Population balance modeling of InP quantum dots: Experimentally enabled global optimization to identify unknown material parameters

• Published in: Chemical engineering science 2023-11, Vol.281, p.119062, Article 119062
• Main Authors: Wang, Zhuang, Traoré, Nabi E., Schikarski, Tobias, Stiegler, Lisa M.S., Drobek, Dominik, Zubiri, Benjamin Apeleo, Spiecker, Erdmann, Walter, Johannes, Peukert, Wolfgang, Pflug, Lukas, Segets, Doris
• Format: Article
• Language: English
• Subjects: Automated synthesis; InP; Population balance equation; Simulation
• ISSN: 0009-2509
• DOI: 10.1016/j.ces.2023.119062

•An aminophosphine-based automated synthesis of InP QDs with high reproducibility was demonstrated.•PSDs derived from the UV–vis spectra can be interpreted as volume equivalent diameters in case of InP QDs with tetrahedral shape.•A predictive model based on population balance equation was proposed.•With the identified parameter set, a very good agreement between experimental results and simulations was obtained, including activation energies. 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.