Extraction evolutionary features of continuous light-induced plasmonic nanobubbles by a dynamic spectral regression model

Abstract

A comprehensive understanding of the nature of light-induced plasmonic nanobubbles (PNBs) generated around noble metal nanoparticles is essential for optimizing PNB-based applications, such as light-driven microfluidic control. To investigate the overall evolution pattern of plasmonic nanobubbles (PNBs) under continuous light illumination, a computational model based on the least-squares method is established. Meanwhile, the variations in nanofluid transmittance and vaporization mass under continuous light illumination are measured by an immersion fiber and an electronic balance, respectively. The effects of nanoparticle concentration on the nanofluid transmittance and vaporization mass are analyzed. Considering the variations in nanofluid concentration, the overall evolution pattern of PNBs is preliminarily analyzed by monitoring the real-time dynamic change in nanofluid transmittance. The experimental results indicate that the nanoparticle concentration has an important effect on the nanofluid vaporization process. During the illumination stage, an increased nanofluid concentration intensifies the synergistic effect of water vaporization and PNB growth on transmittance reduction. The change in nanofluid transmittance caused by PNB scattering greatly exceeds that caused by increasing nanoparticle concentration. The nanofluid concentration gradually increases and peaks at the end of the cooling stage, and the rate of change of the nanofluid concentration is proportional to the nanofluid concentration. The numerical calculations show that the average size and the range of the PNB size distribution continuously increase with increasing irradiation time, and decrease during the cooling stage. A higher nanofluid concentration corresponds to a wider range of PNB size distribution.