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Updated: Jun 17, 2026

Bridging the Bio-Electronic Interface with Biofabrication
Published on: June 6, 2012
Lignin-Regulated Amphiphilic Janus Membrane for Eco-Friendly Electronic Skin
Yuan He1, Haichuan Ye1, Cuihuan Li1
1State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing 100083, P. R. China.
Abstract:
Eco-friendly electronic skin fabricated from sustainable natural materials represents a pivotal technology for advancing healthcare and sports-oriented wearable electronics, yet balancing comfort and electronic functionality remains challenging due to inadequate moisture management. Herein, we report an eco-friendly lignin-regulated amphiphilic Janus membrane (LAJM)-based pressure sensor that synergistically integrates unidirectional water transport with exceptional sensing performance. Through sulfonation and fluorination modifications of lignin fractions with distinct hydrophilic and hydrophobic properties, we engineer an electrospun layer with controlled hydrophobic-hydrophilic gradients, enabling unidirectional water transport while maintaining a dry and comfortable microenvironment. Moreover, LAJM demonstrates notable biocompatibility, antibacterial efficacy, and antioxidant properties, positioning it as a promising candidate for intelligent wound care systems. The LAJM-based pressure sensor achieves integrated sensing capabilities through its hierarchical structure, exhibiting a high sensitivity of 23.97 kPa-1 in the 0-20 kPa range, an extended operational range (0-200 kPa), and ultrafast response dynamics (22 ms response/31 ms recovery). These metrics enable robust continuous monitoring of vital signs (e.g., radial pulse waveforms), discrimination of voice patterns, and precise motion trajectory mapping under dynamic conditions. This technological breakthrough in lignin valorization establishes a scalable platform for next-generation breathable epidermal electronics, with transformative implications for AI-driven healthcare diagnostics and adaptive human-machine interfaces.