Shape-tailored Semiconductor Dot-in-rods: Optimizing CdS-shell Growth for Enhanced Chiroptical Properties Via the Rationalization of the Role of Temperature and Time

Junjie Hao, Peizhao Liu, Ziming Zhou, Haochen Liu,Wei Chen, Peter Mueller-Buschbaum, Jiaji Cheng,Kai Wang,Xiao Wei Sun,Jean-Pierre Delville,Marie-Helene Delville

NANOSCALE ADVANCES（2025）

Univ Bordeaux

Cited 0|Views8

Abstract

Colloidal chemistry provides an assortment of synthetic tools for tuning the shape of semiconductor nanocrystals. To fully exploit the shape- and structure-dependent properties of semiconductor nanorods, high-precision control on growth and design is essential. However, achieving this precision is highly challenging due to the high temperatures (>350 degrees C) and short reaction times (<8 minutes) often required for these reactions. In this study, we performed the first investigation on the impact of temperature and time on the CdS-shell growth of CdSe/CdS quantum rods. Our findings demonstrate that temperature plays a pivotal role in achieving ultra-thin shell dot-in-rods, which are crucial for enhancing chiroptical properties. The two-step process proposed here explains the shell growth of CdSe/CdS dot-in-rods (DRs). It involves finely-tuned isotropic shell growth in the first stage, followed by anisotropic length growth along the [0001] rod axis in the second step. This approach has two advantages: a systematic control of the shell thickness for different aspect ratios (ARs) and batch monodispersity. These DRs, with an ultra-thin CdS shell and a high AR, after modification with l/d cysteine molecules, exhibit significant enhancement of their ligand-induced chirality, with circular dichroism (CD) g-factor values as high as 10(-3).

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper