Bio‐Inspired Design of Quasi‐Ordered Structural Color via Stress‐Driven Reconfiguration Enables Ultra‐Secure and Scalable Unclonable Application

X Xiaofeng Lin D Dan Song (Kainomyx Inc.) C Changsong Xu (Department of Physics, Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, Fudan University) J Jin Peng D Daxiang Shi (School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China) Y Yingjuan Sun (School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China) J Jiayao Chen (State Key Laboratory of Organic–Inorganic Composites, College of Materials Science and Engineering) C Cheng Wang D Dingshan Yu G Guobin Yi (School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China)

Abstract

ABSTRACT Physically unclonable functions (PUFs) offer intrinsic security for next‐generation authentication systems, yet current optical PUFs face a trilemma—simultaneously achieving high coding capacity, rapid recognition, and scalable manufacturing in one stable system. Inspired by quasi‐ordered photonic structures in Thecla opisena wing scales, we report a stress‐driven microstructural reconfiguration strategy that reversely emulates the natural evolution of photonic domains under mechanical constraints. By controlled imprinting of heterogeneous polymer networks comprising poly(butyl acrylate) microspheres, one can attain spatially random, structurally colored PUF patterns featuring microscopic periodicity and macroscopic disorder. Theoretical simulation reveals that manipulating stress fields in crosslinked elastomers induces stochastic microsphere rearrangements, establishing the physical origin of entropy and unclonability. The resulting mechanically‐induced structural color PUF labels (MSCPLs) exhibit ultrahigh encoding capacity of 2 480×480 (derived from the physical correlation length within 1800 × 1800 µm), sub‐2 s recognition with 99% accuracy via a deep learning assisted strategy of hierarchical classification and dynamic database expansion, and outstanding durability (>85% pattern retention after 1000 bending cycles). Unlike conventional optical PUFs, our strategy enables large‐area fabrication (10 × 10 cm) with superior environmental resilience. This bio‐inspired methodology establishes a robust/scalable platform for secure identification and Internet of Things applications, bridging structural color aesthetics with advanced physical cryptography.

Article Details

Volume / Issue Vol. 38, Issue 17
Published March 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (10)

X

Xiaofeng Lin

D

Dan Song

Kainomyx Inc.

C

Changsong Xu

Department of Physics, Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, Fudan University

J

Jin Peng

D

Daxiang Shi

School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China

Y

Yingjuan Sun

School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China

J

Jiayao Chen

State Key Laboratory of Organic–Inorganic Composites, College of Materials Science and Engineering

C

Cheng Wang

D

Dingshan Yu

G

Guobin Yi

School of Chemical Engineering and Light Industry Guangdong University of Technology Guangzhou P. R. China