High-Performance Iontronic Hydrogel Acoustic Sensor for Low-Frequency Underwater Sound Detection and Intelligent Recognition.

The detection and identification of low-frequency underwater sounds remain challenging due to inefficient acoustic-mechanical-electrical coupling, which limits the conversion of low-frequency weak pressure fluctuations into stable electrical signals in conventional hydrophones. Here, we develop a capacitive hydrogel acoustic sensor (CHAS) that enhances low-frequency transduction through a micro-pyramid iontronic structure governed by dynamic electric double layer (EDL) effects. By combining the EDL-based sensing mechanism with a phase-sensitive lock-in amplification circuit providing a 40 dB gain, the system preserves weak acoustic information within the 20 to 1,000 Hz range. The sensor achieves an average sensitivity of -158.86 dB and reliably detects diverse low-frequency underwater acoustic events, including human speech, water impacts, and vessel-radiated noise. A neural network trained on the ShipsEar database yields 96.3% accuracy and, especially, maintains 89.1% accuracy when directly applied to CHAS-acquired signals without retraining, demonstrating that the iontronic sensing mechanism preserves physically meaningful acoustic features and enables reliable cross-domain intelligent analysis. This work establishes an integrated sensing framework that links iontronic device physics, circuit-level signal conditioning, and data-driven acoustic interpretation, providing a practical pathway toward intelligent low-frequency underwater acoustic monitoring and target recognition.