AI-Driven Battery-Free Wireless Sensing of Hazardous Liquid Spills via a Frequency-Selective Surface in a Monostatic Antenna Configuration

The detection of spills is paramount in safeguarding safety and mitigating environmental risks in sensitive environments, including laboratories and industrial facilities. Here, the novel artificial intelligence (AI)-driven, battery-free, and wireless sensing methodology are presented for detecting liquid spills using a monostatic wireless sensing system. The system consists of a frequency-selective surface (FSS) serving as the sensor, in conjunction with a horn antenna that functions as the readout equipment. The sensing structure features a $25 \times 25$ resonator array with a 7-mm periodicity, operating at a resonant frequency of 7.2 GHz. The system analyzes the renormalized $S_{11}$ response to quantify variations caused by the presence of liquid on the FSS, demonstrating a high sensitivity to isopropyl alcohol (IPA) spills. Using machine learning techniques, the framework generates $512 \times 512$ pixel masks delineating the affected area on the FSS, achieving an ${F}1$ -score exceeding 0.85 for spill localization. This sensing methodology shows potential for integration with augmented reality (AR) systems, enabling enhanced situational awareness and real-time spill localization. Future work aims to enhance the system’s capability to detect more hazardous materials and accurately classify them.