Exploring the Acoustic and Dynamic Characteristics of Phase-Change Droplets

Acoustic droplet vaporization (ADV) provides the on-demand production of bubbles for use in ultrasound (US)-based diagnostic and therapeutic applications. The droplet-To-bubble transition process has been shown to involve localized internal gas nucleation, followed by a volume expansion of threefold to fivefold and inertial bubble oscillation, all of which take place within a few microseconds. Monitoring these ADV processes is important in gauging the mechanical effects of phase-change droplets in a biological environment, but this is difficult to achieve using regular optical observations. In this study, we utilized acoustic characterization [i.e., simultaneous passive cavitation detection (PCD) and active cavitation detection (ACD)] to investigate the acoustic signatures emitted from phase-change droplets ADV and determined their correlations with the physical behaviors observed using high-speed optical imaging. The experimental results showed that activation with three-cycle 5-MHz US pulse resulted in the droplets (diameter: 3.0-6.0\mu \text{m} ) overexpanding and undergoing damped oscillation before settling to bubbles with a final diameter. Meanwhile, a broadband shock wave was observed at the beginning of the PCD signal. The intense fluctuations of the ACD signal revealed that the shock wave arose from the inertial cavitation of nucleated small gas pockets in the droplets. It was particularly interesting that another shock-wave signal with a much lower acoustic frequency (< 2 MHz) was observed at about 5\mu \text{s} after the first half signal. This signal coincided with the reduction of the ACD signal amplitude that indicated the rebound of the transforming bubble. Since internal gas nucleation is a crucial process of ADV, the first half signal may indicate the occurrence of an ADV event, and the second half signal may further reveal the degrees of expansion and oscillation of the bubble. These acoustic signatures provide opportunities for monitoring ADV dynamics based on the detection of acoustic signals.