High-capacity data decoding from miniaturized magnetic tags for anti-counterfeiting and reliable identification

Magnetic nanomaterials offer a versatile platform for advanced information encoding, enabling high data capacity, enhanced counterfeit resistance, and reliable product identification. Here, we present two complementary encoding approaches based on magnetic polymeric supraparticles as miniaturized tags and magnetic particle spectroscopy as the decoding technique. The first exploits variations in nanoparticle composition and polymer matrix to generate one code per tag. The second leverages the nonlinear magnetic response of a single tag under different excitation field conditions, enabling multiple distinguishable codes per tag, shifting the encoding paradigm from material-design to a challenge-response framework. We assess the reliability of the decoding procedure using a dedicated statistical methodology defining reliable distinguishability thresholds. Moreover, translating harmonic spectra into binary keys enables the application of information-theoretic metrics, including Shannon entropy and fractional Hamming distance, to evaluate code robustness and separability. Combined with the robustness of the MPS decoding strategy, these results establish magnetic supraparticles as a scalable platform for high-density, physically secure encoding and authentication. Magnetic nanomaterials enable secure, high-capacity information encoding. Here, the authors developed magnetic polymeric supraparticle tags and demonstrated two encoding strategies with magnetic particle spectroscopy for secure, high-density information encoding and authentication.

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Publication Details

Journal
Communications Materials
Published
2026-09-25
DOI
https://doi.org/10.1038/s43246-026-01368-7
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
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High-capacity data decoding from miniaturized magnetic tags for anti-counterfeiting and reliable identification

Federica Celegato, Federico Scaglione, Elena Togliatti, Stefano Zago et al.
Communications Materials
Physical Unclonable Functions (PUFs) and Hardware Security
article

High-capacity data decoding from miniaturized magnetic tags for anti-counterfeiting and reliable identification

Federica Celegato, Federico Scaglione, Elena Togliatti, Stefano Zago, Gajanan Pradhan, Paola Maria Tiberto, Daniel Milanese, Gabriele Barrera, Mario Malerba, Marzia Gallo, Corrado Sciancalepore
article en

Abstract

Magnetic nanomaterials offer a versatile platform for advanced information encoding, enabling high data capacity, enhanced counterfeit resistance, and reliable product identification. Here, we present two complementary encoding approaches based on magnetic polymeric supraparticles as miniaturized tags and magnetic particle spectroscopy as the decoding technique. The first exploits variations in nanoparticle composition and polymer matrix to generate one code per tag. The second leverages the nonlinear magnetic response of a single tag under different excitation field conditions, enabling multiple distinguishable codes per tag, shifting the encoding paradigm from material-design to a challenge-response framework. We assess the reliability of the decoding procedure using a dedicated statistical methodology defining reliable distinguishability thresholds. Moreover, translating harmonic spectra into binary keys enables the application of information-theoretic metrics, including Shannon entropy and fractional Hamming distance, to evaluate code robustness and separability. Combined with the robustness of the MPS decoding strategy, these results establish magnetic supraparticles as a scalable platform for high-density, physically secure encoding and authentication. Magnetic nanomaterials enable secure, high-capacity information encoding. Here, the authors developed magnetic polymeric supraparticle tags and demonstrated two encoding strategies with magnetic particle spectroscopy for secure, high-density information encoding and authentication.

Communications Materials
University of Parma (IT), Istituto Nazionale di Ricerca Metrologica (IT), National Interuniversity Consortium of Materials Science and Technology (IT), University of Turin (IT)
Openalex Percentile: Top 6%
Physical Unclonable Functions (PUFs) and Hardware Security
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