Enhancement of the Au/ZnO-NA Plasmonic SERS Signal Using Principal Component Analysis as a Machine Learning Approach

Abstract

In this work, we modeled a novel approach to enhance surface-enhanced Raman scattering (SERS) signals using principal component analysis (PCA) as a machine learning approach. Zinc oxide nanoarrays (ZnO-NAs) were synthesized using a hydrothermal method followed by zinc oxide nucleation on ITO glass substrates via an oxidation furnace at 500°C. The surface morphology was improved by short rapid thermal annealing (S-RTA) after deposition of a gold layer via a thermal evaporator to avoid chemical contamination of the sensing surface, which is a suitable plasmonic platform for the generation of "hot spots" for SERS enhancement with fewer defects. The proposed Au/ZnO-NA SERS sensor exhibited an enhancement factor (EF) of 1.15x10 7 via the R6G Raman probe and excellent uniformity over the entire surface. The PCA algorithm was used to extract useful features and information from the SERS signal. The algorithm was implemented with MATLAB software (R2019a) by the multivariable analytical tool to find an enhanced signal (~3 times higher) with high uniformity, which has great potential and is applicable to a wide range of probe molecules suitable in medical, safety, and environmental applications.

Research on Grid Monitoring Algorithm of Educational Resources Based on Economic Structure Transformation

“…en, the given signal matrix is X((x i ), i � 1, 2, 3, ..., n), where x i is the row vector of X [12]. Its mean vector is…”

Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics

“…The combination of machine learning analysis tools and multipotonic effects has enormous potential in data interpretation. Gupta et al used Principal Component Analysis (PCA) as a learning machine to improve SERS signals [80]. The use of PCA in this research was able to increase the SERS signal to be up to three times higher.…”

Principal Component Analysis of Surface-Enhanced Raman Scattering Spectra Revealing Isomer-Dependent Electron Transport in Spiropyran Molecular Junctions: Implications for Nanoscale Molecular Electronics

“…Thereafter, the SERS spectra were analyzed using PCA to determine the relationship between the molecular structures of the isomers and conductance. PCA is a multivariate statistical analysis technique that can identify the major directions of variation in a given data set. − Intercorrelated quantitative dependent variables can be extracted from complex data by PCs. , Through the PCA of the SERS spectra, it was found that the third principal component (PC3) suitably reflected the isomerization behavior (Figure b,c), whereas PC1 and PC2 reflected the spectral background and the dominant MC isomer, respectively (Section 8 in the Supporting Information). The prominent peak at 1610 cm –1 in the loading vector of PC3, corresponding to the indole C–C stretching vibration of SP, is shown in Figure b, indicating that the discrimination between the SP- and MC-SMJs can be achieved via PC3.…”