Temperature-insensitive and wide-range linear tactile electronic skins for reliable shape and texture recognition

Abstract

Electronic skins that emulate the tactile functionality of the human skin are crucial for robotic applications. The sensitivity and pressure-sensing range of current humanoid tactile sensors have made significant advancements. However, the temperature susceptibility of conductive materials and the rapid saturation of conductive contact sites in soft polymeric materials pose challenges for these sensors, including environmental interference and a narrow linear sensing range. These issues often lead to inconsistencies between the sensing signal and contact behavior, which subsequently reduce the accuracy and reliability. Herein, we proposed a flexible piezoresistive pressure sensor with a minimized response to temperature variation and an extended linear sensing range. The sensor utilized a novel hybrid conductive material created by combining materials with opposite temperature coefficients, resulting in a zero-temperature resistance coefficient. Accordingly, a reliable operation ranging from 20–70 °C with temperature-variation-induced fluctuation-free performance was achieved. Additionally, the sensor exhibited a biomimetic polymer microstructure with multilevel cone-dome structural features, resulting in an ultra-wide linear pressure-sensing range of 0–200 kPa. Based on the simple and scalable fabrication process, a high-density sensor array (16 × 16) was produced to accurately outline the spatial pressure distributions, even under external temperature interference, and successfully discern the texture of the contact object.

Graphical abstract: Temperature-insensitive and wide-range linear tactile electronic skins for reliable shape and texture recognition

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Article information

Article type
Paper
Submitted
14 Feb 2025
Accepted
07 May 2025
First published
08 May 2025

Nanoscale, 2025, Advance Article

Temperature-insensitive and wide-range linear tactile electronic skins for reliable shape and texture recognition

L. Xue, L. Yuan, J. Zhou, J. Dai, X. Zhang, H. Hu, H. Liu and T. Zhao, Nanoscale, 2025, Advance Article , DOI: 10.1039/D5NR00660K

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