Recently, a research team from the School of Electronic Science and Engineering at Southeast University, led by Professor Xu Xiaobao and Lei Wei, has made a notable breakthrough in the realm of multi-energy X-ray imaging and intelligent material identification. Their related research findings have been published in the internationally renowned journal Science Advances, under the title "Multi-energy X-ray Imaging and Intelligent Material Recognition Based on Unipolar Perovskite Detectors."

Traditional X-ray imaging techniques often struggle with accurately distinguishing different material compositions. This study tackles that challenge by innovatively introducing a method for achieving multi-energy X-ray imaging through the use of unipolar perovskite detectors. The team developed unipolar perovskite detectors with an n-i-n structure. By adjusting the applied bias voltage, they can control the depth of electron collection. This enables the continuous-spectrum X-rays to be divided into seven distinct energy channels.

Integrating these capabilities with machine learning algorithms and a comprehensive database of attenuation coefficient ratios for common materials, the researchers constructed a pixel-level intelligent material recognition system. This system can precisely differentiate between various materials and mark them with different colors, effectively overcoming the limitations of traditional imaging methods in material recognition.

Experimental validation has shown that the system is capable of distinguishing not only inorganic materials like metals but also biological tissues with similar densities, such as bone, muscle, and fat. This opens up broad application prospects in fields like medical imaging, security inspection, and biological and materials sciences. For instance, in biological tissue recognition, the system can clearly distinguish components in chicken feet, including leg bones, toe bones, palms, skin, and gelatinous tissues. In security inspection scenarios, it can identify materials such as iron, zinc, glass, copper, rubber, and cardboard.

This achievement provides crucial technical support for the advancement of a new generation of intelligent imaging technologies.