Stretchable Polymorphic Electrochromic Textile Electronics for Deep Learning-Assisted Self-Adaptive Camouflage

Area of Science:

• Materials Science
• Wearable Electronics
• Biomimetic Systems

Background:

• Electrochromic (EC) textile electronics offer seamless integration for advanced human-machine interaction.
• Challenges include achieving simultaneous performance, mechanical reliability, environmental stability, and hierarchical control in EC textiles.

Purpose of the Study:

• To develop a stretchable, all-solid-state polymorphic EC textile electronics system.
• To enable deep learning-assisted self-adaptive camouflage for dynamic environments.
• To overcome limitations in current EC textile performance and stability.

Main Methods:

• Development of liquid-free poly(urethane-urea) elastomeric ionic conductors with a supramolecular-covalent dual cross-linking network.
• Integration of a lightweight deep learning model (MobileNetV2) for environmental information processing and camouflage strategy.
• Implementation of a smart glove as a proof-of-concept for self-adaptive camouflage.

Main Results:

• Achieved fast response (≈3 s), high color contrast (≈58.1%), and long-term operation (>12,000 s).
• Demonstrated excellent coloration stability under large deformation and freezing conditions.
• Elastomeric ionic conductors exhibited high ionic conductivity, stretchability, softness, adhesion, self-healing, and wide temperature adaptability.

Conclusions:

• The developed EC textile electronics system provides a paradigm shift for next-generation wearable devices.
• The biomimetic self-adaptive camouflage system showcases potential for advanced display, imaging, and sensing applications.
• The novel elastomeric ionic conductors are key to achieving robust and adaptable EC textile performance.