The effect of boundary proximity on the fundamental and subharmonic emissions from individual microbubbles at higher frequencies
Bibliographic record
Abstract
It is recognized that the proximity of a boundary can influence the dynamic behaviour of acoustically stimulated microbubbles. In a biomedical ultrasound context, this is relevant to molecular imaging with targeted microbubbles, and when microbubbles are near vessel walls or contained within microvessels. Theoretical models have recently been developed to examine these effects, but experimental work has been more limited and primarily focused on the assessment of resonant frequency effects rather than its impact on nonlinear behaviour, which is perhaps more relevant to imaging applications. Understanding this behaviour is important to improving microbubble detection and for the quantitative interpretation of contrast images. With the use of an optical trap, this study experimentally investigates the effect of boundary proximity (0 to 200 µm) and boundary stiffness (Opticell and agarose) on fundamental and subharmonic emissions from individual Definity and MicroMarker bubbles at 11 MHz. The scattered pressure dependence on proximity from an Opticell boundary resulted in an oscillatory dependence, while from an agarose boundary resulted in a decreasing fundamental and an increasing subharmonic response with increasing distance from the boundary. These experimental findings are not entirely captured by basic analytical simulations, likely suggesting that more complex numerical models may be required.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".