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Updated: Jan 7, 2026
Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management
Published on: September 2, 2015
A smart, shielding, and self-sterilizing E-skin with wavelet-enhanced signal fidelity for next-generation wearable
Xiaojing Yang1, Yijun Fu2, Chen Chen3
1School of Materials Science and Engineering, Hainan University, Haikou 570228, China; School of Information and Communication Engineering, Hainan University, Haikou 570228, China; Key Laboratory of Advanced Materials of Tropical Island Resources of Ministry of Education, Hainan University, Haikou, 570228, China.
Abstract:
The reliable acquisition of high-fidelity physiological signals in wearable electronics is currently severely hindered by the trade-off between mechanical compliance and electrical stability, particularly under complex conditions involving electromagnetic interference (EMI) and interfacial bio-contamination. Herein, a hierarchical composite conductive sensing film tailored for versatile applications is designed. The platform employs a polyacrylamide-deproteinized natural rubber (PAAm-DPNR) semi-interpenetrating polymer network (semi-IPN) as the elastic substrate. Subsequently, a polydopamine/polyethyleneimine (PDA/PEI) adhesive layer is co-deposited onto the surface to firmly anchor a silver nanowire (AgNW) conductive network, thereby constructing a composite interface characterized by both high conductivity and robust stability. This unique hierarchical architecture endows the material with superior mechanical stretchability (>500 %), impressive conductivity (8.12 × 104 S m-1), and exceptional electrical stability (ΔR/R0 < 13 % after 10,000 stretching cycles). Beyond its sensing capabilities, the platform exhibits outstanding electromagnetic interference (EMI) shielding effectiveness and potent antibacterial activity. Functioning as a wearable sensor, it enables the precise detection of diverse physiological signals, including pulse, respiration, and joint movements. To further enhance signal detection accuracy, a wavelet transform-based denoising algorithm combined with a Genetic Algorithm-optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (GA-CEEMDAN) strategy is implemented, yielding a significant improvement in the signal-to-noise ratio (>20 dB). This study provides a comprehensive strategy for the development of next-generation wearable devices capable of achieving precise monitoring in complex environments while simultaneously offering active protective functions.
