Zhang Yiqun, Song Yangyang, Lin Sijian, Zhang Xuyi, Wang Zhuqing, Wu Xiaodong
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
School of Engineering Science, Royal Institute of Technology, Stockholm 11428, Sweden.
ACS Nano. 2025 Feb 11;19(5):5503-5514. doi: 10.1021/acsnano.4c14157. Epub 2025 Jan 28.
Natural skin receptors use ions as signal carriers, while most of the developed artificial tactile sensors utilize electrons as information carriers. To imitate the biological ionic sensing behavior, here, we present a kind of biomimetic, ionic, and fully passive mechanotransduction mechanism leveraging mechanical modulation of interfacial ionic p-n junction (IPNJ) through microchannels. Sensors based on this mechanism do not rely on an external power supply and can encode external tactile stimuli into highly analogous signal outputs to those of natural skin receptors, in terms of both signal type (i.e., ionic potential difference) and signal intensity (≈120 mV). More importantly, the instant interfacial IPNJ regulation characteristic endows the sensors with superior performance when compared to the state-of-the-art piezoionic sensors, including a low detection limit of 0.01 N, fast response/recovery speeds (16 ms/16 ms), ultralow power consumption (pW level), excellent reproducibility (over 100,000 cycles), and good capabilities to resolve both static and dynamic mechanical stimulations. As demonstrations, machine-learning-assisted high accuracy (over 99%) surface texture recognition and object classification are successfully demonstrated with the sensors integrated on robotic hands. This work enriches the family of mechanical sensing mechanisms and provides a path to mimicking natural tactile sensory systems for smart skins, artificial prostheses, and intelligent robots.
天然皮肤感受器使用离子作为信号载体,而大多数已开发的人工触觉传感器则利用电子作为信息载体。为了模仿生物离子传感行为,在此,我们提出了一种仿生、离子且完全被动的机械转导机制,该机制通过微通道利用界面离子 p-n 结(IPNJ)的机械调制。基于这种机制的传感器不依赖外部电源,并且在信号类型(即离子电位差)和信号强度(≈120 mV)方面都可以将外部触觉刺激编码为与天然皮肤感受器高度相似的信号输出。更重要的是,与最先进的压离子传感器相比,即时界面 IPNJ 调节特性赋予了传感器卓越的性能,包括 0.01 N 的低检测限、快速的响应/恢复速度(16 ms/16 ms)、超低功耗(皮瓦级)、出色的再现性(超过 100,000 次循环)以及分辨静态和动态机械刺激的良好能力。作为演示,集成在机器人手上的传感器成功实现了机器学习辅助的高精度(超过 99%)表面纹理识别和物体分类。这项工作丰富了机械传感机制的种类,并为模仿用于智能皮肤、人工假肢和智能机器人的天然触觉传感系统提供了一条途径。