Plasmonic Nanobubbles in “Armored” Surface Nanodroplets
Bibliographic record
Abstract
Plasmonic nanobubbles are bubbles that are formed from local heat generated by noble metal nanoparticles under illumination of light at resonance. Understanding the formation and behavior of plasmonic nanobubbles is important for a broad suite of applications that rely on enhanced local heating of nanoparticles, such as in biomedical treatments and solar energy conversion. Here, we investigate formation, growth, and dissolution of plasmonic bubbles in a model system of oil nanodroplets in water. Gold nanoparticles were located at the surface of nanodroplets immobilized on a substrate. We followed temporal evolution of plasmonic nanobubbles from ∼200 nm in radius and above. Our experimental results show that there is an upper limit of the bubble size set by hosting droplet size, beyond which the nanobubbles burst. Nanobubbles grew, following the same growth exponent that was reported for a sub-millimeter bubble in contact with a bulk organic solvent. When the laser was switched off, the nanobubbles could remain for several minutes, much longer than the bubbles formed directly in water. The findings from this work may be valuable for light-driven chemical conversion in droplets or photothermal treatments involving immiscible phases.
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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 | 0.001 |
| 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".