Machine Learning assisted Paper-Based Fluorescent Sensor Array with Metal-Doped Multicolor Carbon Quantum Dots for Identification and Inactivation of Bacteria

Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that meets the demands of both bacterial identification and real-time inactivation is urgently needed. This work introduces a novel paper-based fluorescence sensor array. It incorporates three types of metal-doped multicolor carbon quantum dots (CQDs)—Ag-CQDs, Cu-CQDs, and Zn-CQDs—deposited directly onto filter paper using an inkjet printer. The presence of various bacteria reduces the fluorescence of the CQDs. These changes are detected with a smartphone and analyzed through a machine learning algorithm, facilitating accurate identification of the bacteria. The results reveal the platform's capacity to recognize five kinds of bacteria across a wide concentration range from 1.0 × 10 3 to 1.0 × 10 7 CFU/mL, underlining its versatility in detecting different levels of bacteria. The platform also exhibits antibacterial capacity, reaching a better antibacterial effect within 30 min. This platform is not only cost-effective and highly integrated but also provides a concept for designing multifunctional biosensors for on-site bacterial detection and antibacterial applications across various fields.