Microbubbles Loaded with Nanoparticles: A Route to Multiple Imaging Modalities
Bibliographic record
Abstract
We report a single-step approach to producing small and stable bubbles functionalized with nanoparticles. The strategy includes the following events occurring in sequence: (i) a microfluidic generation of bubbles from a mixture of CO(2) and a minute amount of gases with low solubility in water, in an aqueous solution of a protein, a polysaccharide, and anionic nanoparticles; (ii) rapid dissolution of CO(2) leading to the shrinkage of bubbles and an increase in acidity of the medium in the vicinity of the bubbles; and (iii) co-deposition of the biopolymers and nanoparticles at the bubble-liquid interface. The proposed approach yielded microbubbles with a narrow size distribution, long-term stability, and multiple functions originating from the attachment of metal oxide, metal, or semiconductor nanoparticles onto the bubble surface. We show the potential applications of these bubbles in ultrasound and magnetic resonance imaging.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".