Industrial wastewater containing large amounts of organic pollutants is a severe threat to the environment and human health. Thus, the rapid detection and removal of these pollutants from wastewater are essential to protect public health and the ecological environment. In this study, a multifunctional and reusable surface-enhanced Raman scattering (SERS) substrate by growing Ag nanoparticles (NPs) on ZnO nanorods (NRs) was produced for detecting and degrading Rhodamine B (RhB) dye. The ZnO/Ag substrate exhibited excellent sensitivity, and the limit of detection (LOD) for RhB was as low as 10⁻¹¹ M. Furthermore, the SERS substrate could efficiently degrade RhB, with a degradation efficiency of nearly 100% within 150 min. Moreover, it retained good SERS activity after multiple repeated uses. The interaction between Ag NPs, ZnO, and RhB was further investigated, and the mechanism of SERS and photocatalysis was proposed. The as-prepared ZnO/Ag composite structure could be highly applicable as a multifunctional SERS substrate for the rapid detection and photocatalytic degradation of trace amounts of organic pollutants in water.

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect aging states. The samples are categorized into different aging stages based on the polymerization degree of the insulating paper. Principal component analysis (PCA), multi-dimensional scale change method (MDS), and isometric mapping algorithm (Isomap) are employed to extract features from the Raman spectra. Subsequently, the XGBoost strong classifier, optimized through Bayesian hyperparameter optimization (BO-XGBoost), is utilized to distinguish between four and ten states among 175 groups of samples after feature extraction. The subsequent classification results of the three feature-extraction methods are compared. The results indicate that Isoamp, which pertains to the manifold dimension transformation, achieves the highest average discriminative accuracy after feature extraction. The discriminative accuracies for aging states four and ten are 97.0% and 95.1% respectively, demonstrating that Raman spectroscopy manifold dimension transformation enhances the distinctiveness between samples of different aging states in the feature-extraction process. The diagnostic model constructed with Isomap and BO-XGBoost enables accurate discrimination of the aging states of oil–paper insulation.