Here, a novel multifunctional phosphor of Li1−xGa5O8:xPr3+ is reported. The crystal structure, mechanoluminescence (ML), persistent luminescence (PersL), photoluminescence (PL) and photoluminescence excitation (PLE) are systematically studied. LiGa5O8 emits blue PersL peaked at 437 nm after irradiation by a 254 nm lamp for 10 s, and corresponding PersL can be observed for 30 min by naked eyes. In the same case, the PersL from Li0.995Ga5O8:0.005Pr3+ covers visible to near‐infrared lights (400–1000 nm) which can persist for 120 min. Thermoluminescence analysis shows Pr3+ doping not only increases the shallower trap (0.73 eV) concentration by at least 20 times but also helps the formation of deeper trap centers (0.87 eV). The differences between ML and PersL spectra reveal the selective effects of mechanical stimuli on the transitions of Pr3+ ions. In the end, the fabrication of an intelligent PersL quick response code (QR‐code) permits optical information write‐in after irradiation by a 254 nm lamp and then cryptical information of “SCUT” can be read out by thermal stimulation which shows the potential in information storage. Moreover, the biological tissues penetration experiments and cytotoxicity tests are carried out to display the potential of PersL in bioimaging.
Till now, many doped persistent luminescence (PersL) phosphors have been investigated and found various applications in such as bioimaging, photocatalysis and information storage, but introducing PersL emitters into a proper host is mostly complex. In this research, a self-activated PersL phosphor Ba 2 Zr 2 Si 3 O 12 (BZSO) is prepared by solid state reaction. By adding NH 4 Cl, the self-activated PersL intensity is evidently enhanced. The trap depths and concentrations are examined by thermoluminescence spectra. Meanwhile, Bi 3+ ions are introduced into BZSO and show wide band photoluminescence (PL) from 300 to 600 nm. Moreover, the PL of Bi 3+ is tunable under excitation by 265-350 nm lights. Furthermore, as a proof-of-concept design, we designed a patterned quick response (QR) code based on the self-activated PersL of BZSO, and the information of "South China University of Technology (SCUT)" can be read out by the code scanning technology. Bi 3+-doped BZSO phosphors are suggested to provide potential applications in information storage by its self-activated PersL, and to excite researchers to study the tunable PL in Bi 3+-doped phosphor.
With the popularity of new social media, automatic image annotation (AIA) has been an active research topic due to its great importance in image retrieval, understanding, and management. Despite their relative success, most of annotation models suffer from the low-level visual representation and semantic gap. To address the above shortcomings, we propose a novel annotation method utilizing textual feature generated by image captioning, in contrast to all previous methods that use visual feature as image feature. In our method, each image is regarded as a label-vector of k userprovided textual tags rather than a visual vector. We summarize our method as follows. First, the image visual features are extracted by combining the deep residual network and the object detection model, which are encoded and decoded by the mesh-connected Transformer network model. Then, the textual modal feature vector of the image is constructed by removing stop-words and retaining high-frequency tags. Finally, the textual feature vector of the image is applied to the propagation annotation model to generate a high-quality image annotation labels. Experimental results conducted on standard MS-COCO datasets demonstrate that the proposed method significantly outperforms existing classical models, mainly benefiting from the proposed textual feature generated by image captioning technology.
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