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

In the era of the Internet of Things (IoT), droplet-based nanogenerators offer a promising solution for self-powered environmental monitoring. However, conventional displacement-current-driven devices are constrained by fluid-solid interface coupling, which results in low carrier transport efficiency and limits both output performance and sensing sensitivity. To address the need for acid rain monitoring, this study introduces a novel total-current nanogenerator (TCNG) optimized through curvature engineering, enabling an intelligent real-time acid rain sensing platform. By optimizing fluid-solid coupling dynamics and regulating the interfacial electric field gradient, the TCNG enhances spatiotemporal separation and pump accumulation of charge carriers at the solid-liquid interface, thereby improving charge transfer efficiency in the droplet-based nanogenerator platform. The platform exhibits high sensitivity and rapid response for acid rain monitoring, and it incorporates a ResNet18-1D deep learning algorithm to analyze the time-frequency features of the current signals, achieving 99.86% accuracy in identifying the pH values of acid rain. Furthermore, the TCNG delivers a peak voltage of 4500 V, sufficient to power electronic devices. By integrating multiphysics coupling design with artificial intelligence, this work provides a novel strategy for advancing the next-generation IoT-based environmental monitoring platform.