Chemical Engineering Science
Despite great progress in the synthetic chemistry of InP QDs, a predictive model to describe their temporal formation is still missing. In this work, we introduce a population balance model incorporating liquid phase reactions, homogeneous nucleation and reaction-limited growth of InP supported with the highly reproducible and reliable experimental data acquired from an automated robotic synthesis platform. A comparison between experimental kinetic data (different initial concentrations and temperatures) and simulations was made. The proposed model describes the temporal evolution of solid concentration, particle diameter and particle size distribution very well. The quantitative agreement between experiments and simulations was only achieved by global optimization to identify unknown and hardly measurable material parameters and kinetic constants such as surface energy, growth rate constants or activation energies. We see this model rendering the first step towards the development of more refined models that enable rigorous optimization and control of the production process for III-V semiconductors.
For details
Population balance modeling of InP quantum dots: Experimentally enabled global optimization to identify unknown material parameters
Zhuang Wang a, Nabi E. Traoré b,c, Tobias Schikarski b,c, Lisa M.S. Stiegler b,c, Dominik Drobek d, Benjamin Apeleo Zubiri d, Erdmann Spiecker d, Johannes Walter b,c, Wolfgang Peukert b,c, Lukas Pflug e, Doris Segets a,f
a. Institute for Energy and Materials Processes - Particle Science and Technology (EMPI-PST), University of Duisburg-Essen, Duisburg, Germany
b. Institute of Particle Technology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
c. Interdisciplinary Center for Functional Particle Systems (FPS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
d. Institute of Micro- and Nanostructure Research (IMN) & Center for Nanoanalysis and Electron Microscopy (CENEM), Interdisciplinary Center for Nanostructured Films (IZNF), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
e. Competence Unit for Scientific Computing (CSC), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
f. Center for Nanointegration Duisburg-Essen (CENIDE), Duisburg, Germany
DOI: https://doi.org/10.1016/j.ces.2023.119062
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