Institute of Functional Nano and Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123, P. R. China.
Department of Applied Mathematics, School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, P. R. China.
Adv Mater. 2024 Nov;36(45):e2406235. doi: 10.1002/adma.202406235. Epub 2024 Jul 15.
The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.
现有的可穿戴压力传感器面临着巨大的挑战,由于机械传递效率低和皮肤-传感器界面的界面差异,其传感性能会下降,界面附着力也会减弱。在这里,通过引入应力集中的尖端阵列设计和自粘性界面,提高了检测极限,从而提出了一种超灵敏的可穿戴压力传感器。设计了具有不同杨氏模量的双金字塔结构,将机械传递效率从 72.6%提高到 98.4%。通过增加模量差异,机械灵敏度也被放大到 8.5 V kPa,检测极限为 0.14 Pa。开发了自粘性水凝胶以增强传感器-皮肤界面,允许进行长期和实时监测的稳定信号。它可以在无线监测微弱脉搏信号和眼肌运动时产生高信噪比和多特征。最后,结合深度学习双模融合网络,疲劳驾驶识别的准确性显著提高到 95.6%。