Abstract
Intelligent piezoresistive health monitoring systems integrate advanced nanocomposite architectures with precise algorithmic analysis for real-time physiological assessment. However, existing works often prioritize high sensitivity at the expense of strain tolerance and require complex fabrication procedures. Herein, we present an environmentally friendly, low-cost, and nonionic fabrication approach for a 2H-phase molybdenum disulfide (2H-MoS2)-enhanced multi-walled carbon nanotube (MWCNT) strain sensor, developed via a systematically optimized vacuum-assisted filtration process. This study is the first to validate the dual enhancement effect of MoS2, leveraging its shear-exfoliation properties to simultaneously improve strain gauge performance and mechanical robustness. The resulting nacre-like layered hybrid nanocomposite achieves a remarkable gauge factor of 675.7 (R2∼0.993) at low strain (∼0–4.5 %), representing a 3881.5 % improvement over pure MWCNT systems, alongside enhanced toughness (∼89.17 %) and strain tolerance (∼53.93 %). Meanwhile, the optimized composition ensures low rest-state resistance (∼13.1 Ω), minimal hysteresis (∼5.7 %), and robust durability over 5000 cycles at 10 % strain. As a result, the proposed sensor enables highly consistent, high-fidelity monitoring of various subtle-to-moderate biomotions. Integrated with a fine-tuned InceptionTime deep learning model, it achieves an F1-score of 98 % in classifying Dysphagia Diet Standardization Initiative (IDDSI)-standard swallowing activities, demonstrating its potential for AI-driven health monitoring applications.
| Original language | English |
|---|---|
| Article number | 113861 |
| Journal | Materials and Design |
| Volume | 253 |
| DOIs | |
| Publication status | Published - May 2025 |
Keywords
- Deep learning
- Flexible piezoresistive sensor
- Lubrication toughening
- MWCNT/MoS nanocomposite
- Subtle-to-moderate biophysical signal
ASJC Scopus subject areas
- General Materials Science
- Mechanics of Materials
- Mechanical Engineering
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