Hui Huang,Meini Li,Mingwei Hao,Liangli (Lucy) Yu,Yongxin Li
A selective and sensitive colorimetric strategy for sulfide analysis was developed using GMP-Cu nanozymes with a laccase-like activity. This research discovered for the first time that sulfide could significantly enhance the catalytic activity of the GMP-Cu nanozymes by about 3.5 folds. The enhanced laccase activities duo to two reasons. First, Cu 2+ in GMP-Cu nanozymes was reduced to Cu + . The other reason was the formation of Cu–S bond which was beneficial to accelerate the electron transfer rate to improve catalytic activity. Therefore, this method showed an excellent selectivity for sulfide. And it had a linear relationship in the sulfide concentration range of 0–220 μmol/L with a detection limit of 0.67 μmol/L. Furthermore, the proposed method was successfully applied to examine sulfide in the food systems. This new method may be used in sulfide detection to improve food quality and safety.
Xiaoyu Yang,Lulu Lei,Donghui Song,Yue Sun,Meng Yang,Zhen Sang,Jianan Zhou,Hui Huang,Yongxin Li
To realize the efficient differential sensing of phenolic pollutants in sewage, a novel sensing strategy was successfully developed based on a nanozyme (GMP-Cu) with polyphenol oxidase activity. Phenolic pollutants can be oxidized using GMP-Cu, and the oxidation products reacts subsequently with 4-aminoantipyrine to produce a quinone-imine compound. The absorption spectra of final quinone-imine products that resulted from different phenolic pollutants showed obvious differences, which were due to the interaction difference between GMP-Cu and phenolic pollutants, as well as the different molecular structures of the quinone-imine products from different phenolic pollutants. Based on the difference in the absorption spectra, a novel differential sensing strategy was developed. A genetic algorithm was used to select the characteristic wavelengths at different enzymatic reaction times. Hierarchical cluster analysis and PLS-DA algorithms were utilized for the discriminant sensing of seven representative phenolic pollutants, including hydroquinone, resorcinol, catechol, resorcinol, phenol, p -chlorophenol, and 2,4-dichlorophenol. A scientific wavelength selection algorithm and a recognition algorithm resulted in the successful identification of phenolic pollutants in sewage with a discriminant accuracy of 100%, and differentiation of the phenolic pollutants regardless of their concentration. These results indicated that a sensing strategy can be used as an effective tool for the efficient identification and differentiation of phenolic pollutants in sewage.
危险品化学品经营许可证（带存储）