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      Self-powered photodetectors based on CsPbBr3 quantum dots/organic semiconductors/SnO2 heterojunction for weak light detection

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          Highly narrowband perovskite single-crystal photodetectors enabled by surface-charge recombination

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            High-detectivity polymer photodetectors with spectral response from 300 nm to 1450 nm.

            Sensing from the ultraviolet-visible to the infrared is critical for a variety of industrial and scientific applications. Today, gallium nitride-, silicon-, and indium gallium arsenide--based detectors are used for different sub-bands within the ultraviolet to near-infrared wavelength range. We demonstrate polymer photodetectors with broad spectral response (300 to 1450 nanometers) fabricated by using a small-band-gap semiconducting polymer blended with a fullerene derivative. Operating at room temperature, the polymer photodetectors exhibit detectivities greater than 10(12) cm Hz(1/2)/W and a linear dynamic range over 100 decibels. The self-assembled nanomorphology and device architecture result in high photodetectivity over this wide spectral range and reduce the dark current (and noise) to values well below dark currents obtained in narrow-band photodetectors made with inorganic semiconductors.
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              Colloidal CsPbBr3Perovskite Nanocrystals: Luminescence beyond Traditional Quantum Dots

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                Author and article information

                Journal
                Science China Materials
                Sci. China Mater.
                Springer Science and Business Media LLC
                2095-8226
                2199-4501
                February 2023
                September 01 2022
                February 2023
                : 66
                : 2
                : 716-723
                Article
                10.1007/s40843-022-2155-0
                436315c8-258e-4818-959c-4fd2ca02176d
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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