Sharma Kusum, Bhunia Kousik, Chatterjee Subhajit, Perumalsamy Muthukumar, Saj Anandhan Ayyappan, Bhatti Theophilus, Byun Yung-Cheol, Kim Sang-Jae
Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, Republic of Korea.
Department of Computer Engineering, Jeju National University, Jeju-Si, 63243, Republic of Korea.
Nanomicro Lett. 2025 Sep 8;18(1):63. doi: 10.1007/s40820-025-01912-z.
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring, clinical diagnosis, and robotic applications. Nevertheless, it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility, adhesion, self-healing, and environmental robustness with excellent sensing metrics. Herein, we report a multifunctional, anti-freezing, self-adhesive, and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes (CoN CNT) embedded in a polyvinyl alcohol-gelatin (PVA/GLE) matrix. Fabricated using a binary solvent system of water and ethylene glycol (EG), the CoN CNT/PVA/GLE organogel exhibits excellent flexibility, biocompatibility, and temperature tolerance with remarkable environmental stability. Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range (40%-95% RH). Freeze-tolerant conductivity under sub-zero conditions (-20 °C) is attributed to the synergistic role of CoN CNT and EG, preserving mobility and network integrity. The CoN CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 kPa in the detection range from 0 to 20 kPa, ideal for subtle biomechanical motion detection. A smart human-machine interface for English letter recognition using deep learning achieved 98% accuracy. The organogel sensor utility was extended to detect human gestures like finger bending, wrist motion, and throat vibration during speech.
集成深度学习技术的可穿戴传感器有潜力彻底改变用于实时健康监测、临床诊断和机器人应用的无缝人机界面。然而，要同时实现理想的机械和电气性能以及生物相容性、粘附性、自愈性和具有出色传感指标的环境稳健性，仍然是一项关键挑战。在此，我们报告一种多功能、抗冻、自粘性和自愈合的有机凝胶压力传感器，它由嵌入聚乙烯醇 - 明胶（PVA/GLE）基质中的钴纳米颗粒封装的氮掺杂碳纳米管（CoN CNT）组成。使用水和乙二醇（EG）的二元溶剂体系制备，CoN CNT/PVA/GLE有机凝胶具有出色的柔韧性、生物相容性和温度耐受性以及显著的环境稳定性。电化学阻抗谱证实了在宽湿度范围（40% - 95%RH）内近乎稳定的性能。零下条件（-20°C）下的耐冻导电性归因于CoN CNT和EG的协同作用，保持了迁移率和网络完整性。CoN CNT/PVA/GLE有机凝胶传感器在0至20kPa的检测范围内表现出5.75kPa的高灵敏度，非常适合细微生物力学运动检测。使用深度学习实现的用于英文字母识别的智能人机界面准确率达到98%。该有机凝胶传感器的用途扩展到检测人类手势，如手指弯曲、手腕运动和说话时的喉咙振动。
