Microfluidic Generation of Monodisperse Nanobubbles by Selective Gas Dissolution
Bibliographic record
Abstract
Nanotechnology currently enables the fabrication of uniform solid nanoparticles and liquid nano-emulsions, but not uniform gaseous nanobubbles (NBs). In this article, for the first time, a method based on microfluidics that directly produces monodisperse NBs is reported. Specifically, a two-component gas mixture of water-soluble nitrogen and water-insoluble octafluoropropane as the gas phase are used in a microfluidic bubble generator. First, monodisperse microbubbles (MBs) with a classical microfluidic flow-focusing junction is generated, then the MBs shrink down to ≈100 nm diameter, due to the dissolution of the water-soluble components in the gas mixture. The degree of shrinkage is controlled by tuning the ratio of water-soluble to water-insoluble gas components. This technique maintains the monodispersity of the NBs, and enables precise control of the final NB size. It is found that the monodisperse NBs show better homogeneity than polydisperse NBs in in vitro ultrasound imaging experiments. Proof-of-concept in vivo kidney imaging is performed in live mice, demonstrating enhanced contrast using the monodisperse NBs. The NB monodispersity and imaging results make microfluidically generated NBs promising candidates as ultrasound contrast and molecular imaging agents.
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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) | 0.000 | 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".