Trace heavy metals in water represent a significant health hazard due to their bioaccumulation in the human body. Current detection methods for these contaminants typically require sophisticated laboratory equipment and procedures, limiting their applicability in the field. In this paper, we show that Terahertz (THz), a prospective frequency band for wireless communication technologies, exhibits distinctive pine-like structures (termed “THz-Pine”) in the three-dimensional space when sensing liquids. Moreover, our measurements reveal that this structure possesses superior capabilities for characterizing trace physical information compared to conventional 2D spectrum methods. Leveraging above findings, we introduce Terahertz Trace Sensing,i.e.,TraSen, a contactless method for detecting and quantifying trace heavy metals in water.TraSenoperates by emitting Terahertz signals vertically above a water sample, capturing the reflected signals, and analyzing the resultingTHz-Pineto determine the type and concentration of trace heavy metals present. To quantify trace heavy metals concentrations, we develop an embedded Physics-Informed 3D point cloud deep learning model. Our extensive dataset, comprising 102,500 Terahertz signal samples across 40 trace heavy metals, demonstrates thatTraSencan effectively identify trace heavy metals with 95.4% accuracy in type identification and 94.3% accuracy in concentration quantification. Specifically, our preliminary findings indicate that the detection limit of Terahertz signals for heavy metals in water is approximately 0.1$\mu g/dL$.

Taste perception plays an essential role in promoting human health and maintaining nutritional balance. Current taste perception techniques usually require expensive equipment and delicate storage conditions, which restrict their use primarily to laboratory settings. In this paper, we show that terahertz (THz) signals generate unique fingerprint spectra when interacting with different taste molecules in aqueous solutions. Building on this finding, we propose Molecular-level Taste Sensing (MoleSen), a contact-free gustatory sensing method aimed at achieving wireless human-like perception. Specifically, MoleSen emits terahertz signals towards the aqueous solution, captures the reflected signals, and then determines the type and concentration of the tastes by analyzing the unique fingerprint spectra of the reflected signals influenced by the taste molecules. In MoleSen, we design a bio-inspired deep learning model (DTB, Digital Taste Bud) to identify the subtle taste features diluted by water molecules. Additionally, we incorporate domain adaptive learning to address the issue of feature distribution shifts when multiple tastes are mixed. Through extensive experiments involving over 247,000 samples, we demonstrate that MoleSen can accurately differentiate the five basic tastes—sour, bitter, salty, sweet, and umami—with an accuracy of 98.5% for taste type determination and 96.9% for concentration detection. Moreover, MoleSen outperforms the human's taste sensitivity and achieves a highly accurate perception even for mixed tastes.