SiQDs and [Ru(bpy)(2)(phen-NH(2))](2+) based ratiometric fluorescence probe for point-of-care testing of 6PPD-quinone with 3D-printing portable devices.

BACKGROUND: N-phenyl-N'-(1,3-dimethylbutyl)-p-phenylenediamine-quinone (6PPD-Q), an emerging pollutant, is a highly toxic chemical derived from tires, which have possible adverse effects on human health via the food chain. Despite the widespread occurrence of 6PPD-Q in the environment, methods for its detection remain relatively scarce. Consequently, the development of accurate methods for the quick, highly responsive, and specific analysis of 6PPD-Q is critical. RESULTS: A deep learning and smartphone integrated ratiometric fluorescence sensor was proposed for the point-of-care testing (POCT) of 6PPD-Q. Silicon quantum dots (SiQDs) and [Ru(bpy)(phen-NH)] were employed for preparing the SiQDs@[Ru(bpy)(phen-NH)] complex with two emissions at 430 nm and 610 nm respectively, which corresponds to blue and red fluorescence colors. As increased concentration of 6PPD-Q, the fluorescence of SiQDs@[Ru(bpy)(phen-NH)] complex at 430 nm was gradually enhanced, while the fluorescence at 610 nm kept unchanged. When the concentration of 6PPD-Q was ranging from 0.0066 to 2 μg mL and 2-7 μg mL, the fluorescence intensity ratio I/I and the concentration of 6PPD-Q gave an outstanding linear relationship with the limit of detection (LOD) of 2 ng mL. Meanwhile, the fluorescence color change of SiQDs@[Ru(bpy)(phen-NH)] underwent a gradual transition from red to magenta and finally to purple, which could be recognized by the naked eye. The YOLOv5 algorithm was utilized to locate the fluorescence images and acquired the corresponding RGB values for deep learning to enhance the accuracy. SIGNIFICANCE: This study established an analytical platform for the analysis of 6PPD-Q with the merits of low cost, convenience, visualization and real-time detection, which is expected to open a new avenue for the detection of emerging pollutants.