Experimental and numerical investigation of backscattered signal strength from different concentrations of nanobubble and microbubble clusters
Bibliographic record
Abstract
Optimizing the clinical performance of Nanobubbles and Microbubbles (MBs and NBs) requires not only a good understanding of their individual bubble dynamics, but how bubble interactions change these dynamics. Here we report our experimental and simulation results showing the changes in backscattered signal strength from different concentrations of NBs or MBs. NBs and MBs are sorted by size to isolate two populations with mean diameters of 200 nm and 1 micron, respectively. Our results show that increasing number density of NBs or MBs is accompanied with an increase in the strength of their backscattered signals up to a certain concentration, above which the strength of the signal decreases. This decrease is not associated with the medium attenuation which was not accounted for. Our numerical simulations that include inter-bubble interactions are in general agreement with our experimental results. Moreover, acquired data show the that concentration at which there is signal saturation for NBs is orders of magnitude higher than that of MBs (1e9 vs 1e6 bubbles per mL), suggesting a potential explanation behind the very strong backscatter observed in NB clusters compared to MBs. Numerical simulations show that increasing the number density of bubbles decreases the individual signal strength of each individual bubbles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".