Ultralow-background SERS substrates for reliable identification of organic pollutants and degradation intermediates

Chemical methods for preparing SERS substrates have the advantages of low cost and high productivity, but the strong background signals from the substrate greatly limit their applications in characterization and identification of organic compounds. Herein, we developed a one-step synthesis method to prepare silver nanoparticle substrates with ultralow SERS background using anionic ligands as stabilizing agents and applied the SERS substrate for the reliable and reproducible identification of typical organic pollutants and corresponding degradation intermediates. The synthesis method shows excellent universality to different reducing agents cooperating with different anionic ligands (Cl-, Br-, I-, SCN-). As model applications, the machine learning algorithm can realize the precise prediction of six organophosphorus pesticides and eight sulfonamide antibiotics with 100% accuracy based on SERS training data. More importantly, the ultralow-background SERS substrate enables one to detect and identify the time-dependent degradation intermediates of organophosphorus pesticides by combining them with density functional theory (DFT) calculations. All the results indicate that the ultralowbackground SERS substrate will greatly push the development of SERS characterization applications.

Automated summary

In this study, a one-step synthesis method was developed to prepare silver nanoparticle substrates with ultralow SERS background using anionic ligands as stabilizing agents. The SERS substrate was successfully applied for the identification of organic pollutants and the detection of degradation intermediates. Machine learning algorithms and density functional theory calculations further improved the accuracy and reliability of identification.