Arsenic Sensor Using Fluorescent GQD Nanocomposites: Real-Time Monitoring with AI and IoT Capabilities

Arsenic contamination of drinking water is a major global problem for portable, low-cost, and extremely sensitive detection technologies. In this study, a multi-disciplinary approach was adopted to produce a fluorescent graphene quantum dot (GQD) nanocomposite through synthesis, combined with artificial intelligence (AI) and Internet of Things (IoT) technologies for real-time detection of arsenic. GQDs were manufactured through a one-pot hydrothermal method and functionalized using polyethylene glycol (PEG) and chitosan, the latter of which improved colloidal stability, fluorescence intensity, and analyte binding. The sensor operates by quenching fluorescence in the presence of arsenic ions, which was quantitatively interpreted using a linear regression prediction model. The IoT-enhanced system has a microcontroller-based user-friendly dashboard for real-time data access. The sensor obtained a detection limit of 0.4637 ppm, response time of 2.21 ns and reproducibility of 1.308% (RSD = 1.308%). Thus, this research frontier combined functional nanomaterials, AI-based analytics, and wireless sensor networks, and in doing so, constructed a new platform for scalable, real-world environmental diagnostics. Graphical Abstract