@article{Ji2025, 
author = {Junjie Ji and Zhenlong Huang and Hongwei Xie and Yizhuo Wang and Tao Chen and Longpeng Yang and Yan Jiang and Jing Liu and Dong Cheng and Tailong Wu and Taisong Pan and Min Gao and Binbin Jiang and Yuan Lin},
title = {Stretchable multimodal sensor arrays for hardness perception in bionic skin},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {12},
pages = {94907759},
keywords = {pressure sensor, strain sensor, electronic skin, hardness perception},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907759},
doi = {10.26599/NR.2025.94907759},
abstract = {The development of robotic bionic skin plays a crucial role in enhancing robots’ environmental sensing and interaction capabilities, relying heavily on stretchable multimodal tactile sensors. However, current tactile sensors suffer from significant limitations in integration density and spatial resolution, making them unsuitable for complex applications. To address these challenges, this study proposes a multimodal tactile sensing method based on the co-integration of pressure and strain sensors. By innovatively embedding strain sensors into the gaps of pressure sensor arrays, both sensor types are positioned within the same plane, enabling simultaneous high-resolution measurements of pressure and strain. Furthermore, when combined with pressure-strain bimodal sensing data, this system allows for accurate assessment of object hardness. The bionic skin fabricated using this method demonstrates an average pressure sensitivity of 0.008891 kPa−1 within the range of 1 to 70 kPa, a high sensitivity of around 15.2 for strains up to 70% and enables the accurate prediction of object hardness within a Young’s modulus range of around 0.0725 to 1.27821 MPa. Additionally, a high-density sensor array consisting of 100 pressure units and 18 strain units has been successfully developed. When coupled with machine learning algorithms, the array achieves 100% accuracy in detecting various types of fruits and assessing their ripeness, thus opening new possibilities for the advancement of tactile sensing systems in intelligent robotics.}
}