Electrochromic Artificial Synapses for Spatiotemporal Dual‐Encryption Display

Abstract Global data traffic growth poses unprecedented challenges to information security. This necessitates robust next‐generation solutions, particularly multidimensional protection frameworks that integrate spatial, temporal, and multispectral modulation capabilities. To address this, inspired by structure‐function coupling mechanisms in biological neurons and synapses, this study develops a novel biomimetic artificial synaptic device based on electrochromic materials. The device integrates PEDOT:PSS (enabling millisecond‐level response) and WO 3 ·H 2 O (providing stable memory) with an optimized Mg 2+ gel electrolyte. Key performance metrics include 82.9% optical modulation at 700 nm, precisely controlled multi‐state transitions, and real‐time synaptic visualization. It mimics biological learning/forgetting via paired‐pulse facilitation (PPF: 152–168%) and physiological timescale dynamics (τ 1 = 2.91 s, τ 2 = 6.97 s). Notably, these neuromorphic properties are translated into an innovative spatiotemporal dual‐encryption logic: spatially, pixelated color‐depth modulation generates geometric Morse code patterns (dot/dash = light blue/deep blue pixels); temporally, programmable optical attenuation below the 5% visual perception threshold produces time‐locked, self‐erasing encryption keys. This approach constructs a synergistic space/time/pulse‐field encryption system, fundamentally breakthrough traditional encryption frameworks. It provides an innovative physical‐layer information protection solution and expands electrochromic materials’ technological boundaries in dynamic information concealment and visual security.

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Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2025-10-16
DOI
https://doi.org/10.1002/adfm.202522755
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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article

Electrochromic Artificial Synapses for Spatiotemporal Dual‐Encryption Display

Yang Chai, Huajing Fang, Lingqi Wu, Liyan Zhang et al.
1 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
0.73
article

Electrochromic Artificial Synapses for Spatiotemporal Dual‐Encryption Display

Yang Chai, Huajing Fang, Lingqi Wu, Liyan Zhang, Haolin Yu, Kai Jing
article en
1 citations

Abstract

Abstract Global data traffic growth poses unprecedented challenges to information security. This necessitates robust next‐generation solutions, particularly multidimensional protection frameworks that integrate spatial, temporal, and multispectral modulation capabilities. To address this, inspired by structure‐function coupling mechanisms in biological neurons and synapses, this study develops a novel biomimetic artificial synaptic device based on electrochromic materials. The device integrates PEDOT:PSS (enabling millisecond‐level response) and WO 3 ·H 2 O (providing stable memory) with an optimized Mg 2+ gel electrolyte. Key performance metrics include 82.9% optical modulation at 700 nm, precisely controlled multi‐state transitions, and real‐time synaptic visualization. It mimics biological learning/forgetting via paired‐pulse facilitation (PPF: 152–168%) and physiological timescale dynamics (τ 1 = 2.91 s, τ 2 = 6.97 s). Notably, these neuromorphic properties are translated into an innovative spatiotemporal dual‐encryption logic: spatially, pixelated color‐depth modulation generates geometric Morse code patterns (dot/dash = light blue/deep blue pixels); temporally, programmable optical attenuation below the 5% visual perception threshold produces time‐locked, self‐erasing encryption keys. This approach constructs a synergistic space/time/pulse‐field encryption system, fundamentally breakthrough traditional encryption frameworks. It provides an innovative physical‐layer information protection solution and expands electrochromic materials’ technological boundaries in dynamic information concealment and visual security.

Advanced Functional MaterialsVol. 36(18)
Hong Kong Polytechnic University (HK), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
0.73
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