Ultrasensitive detection and distinction of pollutants based on SERS assisted by machine learning algorithms

SERS as a promising sensing technique still faces challenges in the precise identification of trace-amount molecules due to the limitation of sensitivity and cleanliness of SERS substrate. Here, we report a precise and ultrasensitive identification of multiple pollutants via 3D clean cascade-enhanced nanosensor assisted by machine learning algorithms. This SERS substrate could achieve cascading electromagnetic energy with a remarkable enhancement factor as high as 8.35 × 109, which is attributed to the combination of micro-level polystyrene sphere (PS) porous array and nano-level Au-Ag clusters of this substrate. Benefitting from high cleanliness and ultra-sensitivity, multiple hazardous pollutants with similar geometry and Raman peaks at ultra-low concentration were successfully distinguished assisted by principal component analysis (PCA). As a result, this efficient and clean SERS substrate together with artificial intelligence could promote the application of SERS technology in the accurate identification of trace contaminants. Data will be made available on request.