All Resistive Pressure-Temperature Bimodal Sensing E-Skin for Object Classification

Area of Science:

• Materials Science
• Robotics
• Sensors

Background:

• Electronic skin (E-skin) offers significant potential for object classification in intelligent robots.
• Challenges exist in E-skin development due to multiple output signal types.

Purpose of the Study:

• To develop a hierarchical pressure-temperature bimodal sensing E-skin with all resistive output signals.
• To enable accurate object classification for intelligent robots and human-machine interfaces.

Main Methods:

• Fabrication of an E-skin using laser-induced graphene/silicone rubber (LIG/SR) for pressure sensing and NiO for temperature sensing.
• Utilizing highly conductive LIG as both pressure-sensitive material and interdigital electrodes.
• Simultaneous pressure and temperature perception with negligible crosstalk using only electrical resistance.

Main Results:

• Achieved high sensitivity for pressure perception (-34.15 kPa⁻¹).
• Obtained a high temperature coefficient of resistance (-3.84%°C⁻¹) in the 24-40 °C range.
• Demonstrated over 92% accuracy in object classification using a smart glove based on the E-skin with deep learning.

Conclusions:

• The developed bimodal sensing E-skin effectively integrates pressure and temperature sensing.
• This technology shows promise for advanced human-machine interfaces, intelligent robots, and smart prosthetics.
• The all-resistive output simplifies signal processing for multimodal sensing applications.