On amplification of radial oscillations of microbubbles due to bubble-bubble interaction in polydisperse microbubble clusters under ultrasound excitation
Bibliographic record
Abstract
Optimizing the performance of microbubbles (MBs) in applications not only requires a good understanding of the dynamics of individual MBs, but also knowledge of their interactions with each other within polydisperse clusters. However, the behavior of acoustically driven MB clusters is not well understood. Most studies have been limited to cases of a few interacting MBs for a limited range of acoustical parameters. We use a numerical method to simulate the dynamics of clusters of (20–50 MBs) randomly distributed MBs. MBs with the size distribution of 2–8 micron were sonicated with pressures between 1 and 250 kPa, frequencies between 0.5 and 10 MHz and concentrations between 2e3 and 2.5e6 MBs/mL. Bifurcation structures of radial oscillations of each MB within a cluster were studied. Results show that in a polydisperse MB cluster, larger MBs (even in small numbers) can amplify the radial oscillations of smaller MBs. Amplification occurs if the resonance peaks of larger MBs coincide with the resonance peaks of smaller MBs. Additionally, there is an optimum number density at which amplification is maximized. Results suggest that the presence of bigger MBs may enhance the superharmonic oscillations of the smaller MBs which can be applied in superhamonic ultrasound imaging using MBs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".