Sub-30-Seconds ultrafast intelligent detection of glutathione using machine learning-guided handheld sensing platform based on mercury ion-mediated ratiometric fluorescence carbon dots

Rapid, accurate, and in-field detection of glutathione (GSH) is indispensable for food safety, medical diagnosis, and environmental monitoring. However, most conventional approaches typically require the use of expensive laboratory-based techniques and trained personnel. Herein, combined with a machine learning algorithm, a portable handheld sensor based on mercury ion (Hg2+)-mediated ratiometric fluorescence carbon dots (D-CDs) is first constructed for ultrafast detection of GSH. The intelligent system utilizes a smartphone with a self-programming applet as a real-time result-processing terminal, which greatly improves the detection accuracy and efficiency. Interestingly, orange emission of D-CDs gradually decreases with increasing Hg2+ concentrations, while green emission shows an obvious enhancement, resulting in a distinct color shift from orange to green. Subsequent addition of GSH restores the fluorescence of D-CDs@Hg2+ accompanied by a noticeable color transition from green to orange. More importantly, the proposed method realizes on-site monitoring of GSH with the detection limit of 1.84 μM. The application of machine learning technology on automated handheld sensors shows its potential for sample-to-answer detection, providing a valuable and efficient tool for rapid on-site chemical analysis and intelligent point-of-care diagnosis.