How persistent microbubbles shield nanoparticle productivity in laser synthesis of colloids – quantification of their volume, dwell dynamics, and gas composition
Bibliographic record
Abstract
During laser synthesis of colloids, cavitation bubbles with lifetimes in the microsecond-scale form and shield the laser pulse leading to a decrease in nanoparticle output. A second type of productivity-limiting bubble that severely affects the productivity of the process is often neglected. With lifetimes from milliseconds to seconds, these persistent bubbles are systematically studied in this work by quantifying their composition, amount, size and dwell time in liquids with different viscosities and by relating the results to the nanoparticle productivities. It is found that during synthesis in water, water splitting occurs leading to persistent bubbles consisting of hydrogen and oxygen. In glycols, hydrogen and molecular carbon species containing microbubbles are formed. These persistent microbubbles shield up to 65% of the incoming laser beam depending on the liquid as well as the laser fluence and require attention by means of reducing their dwell time in the ablation zone and enhancing the nanoparticle output by liquid flow. The highest productivities and monodisperse quality are achieved in liquids with the lowest viscosities.
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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 | 0.001 |
| 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".