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Bright InP Quantum Dots by Mid-Synthetic Modification with Zinc Halides.

Hui-Ling HuHao HaoXue RenZhe-Yong ChenMeng LiuYi LiuFeng-Lei Jiang
Published in: Inorganic chemistry (2023)
InP quantum dots (QDs) attract growing interest in recent years, owing to their environmental advantages upon applications in display and lighting. However, compared to Cd-based QDs and Pb-based perovskites, the synthesis of InP QDs with high optical quality is relatively more difficult. Here, we established a mid-synthetic modification approach to improve the optical properties of InP-based QDs. Tris(dimethylamino)phosphine ((DMA) 3 P) and indium iodide were used to prepare InP QDs with a green emission (∼527 nm). By introducing zinc halides (ZnX 2 ) during the mid-synthetic process, the photoluminescence quantum yield (PLQY) of the resulting InP/ZnSeS/ZnS core/shell/shell QDs was increased to >70%, and the full-width-at-half-maximum (FWHM) could be narrowed to ∼40 nm. Transmission electron microscopy clearly showed the improvement of the QDs particle size distribution after introducing ZnX 2 . It was speculated that ZnX 2 was bound to the surface of QDs as a Z-type ligand, which not only passivated surface defects and suppressed the emission of defect states but also prevented Ostwald ripening. The InP cores were also activated by ZnX 2 , which made the growth of the ZnSeS shell more favorable. The photoluminescence properties started to be improved significantly only when the amount of ZnX 2 exceeded 0.5 mmol. As the amount increased, more ZnX 2 was distributed around the QDs to form a ligand layer, which prevented the shell precursor from crossing the ligand layer to the surface of the InP core, thus reducing the size of the InP/ZnSeS/ZnS QDs. This work revealed a new role of ZnX 2 and found a method for InP QDs with high brightness and low FWHM by the mid-synthetic modification, which would inspire the synthesis of even better InP QDs.
Keyphrases
  • quantum dots
  • heavy metals
  • energy transfer
  • photodynamic therapy
  • neural network