Paper-Based Resistive Networks for Scalable Skin-Like Sensing

Xiyue Zou, Chuyang Chen, Tongfen Liang, Jingjin Xie, Eda Nicole Gillette-Henao, Jihoon Oh, Jonathan Tumalle, Aaron D. Mazzeo

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Article number1800131
JournalAdvanced Electronic Materials
Issue number8
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials


  • human–machine interfaces
  • leak detectors
  • paper-based electronics
  • skin-like sensors
  • touch sensors


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