Relation between the size of fog droplets and their contact angles with CR39 surfaces
Bibliographic record
Abstract
The formation of fog on CR39 surfaces has been studied. Water droplets form fog coalesce with time, especially during the first 20 s at the beginning of the formation of fog. Consequently, their mean diameter increases. Formation of fog being related to the wettability of the surfaces, the latter has been increased by the implantation of Ar ions into CR39 surfaces under an oxygen partial pressure. A very wetting CR39 surface with advancing (ACA) and receding (RCA) contact angles below 5° has been obtained with an implantation dose of 1.28 × 10 17 Ar + cm −2 . In this condition, no formation of fog was observed. Characterization using x-ray photoelectron spectroscopy has shown that the molecular structure of CR39 is strongly modified by Ar + implantation, which would be responsible for the increase in wettability. Unfortunately, both ACA and RCA increase with time, which is called ageing, and the formation of fog is again observed. The diameter and concentration of water droplets forming fog have been plotted against the contact angle. These plots show that no formation of fog occurs for ACA < ∼40°. Usually, the ACA reaches 40° after an ageing time of ∼200 h. A He pre-implantation with a dose of 5 × 10 15 He + cm −2 and an energy of 2 keV, sufficient to push the ions deeper than the Ar depth profile, delays the ageing effect in such a way that ACA ≅ 40° is reached after ∼2000 h and no formation of fog is observed during these first ∼2000 h.
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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 | 0.000 |
| 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".