Xing Canglong, Luo Ming, Sheng Qiuhui, Zhu Zhichao, Yu Dan, Huang Jian, He Dan, Zhang Meng, Fan Wei, Chen Dongzhen
School of Materials Science and Engineering, Key Laboratory of Functional Textile Material and Product of the Ministry of Education, Xi'an Key Laboratory of Textile Composites, Xi'an Polytechnic University, Xi'an 710048, China.
CPL New Material Technology Company, Ltd., Jiashan, Zhejiang 314100, China.
ACS Appl Mater Interfaces. 2024 Sep 25;16(38):51669-51678. doi: 10.1021/acsami.4c10253. Epub 2024 Sep 13.
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 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.
将生物力学和生物分子传感机制集成到可穿戴设备中是一项艰巨的挑战，也是实现个性化健康管理的关键。为了解决这个问题，我们开发了一种由等离子体功能化丝绸织物制成的创新型多功能传感器，它具有生物力学和生物分子的多模态传感能力。采用种子介导生长法将银纳米颗粒（AgNPs）包覆在丝绸纤维上，得到功能化的含银纳米颗粒丝绸纤维（SFs@Ag），其表现出压阻响应和局域表面等离子体共振效应。SFs@Ag膜能够在可穿戴传感过程中准确检测机械压力和特定生物分子，为全面的个性化健康监测提供了一种通用解决方案。此外，还建立了一种机器学习算法来专门识别肌肉应变信号，这有可能扩展到对诸如肌萎缩侧索硬化症（ALS）等神经肌肉疾病的诊断和监测。与临床医学中检测大肌肉的肌电图不同，使用SFs@Ag传感器检测微小肌肉的传感数据能增强我们对肌肉协调性的理解。这种检测模型为神经肌肉疾病的早期检测和预防提供了可行性。除了肌肉压力和应变传感外，生物分子检测是实现有效健康管理的关键补充。在本研究中，我们开发了用于可穿戴健康监测的高灵敏度表面增强拉曼散射（SERS）检测。利用时域有限差分数值模拟分析了SFs@Ag传感器用于可穿戴生物分子SERS传感的效果。基于特定的SERS光谱，还实现了汗液分子信号的自动提取。总之，SFs@Ag传感器弥合了可穿戴应用中生物力学和生物分子传感之间的差距，为个性化健康管理提供了重要价值。
