Nonlinear acoustic characterization of the shell and size engineered microbubbles and nanobubbles
Bibliographic record
Abstract
Optimization of the bubble performance requires size isolation and accurate shell characterization using models that are not limited by linear assumptions. MBs and NBs with 2 different shell compositions (crosslinked (C) and non-crosslinked (NC)) were made in-house. NC shell is made with 4 different lipids including DBPC, DPPA,DPPE and DSPE-PEG2000 . C shell bubbles have additional ingredients that produce a UV polymerized crosslinked shell. Using the method of multiple differential centrifugations, two distinct size populations were separated with mean diameters of 2.9 μm for NC-MB and 3.3 μm for C-MB. The attenuation and sound speed of the diluted solutions were measured through transmission and reception method using one pair of PVDF transducers with center frequencies of 10 MHz and 100% BW at acoustic pressures of approximately 5 to 40 kPa. Our nonlinear model accounting for large amplitude MB oscillations was used to fit the measured attenuation and sound speed data at each pressure. As the pressure increased from 5 kPa to ≈ 50kPa, resonance frequency (fr) of the NC-MBs with a mean diameter (MD) of 2.9 μm decreased from 9.1 to 5.9 MHz and frof the C-MBs with (MD) of 3.3 μm decreased from 8 to 4.8 MHz. NC-NB solutions did not display any attenuation peak in the frequency range of 2-10 MHz, additionally; the measured attenuation was 5-10 times smaller than the MBs with the same shell composition. Fitting of the shell parameters suggests that crosslinking the shell results in ≈ 37% increase in stiffness and 50 % decrease in shell viscosity. The lower attenuation of the NBs even at very high concentrations may explain the enhancement in NB contrast ultrasound.
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 | 0.001 |
| 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 | 0.000 |
| 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".