Chen Ziwei, Qian Daoxiong, Xie Dandan, Gao Chunxia, Shi Jian, Morikawa Hideaki, Zhu Chunhong
Graduate School of Medicine, Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano, 3868567, Japan.
Institute for Fiber Engineering and Science (IFES), Shinshu University, 3-15-1 Tokida, Ueda, Nagano, 3868567, Japan.
Adv Sci (Weinh). 2025 Mar;12(12):e2416564. doi: 10.1002/advs.202416564. Epub 2025 Feb 4.
A fibrous flexible sensor, with its small size, minimally burdens the human body, ranking among the most user-friendly flexible sensors. However, its application is often limited by damage caused by electrode movement, as flexible sensors are typically attached to joints, which can be greatly alleviated by placing the two electrodes on the same side. Inspired by the hydrogen bonds in the double-helical structure of DNA, the double-helical electrode design is commonly found and applied in fiber-based batteries and supercapacitors into fibrous flexible sensors through coaxial wet-spinning and further treatment. The double helical sensor exhibits high strength and maintains stable operation and is prepared under over 300% strain with gauge factors (GF) of 0.9, 39.5, and 349, respectively, in its working ranges. This unique single-sided electrode structure also enabled applications such as water flow sensing. The sensor into a smart glove capable of real-time is further integrated, five-channel finger motion detection, and used a convolutional neural network (CNN)-based machine learning algorithm to achieve 98.8% accuracy in recognizing six common gestures. This study provides a novel approach to optimize the electrode distribution in fiber-based flexible sensors through an internally encapsulated double-helical structure, making a significant contribution to the field of flexible sensing.
一种纤维柔性传感器,因其尺寸小,对人体负担极小,是最便于使用的柔性传感器之一。然而,其应用常常受到电极移动造成的损坏的限制,因为柔性传感器通常附着在关节上,而将两个电极置于同一侧可大大缓解这一问题。受DNA双螺旋结构中氢键的启发,双螺旋电极设计常见于基于纤维的电池和超级电容器中,并通过同轴湿法纺丝及进一步处理应用于纤维柔性传感器。双螺旋传感器具有高强度,能保持稳定运行,在其工作范围内,分别在超过300%的应变下制备,应变片系数(GF)为0.9、39.5和349。这种独特的单面电极结构还实现了诸如水流传感等应用。该传感器进一步集成到一个能够进行实时五通道手指运动检测的智能手套中,并使用基于卷积神经网络(CNN)的机器学习算法在识别六种常见手势时达到了98.8%的准确率。本研究提供了一种通过内部封装双螺旋结构优化基于纤维的柔性传感器中电极分布的新方法,为柔性传感领域做出了重大贡献。