Size reduction of cosolvent-infused microbubbles to form acoustically responsive monodisperse perfluorocarbon nanodroplets
Bibliographic record
Abstract
Perfluorocarbon (PFC) nanodroplet agents are exciting new biomaterials that can be remotely vapourized by ultrasound or light to change into micron-scale gas bubbles in situ. After PFC nanodroplet vapourization, the micron-scale gas bubble can interact strongly with ultrasound radiation, such that the bubbles can be used for cancer imaging and therapy. For these phase-change agents to be useful, however, PFC nanodroplets must be produced in the range of 100 to 400 nm in diameter with high size control and monodispersity, restrictions that remain a challenge. Here, we address this challenge by taking advantage of the size control offered by microfluidics, in combination with the size reduction provided by cosolvent-infused PFC bubbles through both condensation and cosolvent dissolution. In this approach, PFC bubbles with a high percentage of cosolvent (in this study, diethyl ether, DEE) are produced using microfluidics at a temperature above the boiling point. After synthesis, these bubbles become much smaller through both condensation of the gas into liquid droplets and from dissolution of the DEE into the continuous phase. This approach demonstrates that monodisperse, cosolvent-incorporated PFC bubbles can directly form monodisperse PFC nanodroplets a factor of 24 times smaller than the precursor bubbles. We also demonstrate that these nanoscale droplets can be converted to echogenic microbubbles after exposure to ultrasound, showing that these PFC nanodroplets are viable for the in situ production of ultrasound contrast agents. We show that this system can overcome the minimum droplet size limit of standard microfluidics, and is a powerful new tool for generating monodisperse, PFC phase-change ultrasound contrast agents for treating and imaging cancer.
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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".