Programmable magnetic-induced functional gradient hydrogel for NIR-driven underwater soft robots.

Area of Science:

• Materials Science
• Robotics
• Biomimetics

Background:

• Soft underwater robots offer enhanced adaptability but suffer from interfacial delamination in multilayer designs.
• Limited drive modes and interface layering compromise structural integrity and reconfigurability.

Purpose of the Study:

• To develop a novel programmable functional gradient hydrogel for improved soft underwater robotics.
• To address interfacial failure issues and enhance reconfigurability in multilayer soft robots.

Main Methods:

• Fabrication of a gradient hydrogel incorporating magnetic Fe3O4/polydopamine hybrid nanoparticles (FPHPs) into a temperature-sensitive network.
• Utilizing 808 nm near-infrared (NIR) irradiation to induce localized heating and create a temperature gradient for actuation.
• Developing underwater smart optical sensors and jellyfish-inspired robots leveraging hydrogel properties and programmable magnetization.

Main Results:

• The gradient hydrogel demonstrated a seamless structure, avoiding interfacial failure.
• Achieved 180° bending deformation within 5 seconds, maintaining performance over 50 cycles.
• Successfully developed a hydrogel-based underwater optical sensor for tunable RGB-LED displays and a locomotion-controlled jellyfish robot.

Conclusions:

• The developed gradient hydrogel offers a stable and reconfigurable solution for soft underwater robotics.
• Pioneered hydrogel-based underwater optical sensors, enhancing deep-sea communication capabilities.
• Advanced intelligent deep-sea exploration through improved robotic design and sensing technologies.