Transistor-like iontronics device based on MXene/Bi 2D heterojunction for human-machine intelligent interaction

Area of Science:

• Materials Science
• Robotics
• Biomedical Engineering

Background:

• Human-machine intelligent interaction (HMII) is advancing healthcare and virtual reality.
• Current HMII systems often require complex multi-sensor setups for precise robotic control.
• Challenges remain in achieving sensitive and accurate human-machine interfaces.

Purpose of the Study:

• To develop a novel, flexible, all-solid-state, self-powered iontronics pressure sensor.
• To construct a deep-learning-assisted single-device HMII system.
• To demonstrate precise control of robotic hands and decoding of hand gestures using the developed sensor.

Main Methods:

• Fabrication of a transistor-like iontronics sensor using a MXene/Bi 2D heterojunction.
• Integration of the sensor into a deep-learning-assisted HMII system.
• Testing the sensor's performance in monitoring skin deformations, decoding gestures, and controlling robotic hands.

Main Results:

• The iontronics sensor demonstrated a fast response time (66.59 ms) and long lifetime (50,000 cycles).
• The HMII system achieved 95.83% accuracy in recognizing sophisticated hand gestures.
• The system enabled precise robotic hand control with tactile perception feedback, monitoring nerve-triggered skin deformations.

Conclusions:

• The novel MXene/Bi 2D heterojunction iontronics sensor offers a promising solution for advanced HMII.
• This single-device approach simplifies HMII systems by reducing sensor redundancy.
• The technology significantly enhances human-machine perceptual interaction for applications in healthcare and robotics.