Large-Scale Analysis of Intra-and Inter-Particle Distributions of SERS Enhancement Factor

Surface-enhanced Raman scattering (SERS) can be a powerful tool for live-cell molecular imaging, yet its quantitative reliability is often limited by signal fluctuations arising from stochastic hotspot formation. We performed a large-scale statistical analysis of the SERS enhancement factor using DNA-gold nanoparticle (DNA-AuNP) crystals as a particle-like SERS substrates. Using a home-built confocal Raman microscope, we conducted hyperspectral SERS imaging of 190 individual particles using Rhodamine 6G as a probe molecule. Multivariate curve resolution alternating least squares (MCR-ALS), applied to masked particle regions, successfully decomposed the intra-particle signals into three distinct spectral components: vertical and horizontal molecular adsorption geometries, and a metallic lattice vibration component. Our statistical analysis revealed that while absolute SERS intensities showed significant variation, particularly in the vertical orientation (CV=2.264), the horizontal orientation exhibited lower variability (CV=0.767). Furthermore, the intensity ratio between these components (C ₁ /C ₂ ) yielded a CV of 1.907, providing a quantitative benchmark for spectral profile consistency. Furthermore, a spatial correlation between the metallic lattice signal and the specific vertical orientation was observed. These results demonstrate that DNA-AuNP crystals provide a uniform surface chemistry and a consistent distribution of enhancement sites. We propose that monitoring these low-frequency metallic lattice vibrations during the fabrication process could provide a real-time feedback mechanism to further improve the reproducibility of SERS sensors. This study establishes a robust framework for the evaluation of SERS-active substrates, paving the way for quantitative molecular sensing in complex biological environments.