journal article·2025-03Open access

A self-damping triboelectric tactile patch for self-powered wearable electronics

Guoli Du, Jiamin Zhao, Yuzheng Shao, Tao Liu, Bin Luo, Song Zhang, Mingchao Chi, Chenchen Cai, Zhaomeng Liu, Shuangfei Wang, Shuangxi Nie

eScience· Volume 5· Issue 2

Abstract

Wearable tactile sensing systems with bionic designs holds significant promise for environmental interactions and human–machine communication. Triboelectric sensing technology plays a vital role in acquiring and quantifying tactile signals. Conventional elastic sensing materials, however, lack damping performance and are easily damaged by vibrations, leading to sensor failure. To address this challenge, our study proposes a highly damping triboelectric gel based on a hydrogen bonding assisted microphase separation strategy. In microphase separation, the soft phase provides the viscoelasticity needed for the gel, while the hard phase dissipates shock energy. This energy dissipation mechanism enables the gel to achieve excellent damping performance (tan ​δ ​= ​0.68 ​at 1Hz), skin-like softness (Young’s modulus of 130 ​kPa), and stretchability (> 900 ​%). The resulting self-damping tactile patch effectively absorbs and dissipates external vibrations, ensuring a stable and reliable wearable tactile sensing device. This work provides new insights into the application of triboelectric gels in wearable electronics. • A self-damping triboelectric tactile patch for wearable electronics has been developed based on a skin-like design. • The triboelectric gel used in the patch has a bionic energy dissipation mechanism with a tan ​δ of 0.68 (at 1 ​Hz). • The output performance and response time of the highly damping triboelectric tactile patch are 159 V and 66 ms, respectively.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Dielectric materials and actuators · Conducting polymers and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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