Surfactant-Enhanced Anti-Swelling Hydrogel Flexible Sensor for Machine Learning-Assisted Underwater Gesture...

Area of Science:

• Materials Science
• Polymer Chemistry
• Sensor Technology

Background:

• Hydrogels are promising for flexible sensors but struggle with swelling and stability.
• Developing hydrogel sensors with robust anti-swelling properties is crucial for reliable performance.

Purpose of the Study:

• To introduce a novel design for hydrogel sensors with enhanced anti-swelling properties and stable performance.
• To fabricate hydrogels using surfactant-assisted hydrophobic associative strategies.

Main Methods:

• Free-radical copolymerization utilizing cetyltrimethylammonium bromide as a surfactant.
• Fabrication of hydrogels with controlled hydrophobic associations.
• Mechanical testing, swelling ratio measurements, and fatigue resistance evaluation.
• Integration with machine learning for underwater gesture recognition and motion monitoring.

Main Results:

• Successfully fabricated hydrogels exhibiting outstanding mechanical properties (breaking strain: 3594.5%) and excellent anti-swelling capabilities (swelling ratio: -2.3%).
• Demonstrated sustained fatigue resistance over 1000 underwater loading-unloading cycles.
• Achieved high-accuracy (98.3%) underwater gesture recognition and motion monitoring through machine learning integration.

Conclusions:

• Surfactant-assisted hydrophobic associative hydrogels offer a promising solution for stable and high-performance flexible underwater sensors.
• The developed hydrogel sensors show significant potential for applications in smart wearables, health monitoring, and underwater exploration.