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Biomass-Based Hydrogel Flexible Sensors for Multi-Scenario Vital Sign Monitoring
Release time：
2025-07-26
Recently, Professor Yuxia Chen's team at the Anhui Engineering Research Center for Healthy Sleep Household Products, School of Materials and Chemistry, Anhui Agricultural University, published their latest research on biomass-based hydrogel flexible sensors in Advanced Functional Materials (CAS Q1 TOP, IF=18.5, Nature Index journal), titled 'Multifunctional Luffa Sponge Hydrogel with High Mechanical Strength, Fatigue Resistance, and Ionic Conductivity for Monitoring Human Vital Signs'.
Biomass-based multifunctional hydrogels exhibit high mechanical strength, fatigue resistance, and conductivity, making them ideal materials for flexible electronics.However, achieving these mutually exclusive properties simultaneously remains challenging. This study employs a simple, mild radical polymerization method to construct a high-strength, highly conductive, and multifunctional hydrogel flexible sensor (WLSHG) based on natural loofah sponge (LS) fiber scaffolds.Delignification of LS released tight inter-fiber connections while preserving the macro-morphology and flexibility of the original 3D scaffold.
Subsequently, the synergistic tannic acid (TA)-Fe³⁺ autocatalytic system and H₂O/EG solvent system enabled in-situ polymerization of acrylamide (PAM) on the LS fiber scaffold, forming a unique composite structure with polymer-encapsulated LS fibers.Benefiting from LS's innate robust 3D scaffold and multi-scale molecular interactions, the WLSHG hydrogel exhibits exceptional mechanical strength and structural stability, enduring over 1,000 large-strain cycles.Simultaneously, the Fe³⁺ as conductive media, combined with the multiscale ion transport channels constructed by LS's inherent micro/nano-porous structure, endows the hydrogel with outstanding ionic conductivity (0.124 S m⁻¹) and pressure-sensing sensitivity (2.03 kPa⁻¹).
Leveraging these properties, this study integrates the WLSHG hydrogel sensor with IoT technology and machine learning algorithms, achieving high-accuracy recognition of both static sitting postures and dynamic gaits, demonstrating its potential for multi-scenario vital sign monitoring.This study provides a novel and effective approach for developing high-performance biomass-based hydrogel sensors and their practical applications in smart home systems.
Master's student Pucen Cao, postdoctoral researcher Jie Wei, and graduate student Tingting Zhang from the School of Materials and Chemistry at Anhui Agricultural University are co-first authors of the paper. Associate Professor Chao Ma, Professor Yuxia Chen, and Professor Yong Guo serve as corresponding authors, with Anhui Agricultural University as the sole corresponding institution.The authors acknowledge the Analytical & Testing support from Anhui Engineering Research Center for Healthy Sleep Household Products and the Forestry Engineering Discipline Platform of Anhui Agricultural University. This work was also supported by the China Postdoctoral Science Foundation, Anhui Provincial Natural Science Foundation for Young Scholars, Anhui Key R&D Program, and Anhui Agricultural University High-Level Talent Introduction Project
Citation: China Electronic Components Association.
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