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      Thermally Activated Delayed Fluorescence Zirconium‐Based Perovskites for Large‐Area and Ultraflexible X‐ray Scintillator Screens

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          Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

          In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
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            All-inorganic perovskite nanocrystal scintillators

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              Efficient and stable emission of warm-white light from lead-free halide double perovskites

              Lighting accounts for one-fifth of global electricity consumption1. Single materials with efficient and stable white-light emission are ideal for lighting applications, but photon emission covering the entire visible spectrum is difficult to achieve using a single material. Metal halide perovskites have outstanding emission properties2,3; however, the best-performing materials of this type contain lead and have unsatisfactory stability. Here we report a lead-free double perovskite that exhibits efficient and stable white-light emission via self-trapped excitons that originate from the Jahn-Teller distortion of the AgCl6 octahedron in the excited state. By alloying sodium cations into Cs2AgInCl6, we break the dark transition (the inversion-symmetry-induced parity-forbidden transition) by manipulating the parity of the wavefunction of the self-trapped exciton and reduce the electronic dimensionality of the semiconductor4. This leads to an increase in photoluminescence efficiency by three orders of magnitude compared to pure Cs2AgInCl6. The optimally alloyed Cs2(Ag0.60Na0.40)InCl6 with 0.04 per cent bismuth doping emits warm-white light with 86 ± 5 per cent quantum efficiency and works for over 1,000 hours. We anticipate that these results will stimulate research on single-emitter-based white-light-emitting phosphors and diodes for next-generation lighting and display technologies.
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                Author and article information

                Contributors
                Journal
                Advanced Materials
                Advanced Materials
                Wiley
                0935-9648
                1521-4095
                October 2022
                September 23 2022
                October 2022
                : 34
                : 43
                : 2204801
                Affiliations
                [1 ]Key Laboratory of Materials Physics of Ministry of Education School of Physics and Microelectronics Zhengzhou University Daxue Road 75 Zhengzhou 450052 China
                [2 ]Institute of Microstructure and Property of Advanced Materials Beijing Key Lab of Microstructure and Property of Advanced Materials Faculty of Materials and Manufacturing Beijing University of Technology Beijing 100124 China
                [3 ]State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China
                Article
                10.1002/adma.202204801
                36047911
                df877f9e-138c-432a-abdd-d0164bc88a4a
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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