Translational dynamics of individual microbubbles with millisecond scale ultrasound pulses
Bibliographic record
Abstract
It is established that radiation forces can be used to transport ultrasound contrast agents, particularly for molecular imaging applications. However, the ability to model and control this process in the context of therapeutic ultrasound is limited by a paucity of data on the translational dynamics of encapsulated microbubbles under the influence of longer pulses. In this work, the translation of individual microbubbles, isolated with optical tweezers, was experimentally investigated over a range of diameters (1.8–8.8 μm, n = 187) and pressures (25, 50, 100, 150, and 200 kPa) with millisecond pulses. Data were compared with theoretical predictions of the translational dynamics, assessing the role of shell and history force effects. A pronounced feature of the displacement curves was an effective threshold size, below which there was only minimal translation. At higher pressures (≥150 kPa) a noticeable structure emerged where multiple local maxima occurred as a function of bubble size. The ability to accurately capture these salient features depended on the encapsulation model employed. In low Reynolds number conditions (i.e., low pressures, or high pressures, off-resonance) the inclusion of history force more accurately fit the data. After pulse cessation, bubbles exhibited substantial displacements consistent with the influence of history effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".