An enhanced, rational model to study acoustic vaporization dynamics of a bubble encapsulated within a nonlinearly elastic shell
Bibliographic record
Abstract
Acoustic droplet vaporization (ADV) is a new approach to generate vapor bubbles that have potentially broad medical applications. ADV-generated bubbles can be used as contrast agents in acoustic imaging, as drug carriers to deliver drugs to particular targets, and also in embolotherapy, thermal therapy, and histotripsy. However, despite much progress, ADV dynamics have still not been well understood and properly modeled. In this paper, we present a theoretical study of ultrasound-induced evaporation of a droplet encapsulated by a shell. The main emphasis of this theoretical study is on a proper description of the supercritical state occurring after bubble collapse. For this purpose, an isentropic equation of state for a van der Waals gas is used to describe the bubble behavior in the supercritical state. Sensitivity of the vaporization process is investigated for different acoustic and geometrical parameters and mechanical properties of the shell. Results show that the value of the minimum pressure required for direct vaporization (without any oscillatory behavior) depends on shell elasticity and initial size of the droplet, especially at high frequencies (greater than 2[MHz]). Moreover, it has been shown that applying an acoustic wave with proper phase such that thermal equilibrium of the bubble temperature with the surrounding liquid is attained, results in direct vaporization at lower acoustic pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".