Statistical Strategy for Quantitative Evaluation of Plasmon-Enhanced Spectroscopy

The plasmonhot spothas drawn immense researchattention in the areas of surface-enhanced spectroscopy, ultrafast opticalswitching, and nanophotonics. However, as a typical complex system,fluctuations caused by randomness and disorder make it difficult evenimpossible to accurately describe and predict the local electricfieldenhancement behavior of hot spots, therefore seriously hindering it fromthe laboratory to move toward practical application. Based on theMonte Carlo algorithm, a three-dimensional multi-hot spot model wasconstructed to theoretically investigate the influence of multiple randomfactors on thefluctuations of electricfield intensity. We found thatfluctuations can be effectively suppressed once hot spot density exceedsa threshold value, which means that plasmon performance of complex nanostructures with randomness can be accurately predicted.These theoretical results are confirmed by our preliminary surface-enhanced Raman scattering experiments. Our work promotes thefundamental understanding of the interplay of disorder and local electricfieldfluctuations in surface plasmon resonance complexsystems and would provide a new way for plasmon-enhanced spectroscopy techniques to enable precise quantitative analyses.

Automated summary

This study constructs a three-dimensional multi-hot spot model using the Monte Carlo algorithm to investigate the influence of multiple random factors on the fluctuations of electric field intensity. It is found that the fluctuations can be effectively suppressed when the hot spot density exceeds a certain threshold value. These theoretical results are confirmed by preliminary surface-enhanced Raman scattering experiments. This study is of great significance for understanding the interplay between disorder and local electric field fluctuations in surface plasmon resonance complex systems and provides a new method for precise quantitative analysis in plasmon-enhanced spectroscopy techniques.