Advanced Anticounterfeiting: Angle-Dependent Structural Color-Based CuO/ZnO Nanopatterns with Deep Neural Network Supervised Learning - Choi, Mun Jeong; Kim, SeongYeon; Shin, Jongho; Kim, Geon Hwee (ACS applied materials & interfaces, 2025)
Current anticounterfeiting technologies rely on deterministic processes that are easily replicable, require specialized devices for authentication, and involve complex manufacturing, resulting in high costs and limited scalability. This study presents a low-cost, mass-producible structural color-based anticounterfeiting pattern and a simple algorithm for discrimination. Nanopatterns aligned with the direction of incident light were fabricated by electrospinning, while CuO and ZnO were grown independently through a solution process. CuO acts as a reflective layer, imparting an angle-dependent color dependence, while ZnO allows the structural color to be tuned by controlling the hydrothermal synthesis time. The inherent randomness of electrospinning enables the creation of unclonable patterns, providing a robust anticounterfeiting solution. The fabricated CuO/ZnO nanopatterns exhibit strong angular color dependence and are capable of encoding high-density information. It uses deep learning algorithms to achieve an average discrimination accuracy of 94%, with a streamlined computational structure based on shape and color features to achieve a processing speed of 80 ms per sample. The training images are acquired with standard high-resolution cameras, ensuring accessibility and practicality. This approach offers an efficient and scalable next-generation solution for anticounterfeiting applications, including documents, currency, and brand labels.