Literature DB >> 33377761

Colloidal Synthesis Path to 2D Crystalline Quantum Dot Superlattices.

Justin C Ondry1,2, John P Philbin1, Michael Lostica1, Eran Rabani1,3,4, A Paul Alivisatos1,2,3,5.   

Abstract

By combining colloidal nanocrystal synthesis, self-assembly, and solution phase epitaxial growth techniques, we developed a general method for preparing single dot thick atomically attached quantum dot (QD) superlattices with high-quality translational and crystallographic orientational order along with state-of-the-art uniformity in the attachment thickness. The procedure begins with colloidal synthesis of hexagonal prism shaped core/shell QDs (e.g., CdSe/CdS), followed by liquid subphase self-assembly and immobilization of superlattices on a substrate. Solution phase epitaxial growth of additional semiconductor material fills in the voids between the particles, resulting in a QD-in-matrix structure. The photoluminescence emission spectra of the QD-in-matrix structure retains characteristic 0D electronic confinement. Importantly, annealing of the resulting structures removes inhomogeneities in the QD-QD inorganic bridges, which our atomistic electronic structure calculations demonstrate would otherwise lead to Anderson-type localization. The piecewise nature of this procedure allows one to independently tune the size and material of the QD core, shell, QD-QD distance, and the matrix material. These four choices can be tuned to control many properties (degree of quantum confinement, quantum coupling, band alignments, etc.) depending on the specific applications. Finally, cation exchange reactions can be performed on the final QD-in-matrix, as demonstrated herein with a CdSe/CdS to HgSe/HgS conversion.

Entities:  

Keywords:  CdSe; nanocrystal superlattices; oriented attachment; quantum dots; self-assembly

Year:  2020        PMID: 33377761     DOI: 10.1021/acsnano.0c07202

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  2 in total

1.  AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles.

Authors:  Xingzhi Wang; Jie Li; Hyun Dong Ha; Jakob C Dahl; Justin C Ondry; Ivan Moreno-Hernandez; Teresa Head-Gordon; A Paul Alivisatos
Journal:  JACS Au       Date:  2021-02-25

2.  Activating Molybdenum Carbide Nanoparticle Catalysts under Mild Conditions Using Thermally Labile Ligands.

Authors:  Lanja R Karadaghi; Anh T To; Susan E Habas; Frederick G Baddour; Daniel A Ruddy; Richard L Brutchey
Journal:  Chem Mater       Date:  2022-09-22       Impact factor: 10.508

  2 in total

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