In a significant advancement in the field of anti-counterfeiting technology, Professor Jiseok Lee and his research team in the School of Energy and Chemical Engineering at UNIST have developed a new hidden anti-counterfeiting technology, harnessing the unique properties of silver nanoparticles (AgNPs). The results are published in Advanced Materials.
“The technology we have developed holds significant promise in preventing the counterfeiting of valuable artworks and defense materials, particularly in scenarios where authenticity must be verified against potential piracy,” Professor Lee explained.
The team leveraged the inherent disadvantage of AgNPs, which tend to discolor upon exposure to UV light, to create a controlled color development process. By trapping silver nanoparticles within a polymer matrix, researchers can manipulate particle size and, consequently, the color emitted under UV light. Larger polymer nets yield silver nanoparticles that appear yellow, while smaller nets produce a red hue, allowing for precise control of the resultant colors based on ingredient combinations.
Using these high-molecular structures as pixels, the research team successfully crafted high-resolution color images. Utilizing an automated photo-etching technique, they reduced the fabrication time to one-tenth of traditional methods, producing an image of a parrot larger than a standard business card in just 30 minutes. This digital process allows for flawless color printing, with precise control over saturation and tone.
In addition to images, anti-counterfeiting data can be discreetly embedded in arrangements of polymer structures that resemble red, yellow, and blue barcodes. The color response varies with UV exposure time, allowing for the storage of temporal information within the barcode structure.
This innovative approach enables information storage capabilities to increase more than 1,000-fold compared to conventional methods, with a potential for unlimited data encoding by arranging barcode particles without additional synthesis.
To enhance the reliability of this technology, the research team developed an artificial intelligence algorithm capable of analyzing barcode authenticity. This AI system boasts a remarkable reliability rate of 98.36%, distinguishing genuine barcodes from counterfeit ones by assessing material composition, UV exposure duration, and barcode integrity.
“The simplicity of the manufacturing process and the reproducibility of colors present a substantial opportunity for the advancement of information encryption systems, particularly in anti-counterfeiting applications,” stated Byungcheon Yoo, the lead author of the study.
