properties, low cost, facile preparation and high defect tolerance. [1] Remarkable progress has been made, especially in terms of widespread applications spanning from solar cells, photodetectors, photocatalysts, and solid-state lasers to light-emitting diodes (LEDs). [2] In more recent years, owing to the exceptional luminescence properties, such as high brightness, color tunability, and intense absorption coefficient, lead halide perovskites have found promising potential applications as color converter for next-generation solid-state lightings and backlight displays. [3] Unfortunately, the intrinsic nature of poor stability and toxicity of lead halide perovskites are some serious issues to be tackled if the materials are to be used on a large scale. The chemical instability will severely restrict the lifespan of devices, and the accumulation of lead will cause serious environmental problems and fatal threat to human health. [4] Extensive efforts have been devoted to overcome the thorny challenges on the way to practical applications. To completely eliminate the potential danger of lead leakage, the simplest and practicable way is to replace it in the B site of ABX 3 with nontoxic isovalent metal ions. [5] As reasonable candidates, Ge 2+ and Sn 2+ ions are the first to come to mind for the substitution of Pb 2+ due to the same electronic configuration of ns 2 np 0 Lead halide perovskites have emerged as superstar semiconductors owing to their superior optoelectronic properties. However, the issues of chemical and thermodynamic instability and toxicity are yet to be resolved. Here, the non-and Bi 3+-doped all-inorganic lead-free perovskite derivatives are reported. Most remarkable is the successful extending of excitation of Cs 2 ZrCl 6 to match with the commercial near ultraviolet light-emitting diode chips via deliberate Bi 3+ aliovalent doping. The blue emission, contrary to self-trapped exciton (STE) emissions amply reported previously, originates from Bi 3+ ionoluminescence with a high photoluminescence quantum efficiency of 50%. The competition for harvesting electrons between STEs and Bi 3+ is studied in detail by steady-and transient-state fluorescence spectroscopy in combination with theoretical calculations. Surface grafting endows Cs 2 ZrCl 6 :Bi 3+ with a robust water-resistant core-shell-like structure and abiding emission. Surprisingly, the emission intensity even increases to 115.94% of the initial level after immersing in water for 2 h. The as-obtained phosphor enables the fabrication of a white light-emitting diode (w-LED), achieving CCT = 4179 K and Ra = 81.9. This work not only promotes the step toward development of leadfree, stable, and high-efficiency perovskite derivatives for the next-generation warm w-LEDs, but also reveals the structure-PL relationship.

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A super-long-persistent phosphor Ba2SiO4:Eu2+,Ho3+ and photostimulable luminescence for optical information storage.

An abnormal trap distribution and coordination geometry‐dependent multi‐band emission are discovered in chromium‐doped aluminates. The multi‐band and broadband persistent luminescence from 650–1100 nm peaking at 688 and 793 nm from Cr3+‐doped SrAl12O19 is systematically studied via structural and spectroscopic analysis. Solid state nuclear magnetic resonance allows the visualization of various coordination configurations in SrAl12O19, thus offering the possibility of tailoring the local geometry of the emission center to trigger the control of the spectral parameter. Deep tissue ex vivo and in vivo imaging in mice both demonstrate that multi‐band‐emissive SrAl12O19:Cr3+ shows superior, high‐quality near‐infrared (NIR) bio‐imaging in the biological transparency window compared to single band‐emissive (with emission only at 688 nm) SrAl2O4:Cr3+, although SrAl2O4:Cr3+ has a higher luminescent intensity and longer duration at 688 nm. Moreover, by measuring the thermoluminescence spectra the driving force of carrier release is discovered to be only from the deep trap (the depth is > 1 eV), which is different from the generally accepted shallow‐dependent afterglow‐emitting process. These findings pave the way for opening a vista of possible avenues for the enhancement of signal‐to‐noise ratio, the improvement of imaging quality, as well as the understanding of the trapping and de‐trapping process in long‐persistent phosphors.

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has an average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.