Solid–Liquid Triboelectric Nanogenerators as Physicochemical Encoders for Intelligent Liquid Recognition

ABSTRACT

Liquid classification and identification are pivotal for diverse sectors ranging from environmental monitoring to medical diagnostics. However, traditional methods are often constrained by bulky instrumentation and complex procedures. Solid–liquid triboelectric nanogenerators (S‐L TENGs) have emerged as a transformative, self‐powered solution for on‐site detection. This review establishes a unified framework for S‐L TENGs, conceptualizing the solid–liquid interface as a dynamic physicochemical encoder, which is an evolution from the established solid–solid interaction baseline. Herein, we analyze how this interface encodes intrinsic liquid properties (e.g., ion concentration and dielectric constant) and kinetic features into distinct electrical fingerprints across various working modes, including droplet impact, continuous flow, and immersion. Furthermore, the role of artificial intelligence as a powerful decoder is highlighted, demonstrating how deep learning algorithms extract precise information regarding physical parameters, chemical composition, and microscopic content from complex triboelectric signals. Finally, regarding the remaining challenges for practical employment, including signal stability and cross‐sensitivity, we advocate for future research on multimodal sensing and advanced algorithmic strategies. This review aims to provide the theoretical foundation and technological roadmap necessary to advance S‐L TENGs from fundamental research to robust, intelligent liquid identification applications.