Advanced Anticounterfeiting: Angle-Dependent Structural Color-Based CuO/ZnO Nanopatterns with Deep Neural Network...

Area of Science:

• Materials Science
• Nanotechnology
• Optics

Background:

• Current anticounterfeiting methods are often easily replicated, require specialized equipment, and are costly.
• There is a need for scalable, low-cost, and robust anticounterfeiting solutions.

Purpose of the Study:

• To develop a novel, low-cost, and mass-producible structural color-based anticounterfeiting pattern.
• To create an efficient and accessible authentication algorithm for security applications.

Main Methods:

• Fabrication of CuO/ZnO nanopatterns via electrospinning and solution processing.
• Utilizing the inherent randomness of electrospinning for unclonable pattern generation.
• Developing a deep learning algorithm for discrimination based on shape and color features.

Main Results:

• Achieved strong angular color dependence in CuO/ZnO nanopatterns.
• Demonstrated high-density information encoding capabilities.
• Attained an average discrimination accuracy of 94% with a processing speed of 80 ms per sample using standard cameras.

Conclusions:

• The developed structural color nanopatterns offer a robust and scalable anticounterfeiting solution.
• The low-cost fabrication and accessible authentication algorithm enhance practicality for widespread adoption.
• This technology presents a next-generation approach for securing documents, currency, and brand labels.