Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Surface-Engineered Graphene surface-enhanced Raman scattering Platform with Machine-learning Enabled Classification of Mixed Analytes

  • Jae Hee Cho (Thin Film Materials Research Center, Korea Research Institute of Chemical Technology) ;
  • Garam Bae (Thin Film Materials Research Center, Korea Research Institute of Chemical Technology) ;
  • Ki-Seok An (Thin Film Materials Research Center, Korea Research Institute of Chemical Technology)
  • Received : 2024.05.02
  • Accepted : 2024.05.22
  • Published : 2024.05.31
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Abstract

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing 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 platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS 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 ML is 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 the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

Keywords

Acknowledgement

J. H. C., and G. B. contributed equally to this study. This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Program) funded by the Ministry of Trade, Industry, and Energy (20013138).

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