Numerical investigation of heterogeneous nucleation of water vapour on PM<sub>10</sub> for particulate abatement
Bibliographic record
Abstract
A heterogeneous nucleation model with inclusions of the line tension effect, the particle roughness effect, and the surface diffusion mechanism was presented. Effects of the particle roughness and the wetting agent on the heterogeneous nucleation behaviour were examined. The scaled nucleation barrier was analyzed and subsequently implications of the nucleation behaviour in the particulate abatement by vapour condensation were discussed. It was found that the effect of particle roughness on the nucleation behaviour was greatly affected by the line tension. There existed an optimal concentration of wetting agent at which the lowest nucleation barrier and critical saturation ratio could be obtained. The surface diffusion mechanism played an overwhelmingly important role in governing the embryo growth for hydrophilic particles with a diameter Dp > 0.1 µm and an embryo size smaller than the critical size, otherwise the contribution of direct vapour deposition mechanism could be significant. Based on the scaled nucleation barrier, three distinct nucleation regimes, i.e. athermal heterogeneous nucleation, thermal heterogeneous‐dominant nucleation, and homogeneous‐dominant nucleation, have been identified. When the contact angle was large, the wetting agent might need to be added to reduce the contact angle so as to reach the athermal heterogeneous and thermal heterogeneous‐dominant nucleation regimes, thus achieving efficient particulate abatement at low cost. The prediction results matched reasonably with the experimental data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".