Vaporization of perfluorocarbon droplets using optical irradiation
Bibliographic record
Abstract
Micron-sized liquid perfluorocarbon (PFC) droplets are currently being investigated as activatable agents for medical imaging and cancer therapy. After injection into the bloodstream, superheated PFC droplets can be vaporized to a gas phase for ultrasound imaging, or for cancer therapy via targeted drug delivery and vessel occlusion. Droplet vaporization has been previously demonstrated using acoustic methods. We propose using laser irradiation as a means to induce PFC droplet vaporization using a method we term optical droplet vaporization (ODV). In order to facilitate ODV of PFC droplets which have negligible absorption in the infrared spectrum, optical absorbing nanoparticles were incorporated into the droplet. In this study, micron-sized PFC droplets loaded with silica-coated lead sulfide (PbS) nanoparticles were evaluated using a 1064 nm laser and ultra-high frequency photoacoustic ultrasound (at 200 and 375 MHz). The photoacoustic response was proportional to nanoparticle loading and successful optical droplet vaporization of individual PFC droplets was confirmed using photoacoustic, acoustic, and optical measurements. A minimum laser fluence of 1.4 J/cm(2) was required to vaporize the droplets. The vaporization of PFC droplets via laser irradiation can lead to the activation of PFC agents in tissues previously not accessible using standard ultrasound-based techniques.
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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".