Size and pressure dependent activity of lipid coated bubbles and finite element simulation of the propgation of focused ultrasound through bubbly media
Bibliographic record
Abstract
Use of nanodroplets as alternative to bubbles is limited to high pressure applications due to their high vaporization threshold (>1 MPa). For low pressure applications (e.g <800 kPa) and stable bubble activity, size isolated micron or sub-micron bubbles may be used to tackle the pre-focal bubble activity and attenuation. Numerical simulations of the Marmottant model were ran for bubble sizes of 0.45, 1, 2, and 4 μm in response to 1 MHz ultrasound with pressures between 10 and 700 kPa. All agents were volume matched to 20 μl/kg of Definity considering inter-bubble interactions. The pressure-dependent attenuation and the total acoustic power (TAP) were calculated for each population. Finite element simulations (FEMS) were run by taking account the pressure dependent attenuation and sound speed. Using size isolated bubbles an experimental passive cavitation case study was performed for the same exposure conditions and sizes. Numerical results show that TAP and attenuation of the bubbles are size dependent. Bigger bubbles have stronger responses at lower pressures. However, smaller agents exhibit a size-dependent pressure threshold behavior (PT) above which their attenuation and TAP grow stronger than their bigger counterparts in qualitative agreement with experiments. FEMS show that the PT of oscillations may be used to reduce pre-focal attenuation for ultrasound propagation with minimal loss.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".