Paper-Based Resistive Networks for Scalable Skin-Like Sensing
Paper-Based Resistive Networks for Scalable Skin-Like Sensing
复制标题
用于可扩展类皮肤传感的纸基电阻网络
DOI：
10.1002/aelm.201800131
复制
发表时间：
2018
影响因子：
6.2
通讯作者：
Mazzeo, Aaron D.
中科院分区：
文献类型：
--
作者：
Zou, Xiyue;Chen, Chuyang;Liang, Tongfen;Xie, Jingjin;Gillette-Henao, Eda-Nicole;Oh, Jihoon;Tumalle, Jonathan;Mazzeo, Aaron D.
这项工作提出了一种独特的方法来设计、制造和表征基于纸张的皮肤状传感器，这种传感器使用图案电阻网络进行无源、可扩展的传感，并减少了互连数量。当被水接触或浸湿时，电阻网络中的传感器会检测到电阻抗的显著变化。在一张金属化纸上制造这些电阻网络和传感器，减少了阵列传感器不同输入/输出的数量。对于人-电极相互作用，基于电路的模型指导电阻网络的设计/材料处理和工作频率的选择-通常范围在80 kHz和1 MHz之间。例如，只有两条连接线（即激励线和地线）的纸质触摸板可以作为31个按钮的键盘。这些电阻网络还能够在干燥环境中与分配的水滴进行空间映射，并在弯曲时运行。报告的结果标志着电阻网络电容传感技术的进步，减少了所需互连的数量，同时为柔性电子设备提供了对人电极相互作用的科学理解和建模。未来具有图案电阻网络的类皮肤传感器有潜力为可扩展的人机界面、可穿戴设备和液体泄漏检测器做出贡献。
This work presents a unique approach to the design, fabrication, and characterization of paper‐based, skin‐like sensors that use patterned resistive networks for passive, scalable sensing with a reduced number of interconnects. When touched or wetted with water, the sensors in the resistive networks detect significant changes in electrical impedance. Fabricating these resistive networks and sensors in a single sheet of metallized paper reduces the number of distinct inputs/outputs to the arrayed sensors. For human–electrode interactions, circuit‐based models guide the design/material processing of the resistive networks and selection of operating frequencies—typically ranging between 80 kHz and 1 MHz. As an example, a paper‐based touchpad with only two connecting wires (i.e., excitation and ground) functions as a 31‐button keypad. These resistive networks are also capable of spatially mapping contact with dispensed droplets of water in a dry environment and operating when bent. The reported results mark a technological advance in capacitive sensing with resistive networks to reduce the number of required interconnects while providing scientific understanding and modeling of human–electrode interactions for flexible electronic devices. Future skin‐like sensors with patterned resistive networks have the potential to contribute to scalable forms of human–machine interfaces, wearable devices, and liquid‐leak detectors.