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

Abstract

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.