A Coral-Inspired Dual Modal Hydrogel Sensor with Deep Learning-Assisted Decoupling of Force-Thermal Stimuli

Area of Science:

• Flexible electronics
• Multimodal perception
• Hydrogel sensors

Background:

• Multimodal perception is crucial for flexible electronics but faces challenges with signal crosstalk from multiple stimuli.
• Stony corals exhibit integrated perception of thermal, force, and other environmental stimuli.

Purpose of the Study:

• To fabricate a multi-stimuli-responsive visualizable hydrogel sensor inspired by stony corals.
• To develop a deep learning framework for processing multimodal signals from the sensor.
• To achieve accurate recognition and decoupling of concurrent force and thermal stimuli.

Main Methods:

• Fabrication of a PCSC hydrogel sensor integrating thermochromic (SP) and force-induced resistance (CQDs) units within a PVA/CMCS dual network.
• Development of a deep learning framework fusing image (ResNet) and resistance (multilayer perceptron) signals.
• Real-time synchronous decoupling and identification of concurrently applied force and thermal stimuli.

Main Results:

• The PCSC hydrogel exhibits simultaneous thermochromic properties and sensitive force-induced resistance response (gauge factor = 4.14).
• Significant differences in response times for force (0.4 s) and thermal (7.1 s) stimuli were observed.
• High accuracy achieved in temperature (97.5%) and gesture (98.5%) recognition, with >95% accuracy for concurrent stimuli decoupling.

Conclusions:

• The developed hydrogel sensor and deep learning framework enable effective multimodal perception.
• This approach offers a new strategy for next-generation multimodal flexible perception technologies adaptable to complex environments.