Silk Fabric Functionalized by Nanosilver Enabling the Wearable Sensing for Biomechanics and Biomolecules

材料科学 可穿戴计算机 生物分子 纳米技术 可穿戴技术 织物 丝绸 复合材料 计算机科学 嵌入式系统
作者
Canglong Xing,Ming Luo,Qiuhui Sheng,Zhichao Zhu,Dan Yu,Jian Feng Huang,Dan He,Meng Zhang,Wei Fan,Dongzhen Chen
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (38): 51669-51678 被引量:22
标识
DOI:10.1021/acsami.4c10253
摘要

Integrating biomechanical and biomolecular sensing mechanisms into wearable devices is a formidable challenge and key to acquiring personalized health management. To address this, we have developed an innovative multifunctional sensor enabled by plasma functionalized silk fabric, which possesses multimodal sensing capabilities for biomechanics and biomolecules. A seed-mediated in situ growth method was employed to coat silver nanoparticles (AgNPs) onto silk fibers, resulting in silk fibers functionalized with AgNPs (SFs@Ag) that exhibit both piezoresistive response and localized surface plasmon resonance effects. The SFs@Ag membrane enables accurate detection of mechanical pressure and specific biomolecules during wearable sensing, offering a versatile solution for comprehensive personalized health monitoring. Additionally, a machine learning algorithm has been established to specifically recognize muscle strain signals, potentially extending to the diagnosis and monitoring of neuromuscular disorders such as amyotrophic lateral sclerosis (ALS). Unlike electromyography, which detects large muscles in clinical medicine, sensing data for tiny muscles enhance our understanding of muscle coordination using the SFs@Ag sensor. This detection model provides feasibility for the early detection and prevention of neuromuscular diseases. Beyond muscle stress and strain sensing, biomolecular detection is a critical addition to achieving effective health management. In this study, we developed highly sensitive surface-enhanced Raman scattering (SERS) detection for wearable health monitoring. Finite-difference time-domain numerical simulations ware utilized to analyze the efficacy of the SFs@Ag sensor for wearable SERS sensing of biomolecules. Based on the specific SERS spectra, automatic extraction of signals of sweat molecules was also achieved. In summary, the SFs@Ag sensor bridges the gap between biomechanical and biomolecular sensing in wearable applications, providing significant value for personalized health management.
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