You Kejia, Lin Jiasong, Wang Zhen, Jiang Yi, Sun Jiayu, Lin Qinghong, Hu Xin, Fu Hongyang, Guo Xuan, Zhao Yi, Lin Liangxu, Liu Yang, Li Fushan
Strait Institute of Flexible Electronics (SIFE, Future Technologies), Fujian Key Laboratory of Flexible Electronics, Fujian Normal University and Strait Laboratory of Flexible Electronics (SLoFE), Fuzhou 350117, China.
Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China.
ACS Appl Mater Interfaces. 2025 Jan 22;17(3):5254-5267. doi: 10.1021/acsami.4c20440. Epub 2025 Jan 8.
Anticounterfeiting technologies meet challenges in the Internet of Things era due to the rapidly growing volume of objects, their frequent connection with humans, and the accelerated advance of counterfeiting/cracking techniques. Here, we, inspired by biological fingerprints, present a simple anticounterfeiting system based on perovskite quantum dot (PQD) fingerprint physical unclonable function (FPUF) by cooperatively utilizing the spontaneous-phase separation of polymers and selective in situ synthesis PQDs as an entropy source. The FPUFs offer red, green, and blue full-color fingerprint identifiers and random three-dimensional (3D) morphology, which extends binary to multivalued encoding by tuning the perovskite and polymer components, enabling a high encoding capacity (about 10, far surpassing that of biometric fingerprints). The strategy is compatible with mainstream production techniques that are widely used in traditional low-cost printed anticounterfeiting labels including spray printing, stamping, writing, and laser printing, avoiding complicated fabrication. Macrographical patterns and micro/nanofingerprint patterns with multiscale-tailorable inter-ridge sizes can be fused into a single FPUF label, satisfying different levels of anticounterfeiting requirements. Furthermore, a smart fused scheme of enhanced deep learning and fingerprint characteristic comparison is leveraged, by which high-efficiency, high-accuracy authentication of our FPUFs is achieved even for the increasingly huge FPUF databases and imperfectly captured images from users.
在物联网时代,由于物体数量迅速增长、它们与人类的频繁连接以及防伪/破解技术的加速发展,防伪技术面临挑战。在此,我们受生物指纹启发,通过协同利用聚合物的自发相分离和选择性原位合成钙钛矿量子点(PQD)作为熵源,提出了一种基于钙钛矿量子点指纹物理不可克隆功能(FPUF)的简单防伪系统。这些FPUF提供红色、绿色和蓝色全彩指纹标识符以及随机三维(3D)形态,通过调整钙钛矿和聚合物成分将二进制编码扩展为多值编码,实现了高编码容量(约为10,远远超过生物特征指纹)。该策略与广泛应用于传统低成本印刷防伪标签的主流生产技术兼容,包括喷涂、冲压、书写和激光打印,避免了复杂的制造过程。具有多尺度可定制脊间距尺寸的宏观图案和微/纳米指纹图案可以融合到单个FPUF标签中,满足不同级别的防伪要求。此外,利用了增强深度学习和指纹特征比较的智能融合方案,通过该方案,即使对于日益庞大的FPUF数据库和用户采集的不完美图像,也能实现对我们的FPUF的高效、高精度认证。