A Highly Sensitive, Ultrawide-Range Temperature-Pressure Dual-Mode Sensing Platform for Battery Health and Marine...

Area of Science:

• Materials Science
• Nanotechnology
• Sensor Technology

Background:

• Developing dual-mode sensors with high sensitivity, wide detection range, and stable temperature response is crucial for extreme environment monitoring.
• Existing sensors often struggle with sensitivity, stability, and interference in harsh conditions.

Purpose of the Study:

• To develop a novel temperature-pressure dual-mode sensor with synergistic enhancement for ultrahigh sensitivity and stability.
• To investigate the sensor's performance in extreme conditions and its application in real-world scenarios.
• To utilize deep learning for precise prediction of battery thermal behavior using sensor data.

Main Methods:

• Fabrication of a dual-mode sensor using hexagonal boron nitride (h-BN) and few-layer graphene (FLG) for pressure sensing.
• Integration of a platinum serpentine electrode temperature sensor fabricated via magnetron sputtering.
• Testing the sensor's mechanical stability, sensitivity, linearity, and temperature response under various conditions.
• Application of the sensor for monitoring lithium-ion battery expansion and deep-sea waves.
• Development of a deep learning model (Informer architecture) for battery thermal behavior prediction.

Main Results:

• Achieved record-high pressure sensitivity of 4771.2 kPa^-1 with a detection limit up to 10 MPa.
• Demonstrated excellent mechanical stability, withstanding 11200 and 6000 cycles at 1 and 6 MPa, respectively.
• The temperature sensor exhibited highly linear (R² = 0.9993) and stable response from -20 to 140 °C with negligible pressure interference.
• Successfully captured high-quality sensing data from lithium-ion batteries and deep-sea waves.
• The deep learning model achieved high-precision short-time temperature prediction (MAE = 4.2 °C, range accuracy = 97.36%).

Conclusions:

• The synergistic h-BN/FLG mechanism significantly enhances sensor sensitivity and stability for extreme environments.
• The developed dual-mode sensor platform is scalable and suitable for high-performance multimodal sensing.
• The integration of deep learning models with sensor data enables accurate prediction of critical parameters like battery thermal behavior.