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An all-in-one nanoprinting approach for the synthesis of a nanofilm library for unclonable anti-counterfeiting applications

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Zhang,  Junfang
Felix Löffler, Biomolekulare Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Liu,  Yuxin       
Felix Löffler, Biomolekulare Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Ronneberger,  Sebastian
Felix Löffler, Biomolekulare Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Tarakina,  Nadezda V.       
Nadezda V. Tarakina, Kolloidchemie, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Löffler,  Felix F.
Felix Löffler, Biomolekulare Systeme, Max Planck Institute of Colloids and Interfaces, Max Planck Society;

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Citation

Zhang, J., Liu, Y., Njel, C., Ronneberger, S., Tarakina, N. V., & Löffler, F. F. (2023). An all-in-one nanoprinting approach for the synthesis of a nanofilm library for unclonable anti-counterfeiting applications. Nature Nanotechnology, 18, 1027-1035. doi:10.1038/s41565-023-01405-3.


Cite as: https://hdl.handle.net/21.11116/0000-000D-4073-9
Abstract
In addition to causing trillion-dollar economic losses every year, counterfeiting threatens human health, social equity and national security. Current materials for anti-counterfeiting labelling typically contain toxic inorganic quantum dots and the techniques to produce unclonable patterns require tedious fabrication or complex readout methods. Here we present a nanoprinting-assisted flash synthesis approach that generates fluorescent nanofilms with physical unclonable function micropatterns in milliseconds. This all-in-one approach yields quenching-resistant carbon dots in solid films, directly from simple monosaccharides. Moreover, we establish a nanofilm library comprising 1,920 experiments, offering conditions for various optical properties and microstructures. We produce 100 individual physical unclonable function patterns exhibiting near-ideal bit uniformity (0.492 ± 0.018), high uniqueness (0.498 ± 0.021) and excellent reliability (>93%). These unclonable patterns can be quickly and independently read out by fluorescence and topography scanning, greatly improving their security. An open-source deep-learning model guarantees precise authentication, even if patterns are challenged with different resolutions or devices.