SSRN
Mixing is a key process parameter during liquid phase particle synthesis that requires special attention, in particular for scalable process and reactor development. This issue is especially pressing for advanced materials where on the one hand multifunctional properties require products of utmost quality, while on the other hand only few strategies for process design are yet developed. One material class in this context is quantum dot (QD) nanocrystals. By making use of automation and high throughout (HT) experimentation, we could recently demonstrate qualitatively the role of mixing on focusing and defocusing during QD synthesis by hot injection. In this work, we demonstrate the application of HT methodologies to investigate the effect of mixing as an important process parameter during process optimization and scale up. We performed reaction optimization by screening a multi-parameter design space using design of experiments (DoE) and machine learning (ML). We developed a so-called cold model as proxy that is based on equivalent mixing times (EMTs) derived from a competing parallel reaction system. Experimentally, we realized a fully automated approach using a HT platform for particle synthesis. The final dataset consisted of 108 experiments and repeats and was combined with DoE and complimented with machine learning. By doing so, with DoE, we could reduce the number of experiments required to develop a predictive, statistical knowledge base for EMTs at ambient conditions. Finally, we demonstrated the applicability of EMTs for two defined stirrer geometries to fine tune the width of the particle size distribution of QDs at increasing reaction volumes from 20 mL to 80 mL. This opens the door towards scalable processes for QDs at increased reaction volumes while maintaining product quality.
For details:
Controlled Mixing During Colloidal Quantum Dot Synthesis: A Proxy-Concept Based on Equivalent Parameters
Ahmed Salaheldin Mahmoud
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) - Institute of Particle Technology (LFG), Interdisciplinary Center for Functional Particle Systems (FPS)
Doris Segets
University of Duisburg-Essen - CeNIDE (Center for Nanointegration Duisburg-Essen)
DOI: http://dx.doi.org/10.2139/ssrn.4414794
For more information about the used Chemspeed solutions:
FLEX AUTOPLANT
AUTOPLANT PRORES
