Giant and explosive plasmonic bubbles by delayed nucleation
Bibliographic record
Abstract
When illuminated by a laser, nano particles immersed in water can very quickly and strongly heat up, leading to the nucleation of so called vapor bubbles, which have huge application potential in e.g. solar light-harvesting, catalysis, and for medical applications.Whilst the long-time behavior of such bubbles has been well-studied, here, by employing ultra-high-speed imaging, we reveal the nucleation and early life phase of these bubbles. After some delay time after beginning of the illumination, a giant bubble explosively grows, up to a maximal radius of 80 um, and collapses again within 200 us (bubble life phase 1). The maximal bubble volume remarkably increases with decreasing laser power P. To explain this behavior, we measure the delay time from the beginning of the illumination up to nucleation,which drastically increases with decreasing laser power, leading to less total dumped energy . This dumped energy E shows a universal linear scaling relation. This finding supports that the initial giant bubble is a pure vapor bubble. In contrast, the delay time does depend on the gas concentration of the water, as gas pockets in the water facilitate an earlier vapor bubble nucleation, which leads to smaller delay times and lower bubble nucleation temperatures. After the collapse of the initial giant bubbles, first much smaller oscillating bubbles form out of the remaining gas nuclei (bubble life phase 2, up to typically 10 ms). Subsequently the known vaporization dominated growth phase takes over and the bubble stabilizes (life phase 3). In the final life phase 4 the bubble slowly grows by gas expelling due to heating of the surrounding. Our findings on the explosive growth and collapse during the early life phase of a vapor bubble have strong bearings on possible applications of such bubbles, affecting their risk assessment.
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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 | 0.001 |
| 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".