Abnormal iron ions levels may lead to some diseases and serious environmental pollution. Herein, optical and visual detection strategies of Fe 3+ in water based on co-doped carbon dots (CDs) were established in the present study. Firstly, a one-pot synthetic strategy for the preparation of the N, S, B co-doped CDs with a home microwave oven was developed. Secondly, the optical properties , chemical structures, and morphology of CDs were further characterized by fluorescence spectroscopy, Uv–vis absorption spectroscopy , Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscope. Finally, the results indicated that the fluorescence of the co-doped CDs was quenched by ferric ions via the static mechanism and the aggregation of CDs, accompanied by the increased red color. The multi-mode sensing strategies of Fe 3+ with fluorescence photometer, UV–visible spectrophotometer, portable colorimeter and smartphone had the advantages of good selectivity, excellent stability and high sensitivity. Fluorophotometry based on co-doped CDs was a powerful probe platform for measuring lower concentrations of Fe 3+ due to its higher sensitivity, better linear relationship, lower limit of detection (0.27 μM) and limit of quantitation (0.91 μM). In addition, the visual detection methods with a portable colorimeter and smartphone had been proven to be very suitable for rapid and simple sensing of higher concentrations of Fe 3+ . Moreover, the co-doped CDs utilized for Fe 3+ probes in tap water and boiler water obtained satisfactory results. Consequently, the efficient, versatile optical and visual multi-mode sensing platform could be extended to apply such a visual analysis of ferric ions in the biological, chemical and other fields.
Hongyue Li,Yang Wang,Jiaxin Zhang,Xuepeng Li,Jinxiang Wang,Shumin Yi,Wenhui Zhu,Yongxia Xu,Jianrong Li
Electronic nose (E-nose), electronic tongue (E-tongue) and colorimeter combined with data fusion strategy and different machine learning algorithms (artificial neural network, ANN; extreme gradient boosting, XGBoost; random forest regression, RFR; support vector regression, SVR) were applied to quantitatively assess and predict the freshness of horse mackerel ( Trachurus japonicus ) during the 90-day frozen storage. The results showed that the fusion data of the E-nose, E-tongue and colorimeter could contain more information (with a total variance contribution rate of 94.734 %) than that of the independent one. ANN, RFR and XGBoost showed good performance in predicting biochemical indexes with the R P 2 (the square correlation coefficient of the Test set) ≥ 0.929, 0.936, 0.888, respectively, while SVR models showed a bad performance ( R P 2 ≤ 0.835). In addition, among the established quantitative models, the RFR model had the best prediction effect on K value (freshness index) with R p 2 of 0.936, ANN model had the highest fitting degree in predicting carbonyl content (protein oxidation degree) with R p 2 of 0.978, XGBoost model had the best performance in predicting the TBA value (lipid oxidation degree) with R p 2 of 0.994, RFR model was the best strategy for predicting Ca 2+ -ATPase activity (protein denaturation degree) with R p 2 of 0.969. The results demonstrated that the freshness of frozen fish can be effectively evaluated and predicted by the combination of electronic sensor fusion signals.
危险品化学品经营许可证（带存储）