Interaction of ultrasonically driven bubble with a soft tissue-like boundary
Bibliographic record
Abstract
This study investigates the size-dependent dynamics of bubbles and their interaction with soft boundaries under various ultrasound (US) conditions. We found that bubble behavior is influenced by size, with smaller bubbles displaying reduced inertial motion in similar ultrasound environments. Detailed analyses of three bubble sizes (1.5 µm, 15 µm, and 150 µm) next to a soft 1 kPa boundary revealed distinct patterns in radial oscillation, bubble center displacement, and boundary deflection for different ultrasound frequencies (5 kHz - 4 MHz). The smallest bubble maintained a spherical shape, while the largest experienced significant shape changes, indicative of impending jet formation. Investigating interactions at various frequencies highlighted the collapse tendency of the larger bubbles, showcasing maximum radial amplitude, displacement, and bubble wall velocity around its natural frequency. The presence of a soft boundary minimally affected radial amplitude and velocity, while the bubble displacement was contingent on the soft boundary modulus. Furthermore, boundary responses demonstrated that softer boundaries experienced less stress during bubble oscillations, exhibiting sharper peaks at resonance frequencies for larger bubbles. These findings provide valuable insights into optimizing ultrasound conditions for a variety of applications, highlighting the influence of bubble size and boundary properties on outcomes.
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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) | 0.000 | 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".