Fine Optimization of Colloidal Photonic Crystal Structural Color for Physically Unclonable Multiplex Encryption and Anti‐Counterfeiting
Abstract: Robust anti‐counterfeiting techniques aim for easy identification while remaining difficult to forge, especially for high‐value items such as currency and passports. However, many existing anti‐counterfeiting techniques rely on deterministic processes, resulting in loopholes for duplication and counterfeiting. Therefore, achieving high‐level encryption and easy authentication through conventional anti‐counterfeiting techniques has remained a significant challenge. To address this, this work proposes a solution that combined fluorescence and structural colors, creating a physically unclonable multiplex encryption system (PUMES). In this study, the physicochemical properties of colloidal photonic inks are systematically adjusted to construct a comprehensive printing phase diagram, revealing the printable region. Furthermore, the brightness and color saturation of inkjet‐printed colloidal photonic crystal structural colors are optimized by controlling the substrate's hydrophobicity, printed droplet volume, and the addition of noble metals. Finally, fluorescence is incorporated to build PUMES, including macroscopic fluorescence and structural color patterns, as well as microscopic physically unclonable fluorescence patterns. The PUMES with intrinsic randomness and high encoding capacity are authenticated by a deep learning algorithm, which proved to be reliable and efficient under various observation conditions. This approach can provide easy identification and formidable resistance against counterfeiting, making it highly promising for the next‐generation anti‐counterfeiting of currency and passports.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Fine Optimization of Colloidal Photonic Crystal Structural Color for Physically Unclonable Multiplex Encryption and Anti‐Counterfeiting ; day:04 ; month:04 ; year:2024 ; extent:13
Advanced science ; (04.04.2024) (gesamt 13)
- Creator
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Gao, Yifan
Ge, Kongyu
Zhang, Zhen
Li, Zhan
Hu, Shaowei
Ji, Hongjun
Li, Mingyu
Feng, Huanhuan
- DOI
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10.1002/advs.202305876
- URN
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urn:nbn:de:101:1-2024040514244219893825
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:59 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Gao, Yifan
- Ge, Kongyu
- Zhang, Zhen
- Li, Zhan
- Hu, Shaowei
- Ji, Hongjun
- Li, Mingyu
- Feng, Huanhuan
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