Vertical matrix perovskite X-ray detector for effective multi-energy discrimination

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.