Deep learning-assisted smartphone-based ratio fluorescence for "on-off-on" sensing of Hg2+ and thiram

Rapid, accurate, and low-cost detection of heavy metals and pesticide residues are crucial to environmental pollution control, but it is still a challenge. In this work, the fluorescence sensing platform system of the iron based metal-organic framework (Fe-MIL-88NH(2)) and gold nanoclusters (Au NCs) based on a smartphone portable device coupled with a deep learning-driven applet of WeChat to measure Hg2+ and thiram were proposed. Meanwhile, Hg2+ quenched the fluorescence mechanism of Au NCs was explored by density functional theory (DFT). Interestingly, thiram can restore the fluorescence intensity of Au NCs. As a result, the ratiometric fluorescence sensor can accurately detect Hg2+ in the "on-off " model and detect thiram in the "off-on " model, which possessed high sensitivity and low detection limits are 7 nM and 0.083 mu M, respectively. Meanwhile, the visual changes of fluorescence color from red to purple and blue for determination of Hg2+ and the color returned to red when detecting thiram. Based on the RGB or HSV values reflected in the images, the linear range individually quantified of Hg2+ and thiram in the broad linear range of 0.002-30 mu M and 0.083-49.910 mu M, respectively, which are equivalent to or better than that attained from fluorescence spectrometer. In addition, the developed sensor in combination with deep learning can accurately predict Hg2+ and thiram concentration levels in actual samples. Besides, our strategy provides a powerful sensing platform in the analysis of water samples and crops and suggests great application potential in environmental monitoring.