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Gradient Nanostructures and Machine Learning Synergy for Robust Quantitative Surface‐Enhanced Raman Scattering

Year: 2025

Type: article

Abstract: Surface‐Enhanced Raman Scattering (SERS) holds significant promise for trace‐level molecular detection but faces challenges in achieving reliable quantitative analysis due to signal variability caused by non‐uniform “hot spots” and external factors. To address these limitations, a novel SERS platform based on gradient nanostructures is developed using shadow sphere lithography, enabling the acquisition of diverse spectral features from a single analyte concentration under identical conditions. The gradient design minimizes fabrication variability and enhances spectral diversity, while the machine learning (ML) model trained on the multi‐spectral dataset significantly outperformed traditional single‐spectrum approaches, with the test Mean Squared Error (MSE) reduced by 84.8% and the coefficient of determination ( R 2 ) improved by 61.2%. This strategy captures subtle spectral variations, improving the precision, robustness, and reproducibility of SERS‐based quantification, paving the way for its reliable application in real‐world scenarios.

Source: Advanced Science

DOI: 10.1002/advs.202501793