One-Step Stochastically-Assembled Copper(I) Iodide Cluster for Multimodal Physically Unclonable Functions

The rapid development of the Internet of Things and artificial intelligence has sharply increased the demand for secure, low-cost, and scalable anticounterfeiting technologies. Physical unclonable functions (PUFs) offer intrinsic protection by converting stochastic material variations into unique identifiers, but their practical implementation is often limited by complex fabrication, toxic luminophores, and low information complexity arising from single-channel readout. Here, we develop a copper(I) iodide cluster-poly(methyl methacrylate) fluorescent PUF through a single ambient coating step. Nonequilibrium solvent evaporation and polymer solidification generate randomly distributed yellow-emissive microdomains together with three-dimensional surface relief, while the cluster and polymer concentrations regulate domain coverage, intensity, and morphology without prescribing their microscopic arrangement. Binary fluorescence keys exhibit a mean interlabel Hamming distance of 0.49935 and an intralabel value of 0.00219. Incorporating discretized surface height further enables multivalued fluorescence-topography encoding. The solution-processable composite is compatible with spin coating, spray coating, transfer printing, and direct writing, allowing stochastic fingerprints to be integrated into planar, flexible, and curved objects for diverse anticounterfeiting scenarios. The fingerprints remain readable after storage, water exposure, thermal treatment, and ultraviolet irradiation. Deep-learning retrieval combined with local-feature matching enables hierarchical identification and physical verification. This work provides a simple, lead-free route to multimodal PUFs for scalable anticounterfeiting and authentication.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-10-08
DOI
https://doi.org/10.1021/acsami.6c15664
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
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article

One-Step Stochastically-Assembled Copper(I) Iodide Cluster for Multimodal Physically Unclonable Functions

Fushan Li, Jiayu Chen, Yang Liu, Tailiang Guo et al.
ACS Applied Materials & Interfaces
Physical Unclonable Functions (PUFs) and Hardware Security
article

One-Step Stochastically-Assembled Copper(I) Iodide Cluster for Multimodal Physically Unclonable Functions

Fushan Li, Jiayu Chen, Yang Liu, Tailiang Guo, Kejia You, Yilu Chen, Xuan Guo, Guotao Sun, Yehan Zeng, Shouzhao Zhan, Ning Liu
article en

Abstract

The rapid development of the Internet of Things and artificial intelligence has sharply increased the demand for secure, low-cost, and scalable anticounterfeiting technologies. Physical unclonable functions (PUFs) offer intrinsic protection by converting stochastic material variations into unique identifiers, but their practical implementation is often limited by complex fabrication, toxic luminophores, and low information complexity arising from single-channel readout. Here, we develop a copper(I) iodide cluster-poly(methyl methacrylate) fluorescent PUF through a single ambient coating step. Nonequilibrium solvent evaporation and polymer solidification generate randomly distributed yellow-emissive microdomains together with three-dimensional surface relief, while the cluster and polymer concentrations regulate domain coverage, intensity, and morphology without prescribing their microscopic arrangement. Binary fluorescence keys exhibit a mean interlabel Hamming distance of 0.49935 and an intralabel value of 0.00219. Incorporating discretized surface height further enables multivalued fluorescence-topography encoding. The solution-processable composite is compatible with spin coating, spray coating, transfer printing, and direct writing, allowing stochastic fingerprints to be integrated into planar, flexible, and curved objects for diverse anticounterfeiting scenarios. The fingerprints remain readable after storage, water exposure, thermal treatment, and ultraviolet irradiation. Deep-learning retrieval combined with local-feature matching enables hierarchical identification and physical verification. This work provides a simple, lead-free route to multimodal PUFs for scalable anticounterfeiting and authentication.

ACS Applied Materials & Interfaces
Fujian Normal University (CN), Foreign Trade University (VN), Fuzhou University (CN)
Openalex Percentile: Top 8%
Physical Unclonable Functions (PUFs) and Hardware Security
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