Urea-Formaldehyde Resin Confined Silicon Nanodots Composites: High-Performance and Ultralong Persistent Luminescence for Dynamic AI Information Encryption.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jan 20, 2026

Abstract

Persistent luminescence materials typically encounter an intrinsic trade-off between high phosphorescence quantum yield (PhQY) and ultralong phosphorescence lifetime. To overcome this limitation, we propose a strategy that immobilizes silicon nanodots (SiNDs) within a dual-functional composite matrix. The SiNDs efficiently generate abundant triplet excitons through intersystem crossing processes and simultaneously exhibit high PhQYs. Importantly, the urea-paraformaldehyde-derived matrix provides both the spatial confinement of molten urea and the extensive hydrogen-bonding network of the urea-formaldehyde resin. This synergistic configuration effectively immobilizes triplet excitons and suppresses nonradiative decay pathways. As a result, the material exhibits a remarkable PhQY of 81.04% together with an ultralong afterglow lifetime of 3.44 s. Furthermore, the energy transfer strategy further extends the persistent afterglow into the deep-red region (702 nm). Leveraging the tunable afterglow colors and time-resolved luminescent characteristics, an artificial intelligence-assisted information encryption system was successfully developed. This work demonstrates that integrating SiNDs with a dual-characteristic matrix provides a promising approach to concurrently achieving high PhQYs and ultralong lifetimes, thereby broadening the application scope of ultralong-afterglow materials and guiding the rational design of next-generation persistent luminescence materials.

Authors

  • Yulu Liu
    BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China. [email protected].
  • Lei Cao
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. University of Chinese Academy of Sciences, Beijing, People's Republic of China.
  • Lele Gao
    NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
  • Panyong Wang
    Department of Biomaterials and Stem Cells, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science (CAS), Suzhou, P. R. China.
  • Qiannan You
    Department of Biomaterials and Stem Cells, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science (CAS), Suzhou, P. R. China.
  • Xinpei Pang
    School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Mingzheng Jia
    Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, P. R. China.
  • Wen-Fei Dong
    CAS Key Laboratory of Bio Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Minghui Zan
    Department of Biomaterials and Stem Cells, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science (CAS), Suzhou, P. R. China.

Keywords

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