Self-Powered Biomimetic Tactile Sensing with Broad Linear Range via Synchronous Mechano-Electrical Regulation

Tiantong Wang, Yewei Song, Yunbiao Zhao*, Yinhao Wang, Yang Xu, Qi Wu, Qining Wang, Yan Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Self-powered flexible pressure sensors based on the mechano-potentiometric conversion mechanism are gaining research interest for robotic tactile sensing and wearables. In contrast to the dynamic mechanical stimuli responsiveness of piezoelectric and triboelectric sensors, they offer the advantage of transducing static mechanical stimuli into sustained voltage outputs. However, sensors employing this mechanism suffer from limitations such as a narrow pressure response range and poor response linearity. Herein, a mechano-potentiometric transducer based on an internal-cavity/microhemisphere-structured solid electrolyte is presented, which achieves a broad linear sensing range in pressure sensing through the synergistic mechano-electrical regulation upon applied pressure. The fabricated sensor exhibits broad (0–350 kPa) and linear (1.66 mV kPa–1, R2= 0.995) pressure sensing performance and shows fast response (42.3 ms)/recovery (62.6 ms) and excellent repeatability (>10,000 cycles). To evaluate its spatial pressure sensing capability, the sensor is scaled into a matrix for pressure distribution mapping. Furthermore, integration of the matrix onto a robotic gripper enables the softness recognition of eight distinct silicone samples with a high accuracy of 95.31%, and facilitates feedback control of the gripper’s grasping force based on fruit softness. This work provides a strategy for high-linearity, broad-range self-powered potentiometric sensors for robotic tactile sensing.

Original languageEnglish
Pages (from-to)51499-51510
Number of pages12
JournalACS Applied Materials and Interfaces
Volume17
Issue number36
DOIs
Publication statusPublished - 10 Sept 2025
Externally publishedYes

Keywords

  • linearity
  • mechano-potentiometric transduction
  • robotic tactile sensing
  • self-powered sensor
  • softness perception

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