WeChat Mini Program
Old Version Features

    • Co-doping of Ho-Yb Ion Pairs Modulating the Up-Conversion Luminescence Properties of Fluoride Phosphors under 1550 Nm Excitation.

    Bohan Lei,Liping Lu,Haiying Sun,Xiyan Zhang,Zhaohui Bai,Xiaoyun Mi

    Dalton Transactions（2023）

    Changchun Univ Sci & Technol

    Cited 1|Views7
    Abstract
    In this study, up-conversion fluoride phosphors NaY1-x-y-m-nMxF4:Er3+y,Ho3+m,Yb3+n (M = Lu3+/Gd3+) were synthesized by a low-temperature combustion method. The optimal ionic ratios in the matrix lattice were also determined by a controlled variable method. It was confirmed that doping a small amount of Ho3+ ions and Yb3+ ions in the Er-doped sample matrix lattice can form a mutual sensitizer and a transient energy capture center to enhance the sample's up-conversion luminescence under excitation at the 1550 nm band, respectively. It was also found that the lanthanide ion introduced can modulate the red-to-green ratio of the up-conversion luminescence of the sample. The phase composition and morphology of phosphors were investigated using X-ray diffraction and scanning electron microscopy. The up-conversion luminescence mechanism of Er-Ho-Yb tri-doped samples excited at the 1550 nm band was also investigated. This work presents a novel approach for improving up-conversion luminescence with high color-purity phosphors for display lighting applications when excited at 1550 nm.
    More
    Translated text
    Key words
    Photon Upconversion,Upconversion Nanoparticles
    求助PDF
    上传PDF
    View via Publisher
    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
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined