Journal
LIGHT-SCIENCE & APPLICATIONS
Volume 11, Issue 1, Pages -Publisher
SPRINGERNATURE
DOI: 10.1038/s41377-022-00791-y
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Funding
- Major State Basic Research Development Program of China [2021YFB3201000]
- National Natural Science Foundation of China [62074066, 62134003, 12050005]
- Fund for Innovative Research Groups of the Natural Science Foundation of Hubei Province [2021CFA036, 2020CFA034]
- Shenzhen Basic Research Program [JCYJ20200109115212546]
- Fundamental Research Funds for the Central Universities HUST [2020JYCXJJ073]
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This study reports the design of vertical matrix perovskite X-ray detectors for multi-energy detection. The detectors can accurately reconstruct incident X-ray spectra and produce a set of images with density-graded information under single exposure. It can also locate the position of substances with different densities.
(.)Multi-energy X-ray detection is sought after for a wide range of applications including medical imaging, security checking and industrial flaw inspection. Perovskite X-ray detectors are superior in terms of high sensitivity and low detection limit, which lays a foundation for multi-energy discrimination. However, the extended capability of the perovskite detector for multi-energy X-ray detection is challenging and has never been reported. Herein we report the design of vertical matrix perovskite X-ray detectors for multi-energy detection, based on the attenuation behavior of X-ray within the detector and machine learning algorithm. This platform is independent of the complex X-ray source components that constrain the energy discrimination capability. We show that the incident X-ray spectra could be accurately reconstructed from the conversion matrix and measured photocurrent response. Moreover, the detector could produce a set of images containing the density-graded information under single exposure, and locate the concealed position for all low-, medium- and high-density substances. Our findings suggest a new generation of X-ray detectors with features of multi-energy discrimination, density differentiation, and contrast-enhanced imaging.
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