Surface-Engineered Graphene surface-enhanced Raman scattering Platform with Machine-learning Enabled Classification of Mixed Analytes
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Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical moleculesowing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platformsthrough surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/grapheneSERS platforms showed exceptional sensitivity (10-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing MLis proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm,the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from theSERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-foldcross-validation (k = 5), showing 99% prediction accuracy
Abstract
Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical moleculesowing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platformsthrough surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/grapheneSERS platforms showed exceptional sensitivity (10-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing MLis proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm,the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from theSERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-foldcross-validation (k = 5), showing 99% prediction accuracy
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