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Energy Migration Control of Multimodal Emissions in an Er3+ -Doped Nanostructure for Information Encryption and Deep-Learning Decoding.
Song, Yapai; Lu, Mengyang; Mandl, Gabrielle A; Xie, Yao; Sun, Guotao; Chen, Jiabo; Liu, Xin; Capobianco, John A; Sun, Lining.
Afiliação
  • Song Y; School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
  • Lu M; Research Center of Nano Science and Technology, College of Science, Shanghai University, Shanghai, 200444, China.
  • Mandl GA; School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
  • Xie Y; Department of Chemistry and Biochemistry and Centre for NanoScience Research, Concordia University, Montreal, QC, H4B 1R6, Canada.
  • Sun G; Research Center of Nano Science and Technology, College of Science, Shanghai University, Shanghai, 200444, China.
  • Chen J; School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
  • Liu X; Research Center of Nano Science and Technology, College of Science, Shanghai University, Shanghai, 200444, China.
  • Capobianco JA; Research Center of Nano Science and Technology, College of Science, Shanghai University, Shanghai, 200444, China.
  • Sun L; Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
Angew Chem Int Ed Engl ; 60(44): 23790-23796, 2021 10 25.
Article em En | MEDLINE | ID: mdl-34476872
Modulating the emission wavelengths of materials has always been a primary focus of fluorescence technology. Nanocrystals (NCs) doped with lanthanide ions with rich energy levels can produce a variety of emissions at different excitation wavelengths. However, the control of multimodal emissions of these ions has remained a challenge. Herein, we present a new composition of Er3+ -based lanthanide NCs with color-switchable output under irradiation with 980, 808, or 1535 nm light for information security. The variation of excitation wavelengths changes the intensity ratio of visible (Vis)/near-infrared (NIR-II) emissions. Taking advantage of the Vis/NIR-II multimodal emissions of NCs and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Angew Chem Int Ed Engl Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Angew Chem Int Ed Engl Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China