Optical droplet vaporization (ODV): Photoacoustic characterization of perfluorocarbon droplets
Bibliographic record
Abstract
Optical droplet vaporization (ODV) of nanoscale and micron-sized liquid perfluorocarbon (PFC) droplets via a 1064 nm laser is presented. The stability and laser fluence threshold were investigated for PFC compounds with varying boiling points. Using an external optical absorber to facilitate droplet vaporization, it was found that droplets with boiling points at 29°C and 56°C were consistently vaporized upon laser irradiation using a fluence of 0.7 J/cm2or greater, while those with higher boiling points did not, up to a maximum laser fluence of 3.8 J/cm2. Upon vaporization, the droplet rapidly expanded to approximately 10-20x the original diameter, then slowly and continuously expanded at a rate of up to 1 μm/s. Lead sulphide (PbS) nanoparticles were incorporated into perfluoropentane (PFP) droplets to facilitate vaporization. The fluence threshold to induce vaporization ranged from 0.8 to 1.6 J/cm2, the wide range likely due to variances of the PbS concentration within the droplets. Prior to vaporization, the photoacoustic spectral features of individual droplets 2-8 μm in diameter measured at 375 MHz agreed very well with the theoretical prediction using a liquid sphere model. In summary, the use of liquid droplets for photoacoustic imaging and cancer therapy has been demonstrated.
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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".