Separation of Indium Phosphide/Zinc Sulfide Core-Shell Quantum Dots from Shelling Byproducts through Multistep...

Semiconductor quantum dots (QDs) possess unique electronic and optical properties, making them promising candidates for applications in light-emitting diodes, solar cells, bioimaging, and photocatalysis. Precise control over their size, shape, and chemical and electronic structure is crucial to ensure the desired functional properties and optimize device performance. However, challenges in QD synthesis and post-synthesis modification persist, especially in large-scale production. This study addresses the classification of QDs synthesized in a tubular flow reactor consisting of a mixture of the desired InP/ZnS core-shell QDs and QDs made from the shell material, i.e., here ZnS QDs formed as a byproduct during the formation step of the ZnS shell. The homogeneous nucleation of ZnS nanoparticles from the shelling material introduces a heterogeneity in size and composition and affects the optical properties of the resulting QDs. To address this issue, we developed a size-selective agglomeration (SSA) technique by incrementally introducing ethanol as a poor solvent and classified the synthesized QDs into 13 distinct fractions. These 13 fractions are sorted into three distinct groups: (i) larger InP/ZnS QDs, (ii) a combination of smaller InP/ZnS QDs and larger ZnS QDs, and (iii) predominant ZnS QDs with some very tiny InP/ZnS QDs. The comprehensive characterization of the fractions was conducted using UV-visible absorption spectroscopy, photoluminescence spectroscopy, high-resolution scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy, total reflection X-ray fluorescence, and analytical ultracentrifugation. We could demonstrate that our method effectively separated unwanted ZnS QDs from the target InP/ZnS QDs. In addition, the fractions enriched in smaller InP/ZnS QDs exhibited a higher photoluminescence quantum yield compared to the fractions with larger QDs. This demonstrates the efficacy of SSA in fine-tuning the composition of QD mixtures produced on a larger scale to improve their functional properties. This approach provides fundamental understanding toward the development of a scalable two-dimensional classification process for such ultrasmall nanoparticles by particle size and composition.