Lateral Heterostructured Vis–NIR Photodetectors with Multimodal Detection for Rapid and Precise Classification of Glioma
Hongfei Xie, Qi Pan, Dongdong Wu, Feifei Qin, Shuoran Chen, Wei Sun, Yang Xu, Sisi Chen, Tingqing Wu, Jimei Chi, Zengqi Huang, Huadong Wang, Zeying Zhang, Bingda Chen, Jan Carmeliet, Meng Su, Yanlin Song
Abstract
Precise diagnosis of the boundary and grade of tumors is especially important for surgical dissection. Recently, visible and near-infrared (Vis–NIR) absorption differences of tumors are demonstrated for a precise tumor diagnosis. Here, a template-assisted sequential printing strategy is investigated to construct lateral heterostructured Vis–NIR photodetectors, relying on the up-conversion nanoparticles (UCNPs)/perovskite arrays. Under the sequential printing process, the synergistic effect and co-confinement are demonstrated to induce the UCNPs to cover both sides of the perovskite microwire. The side-wrapped lateral heterogeneous UCNPs/perovskite structure exhibits more satisfactory responsiveness to Vis–NIR light than the common fully wrapped structure, due to sufficient visible-light-harvesting ability. The Vis–NIR photodetectors with R reaching 150 mA W–1 at 980 nm and 1084 A W–1 at 450 nm are employed for the rapid classification of glioma. The detection accuracy rate of 99.3% is achieved through a multimodal analysis covering the Vis–NIR light, which provides a reliable basis for glioma grade diagnosis. This work provides a concrete example for the application of photodetectors in tumor detection and surgical diagnosis.