Exploring the Influence of Temperature on the Generation and Stability of Bulk Nanobubbles
Bibliographic record
Abstract
Nanobubbles are stable, gas-filled bubbles on the nanoscale that challenge traditional models of bubble dissolution.Despite predictions that they should rapidly dissolve due to high internal pressure, nanobubbles have shown remarkable stability.This study investigates the formation and behaviour of nanobubbles in high-purity water subjected to temperature variation.High-purity water, filtered through sub-20 nm filters and stored at low temperatures, was gradually heated to 45°C.Dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) were employed to monitor particle size and concentration before and after the temperature increase.DLS results indicated an initial increase in particle size as the temperature rose, suggesting the nucleation of nanoparticles.A subsequent decrease in size likely reflected the coalescence and formation of larger bubbles, which impacted measurement accuracy.NTA, however, showed no significant change in particle concentration, suggesting that it was less sensitive to the lower particle concentration in this experiment.Adjustments in experimental design are recommended to reduce interference from larger bubbles in future investigations, potentially providing a reliable methodology for nanobubble generation and characterization.
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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".