Ning Li,Linzi Zuo,Fanchao Xu,Xiaolei Qu,Heyun Fu,Huiting Liu
Cerium sulfide (Ce 2 S 3 ) pigment is an environmentally friendly novel inorganic red-colored metal pigment, representing a potential alternative to traditional heavy metal pigments. In this study, we report concurrent re-precipitation and photo-dissolution of cerium sulfide pigment in a sunlit aquatic phase. Commercial cerium sulfide pigment transforms into its re-precipitation product, CeCO 3 OH, along with the generation of H 2 S gas in its re-precipitation process. Simultaneously, cerium sulfide pigment photo-dissolution results in a significant release of Ce ions. After 4 hours of simulated sunlight irradiation, the particle size of cerium sulfide pigment significantly reduces, and 10.4% of elemental Ce is released into the solution as free Ce ions. The key photo-dissolution mechanism is the oxidation reaction between photogenerated holes and the crystal lattice of the pigment. Cerium sulfide pigment and its re-precipitation product, CeCO 3 OH, can be excited by specific wavelengths in the solar spectrum, mediating the pigment photo-dissolution. UV light shielding can inhibit the cerium sulfide pigment photo-dissolution. Superoxide and hydroxyl radicals are generated during the photoreaction process, in which the hydroxyl radical may oxidize the photo-induced released Ce(III) to Ce(IV) and the re-precipitation-generated H 2 S to SO 4 2- . Our research indicates that commercial cerium sulfide pigment undergoes a series of transformation processes in aquatic environments, potentially posing certain environmental risks.
Yang Jianlian,Feng Weiwei,Cai Zongqi,Wang Huanqing,Liang Xinghui
Fluorescent dissolved organic matter (FDOM) — particularly tryptophan (Trp), tyrosine (Tyr), and humic acid (HA) — serves as a crucial indicator in environmental monitoring. This study introduced a novel quantitative analysis approach for analyzing three-dimensional excitation–emission matrix spectra (3D-EEMs) of FDOM using convolutional neural networks (CNNs). The performance of the CNN model was evaluated and compared with the self-weighting alternating trilinear decomposition (SWATLD) algorithm. Results revealed that the proposed model significantly outperforms the SWATLD algorithm. Specifically, the CNN model achieved R 2 , RMSE, and MAPE values of 0.964, 0.047, and 14.950%, respectively, while for the SWATLD algorithm, these values were 0.944, 0.062, and 17.439%. Augmentation of the original spectral dataset did not yield a substantial improvement in the performance of the SWATLD algorithm, but it significantly enhanced the prediction ability of the CNN model. This enhancement was evident in the improved R 2 , RMSE, and MAPE values of 0.989, 0.030, and 12.837%, highlighting the critical role of data augmentation in boosting the performance of the CNN model, especially when dealing with a limited dataset. Application of the CNN model to water samples from Laizhou Bay yielded satisfactory results, enabling a simple and rapid analysis of FDOM in seawater. Therefore, an accurate and convenient analytical model was developed based on EEMs and CNNs, which can swiftly determine the concentration of FDOM in the environment and provide valuable references for environmental monitoring and early warning.
危险品化学品经营许可证（带存储）