Intelligent Real-Time Acid Rain Sensing Platform Enabled by Curvature-Engineered Droplet-Based DC Nanogenerator with...

Area of Science:

• Materials Science
• Environmental Science
• Electrical Engineering

Background:

• Droplet-based nanogenerators are crucial for self-powered environmental monitoring in the Internet of Things (IoT).
• Conventional devices face limitations in carrier transport efficiency due to fluid-solid interface coupling, impacting performance and sensitivity.
• Acid rain monitoring requires sensitive and efficient sensing platforms.

Purpose of the Study:

• To develop a novel total-current nanogenerator (TCNG) for intelligent real-time acid rain sensing.
• To enhance charge carrier separation and transfer efficiency at the solid-liquid interface.
• To integrate artificial intelligence for accurate acid rain pH identification.

Main Methods:

• Curvature engineering was employed to optimize the TCNG.
• Fluid-solid coupling dynamics and interfacial electric field gradients were regulated.
• A ResNet18-1D deep learning algorithm was utilized for signal analysis.

Main Results:

• The TCNG platform demonstrated high sensitivity and rapid response for acid rain detection.
• The deep learning algorithm achieved 99.86% accuracy in identifying acid rain pH values.
• The TCNG generated a peak voltage of 4500 V, capable of powering electronic devices.

Conclusions:

• The developed TCNG offers a promising solution for next-generation IoT-based environmental monitoring.
• Optimized fluid-solid coupling and interfacial electric field management enhance nanogenerator performance.
• The integration of multiphysics design with AI provides a novel strategy for environmental sensing.