Tailoring Topological Network of Conductive Hydrogel for Electrochemically Mediated Encryption

The sustainable development of an informatized and intelligent society relies on information security. Physical unclonable cryptographic primitives effectively secure information through random physical structures. However, the limited size of challenge-response pairs renders them vulnerable to machine learning attacks. This study proposes a regional assembly crosslinking (RAC) strategy to impart hydrogels with macroscopic, unclonable electrochemical behaviors derived from topological polymeric networks. An electric-field-enhanced phase separation approach is employed to create ion-electron transduction junctions based on polypyrrole:polystyrene sulfonate (PPy:PSS), forming a transduction junction matrix within the RAC hydrogel. The distinct transduction times of individual junctions enable pulsed electrical signals to convert the unique polymeric network topology into unpredictable and unclonable electrochemical responses. The RAC hydrogel-based encryption device generates over 10<sup>19</sup> challenge-response pairs, significantly surpassing the standard requirement of 10<sup>10</sup> for a strong physical unclonable cryptographic primitive. Additionally, the inherent nonlinear electrochemical characteristics of the ion-electron junction matrix significantly enhance the resistance of RAC hydrogels against machine learning attacks, including linear regression, multi-layer perceptrons, and Transformers. This study demonstrates that the electrochemical behavior of polymer networks in conductive hydrogels can emulate 3D electronic component matrices, establishing a novel paradigm for hydrogel phase engineering in information technology applications.

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Publication Details

Journal
Advanced Materials
Published
2025-10-11
DOI
https://doi.org/10.1002/adma.202507637
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
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article

Tailoring Topological Network of Conductive Hydrogel for Electrochemically Mediated Encryption

Chuanjie Liu, Huiru Yun, Yanfei Zhao, Fei Zhao et al.
Advanced Materials
Physical Unclonable Functions (PUFs) and Hardware Security
article

Tailoring Topological Network of Conductive Hydrogel for Electrochemically Mediated Encryption

Chuanjie Liu, Huiru Yun, Yanfei Zhao, Fei Zhao, Zhou Li, Yuke Yan, Xinyue Liu
article en

Abstract

The sustainable development of an informatized and intelligent society relies on information security. Physical unclonable cryptographic primitives effectively secure information through random physical structures. However, the limited size of challenge-response pairs renders them vulnerable to machine learning attacks. This study proposes a regional assembly crosslinking (RAC) strategy to impart hydrogels with macroscopic, unclonable electrochemical behaviors derived from topological polymeric networks. An electric-field-enhanced phase separation approach is employed to create ion-electron transduction junctions based on polypyrrole:polystyrene sulfonate (PPy:PSS), forming a transduction junction matrix within the RAC hydrogel. The distinct transduction times of individual junctions enable pulsed electrical signals to convert the unique polymeric network topology into unpredictable and unclonable electrochemical responses. The RAC hydrogel-based encryption device generates over 10<sup>19</sup> challenge-response pairs, significantly surpassing the standard requirement of 10<sup>10</sup> for a strong physical unclonable cryptographic primitive. Additionally, the inherent nonlinear electrochemical characteristics of the ion-electron junction matrix significantly enhance the resistance of RAC hydrogels against machine learning attacks, including linear regression, multi-layer perceptrons, and Transformers. This study demonstrates that the electrochemical behavior of polymer networks in conductive hydrogels can emulate 3D electronic component matrices, establishing a novel paradigm for hydrogel phase engineering in information technology applications.

Advanced MaterialsVol. 38(4)
Beijing Institute of Technology (CN)
Openalex Percentile: Top 69%
Physical Unclonable Functions (PUFs) and Hardware Security
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