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PubMed Publications| Keyword search (4,163 papers available) |  |
"Song Y" Authored Publications:
| Title: | Energy migration control of multi-modal emissions in an Er3+ doped nanostructure toward information encryption and deep learning decoding | ||||
| Authors: | Song Y, Lu M, Mandl GA, Xie Y, Sun G, Chen J, Liu X, Capobianco JA, Sun L | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/34476872/ | ||||
| DOI: | 10.1002/anie.202109532 | ||||
| Publication: | Angewandte Chemie (International ed. in English) | ||||
| Keywords: | deep learning; information encoding; lanthanide-doped nanocrystals; luminescence; | ||||
| PMID: | 34476872 | Category: | Date Added: | 2021-09-03 | |
| Dept Affiliation: |
ENCS
1 Shanghai University, School of Materials Science and Engineering, CHINA. 2 Shanghai University, School of Communication and Information Engineering, CHINA. 3 Concordia University, Department of Chemistry and Biochemistry and Centre for NanoScience Research, Montreal, CANADA. 4 Shanghai University, College of Sciences, CHINA. 5 Shanghai University, School of Materials Science and Engineering, Shanghai, CHINA. 6 Fudan University, Academy for Engineering and Technology, CHINA. 7 Concordia University, Department of Chemistry and Biochemistry and Centre for NanoScience Research, CANADA. 8 Shanghai University, Research Center of Nano Science and Technology, No. 99 Shangda Road, 200444, Shanghai, CHINA. |
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Description: |
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 multi-modal emissions of these ions has remained a challenge. Herein, we present a new composition of Er 3+ -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 multi-modal emissions of NCs and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue. |
