MetaDetection: wireless intelligent detection of multiliquids by using space-time-coding metasurface

The increasing demand for public safety has created an urgent need for high-performance technologies capable of detecting hazardous liquids with high accuracy, efficiency, and cost-effectiveness. Conventional liquid detection methods often fall short in addressing these requirements due to limitations in precision, operational complexity, and scalability. This study introduces a wireless intelligent system for the detection of suspicious liquids, leveraging advancements in programmable metasurface and software defined radio technologies. By employing a spatiotemporal coding metasurface to transmit orthogonal frequency division multiplexing (OFDM) Wi-Fi signals, the system efficiently manipulates the spectral harmonic distribution of OFDM subcarriers, thereby creating multiple independent detection channels. Artificial intelligence (AI)-based classification algorithms are integrated to extract liquid-specific features from the channel state information (CSI), enabling precise identification of liquid properties. The proposed system exhibits robust immunity to ambient interference, such as interfering signals, temperature fluctuations, and humidity, while achieving near-ideal accuracy in the simultaneous detection and classification of multiple liquids. This innovative approach provides a cost-effective, scalable, and intelligent solution for hazardous substance detection, with transformative potential for security screening and public safety applications.