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

Area of Science:

• Nanotechnology
• Spectroscopy
• Machine Learning

Background:

• Surface-Enhanced Raman Scattering (SERS) offers sensitive trace-level molecular detection.
• Quantitative SERS analysis is hindered by signal variability from "hot spots" and external influences.

Purpose of the Study:

• To develop a novel SERS platform mitigating signal variability for reliable quantification.
• To enhance spectral diversity and robustness in SERS measurements.

Main Methods:

• Fabrication of a SERS platform utilizing gradient nanostructures via shadow sphere lithography.
• Acquisition of diverse spectral features from single analyte concentrations.
• Training a machine learning model on multi-spectral SERS data.

Main Results:

• The gradient nanostructure design minimized fabrication variability.
• Machine learning models trained on multi-spectral data showed an 84.8% reduction in Mean Squared Error (MSE) and a 61.2% improvement in R² compared to single-spectrum methods.
• Improved precision, robustness, and reproducibility in SERS quantification.

Conclusions:

• The developed SERS platform with gradient nanostructures and ML analysis significantly improves quantitative detection.
• This approach addresses key limitations in SERS, enabling reliable real-world applications.