Characterization of Microbubbles and Nanobubbles Using Ultra-High Frequency Ultrasound
Bibliographic record
Abstract
Microbubbles (MBs) and nanobubbles (NBs) are extensively used in various biomedical applications, and therefore must be accurately characterized. It has been shown that ultra-high frequency ultrasound (UHFUS) backscatter is sensitive to the physical characteristics of micron-sized particles. Because UHFUS transducers are highly focused, particles must be placed within a small focal zone. Therefore, a microfluidic-based Acoustic Flow Cytometer (AFC) was developed and used to focus flow the bubbles for rapid interrogation to characterize them individually. Using the AFC, the scattering of MBs and NBs with three different shells to 375 MHz ultrasound was measured from which two parameters were extracted: slope and mid-band fit (MBF). It was found that changing the shell does not significantly affect the slope, but it affects the MBF. The bubble size affected the slope and MBF. These results indicate that UHFUS can be used for the rapid individual characterization of MBs and NBs.
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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 | 0.001 | 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".