specific synthesis remain poorly understood.

The central aim of this project is to develop a universal theoretical framework that explains how these different nanocrystal growth modes emerge. The resulting understanding will be used to guide experiments toward improved control over nanocrystal size, shape, and optical properties.

The doctoral student will develop theoretical and computational descriptions of the nucleation and growth of semiconductor nanocrystals. The initial focus will be on combining existing models for CdSe nanoplatelets and magic-sized nanocrystals. The work will subsequently be expanded to include conventional, continuously growing quantum dots and other semiconductor materials, including InP.

The project will combine three complementary modeling approaches:
First, the student will use density functional theory (DFT) to calculate the energies of surfactant-terminated nanocrystal surfaces, edges, steps, and vertices. These calculations will provide physically meaningful parameters for the growth models. They will also be used to identify surfactant molecules that may stabilize particular nanocrystal shapes. Second, the student will construct mass-balance models describing the coupled growth and dissolution of nanocrystal populations.

These models will examine the competitive growth of nanoplatelets and magic-sized nanocrystals by solving systems of coupled rate equations. The results will be compared directly with experimental stability measurements. The models will then be extended to include quantum dots, with the goal of explaining the transition between discrete and continuous nanocrystal growth. Third, the student will use kinetic Monte Carlo simulations to investigate the early stages of nanocrystal growth.

Such calculations will examine how initially small crystallites develop into competing morphologies and how growth conditions influence the selection of nanoplatelets, magic-sized nanocrystals, or quantum dots.

The doctoral student will work closely with experimentalists responsible for nanocrystal synthesis and growth studies. This interaction between theory and experiment is central to the project: experimental results will provide input for the models, while simulations will guide the design of new experiments. The calculations will be performed using ETH Zurich’s…