Inkjet Printed Physically-Unclonable Structural-Color Anticounterfeiting Labels with Convenient Artificial Intelligence Authentication

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Abstract

Inkjet printing is a deterministic process with distinguished advantages of low cost, mass production, and unlimited pattern design capability, but it usually excludes randomness and the corresponding physically unclonable functions (PUFs). Herein, inkjet printing is utilized to fabricate physically unclonable structural-color anticounterfeiting labels with reliable random photonic structure and convenient artificial intelligence (AI) authentication. The abundant microscale print-drops with random arrangements, consisting of the cross-linked short-range ordered arrays of monodisperse poly(styrene-methylmethacrylate-acrylic acid) (P(St-MMA-AA)) nanospheres, enabled full-color hues, and the reliable encoded PUFs, simultaneously. The encoded PUF information with extremely high encoding capacity can be learned and validated with AI, realizing the fast and accurate decoding of the PUFs and thus the unclonable anticounterfeiting applications. Importantly, the coupling of the structural-color anticounterfeiting labels with perfluorodecyltrimethoxysilane of low surface energy leads to high water resistance, which further improves the structural stability of the labels against water penetration. Based on the package-compatible inkjet printing approach and the reliable AI authentication, the physically unclonable structural-color anticounterfeiting labels with high structural stability and reliable AI validation bridges the gap between convenience (in both fabrication and validation) and ultimate security, opening enormous opportunities for their security applications in medicine, documents, currency, or other high-value products.

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Li, Y., Liu, Z., Zhu, K., Ai, L., Jia, P., Wu, N., … Song, Y. (2021). Inkjet Printed Physically-Unclonable Structural-Color Anticounterfeiting Labels with Convenient Artificial Intelligence Authentication. Advanced Materials Interfaces, 8(21). https://doi.org/10.1002/admi.202101281

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