Effects of Nucleating Agents’ Shapes and Interfacial Properties on Cell Nucleation
Bibliographic record
Abstract
The classical nucleation theory (CNT) provides the fundamentals to understand the cell nucleation phenomena theoretically during polymeric foaming processing. Since a variety of additives and impurities are ubiquitous in commercial plastic resins and crevices are omnipresent on the internal walls of processing equipment, heterogeneous nucleation plays a significant role in plastic foaming. According to the CNT, interfacial tensions at different interfaces and surface geometries at various heterogeneous nucleating sites are important factors that govern the heterogeneous nucleation rate. Therefore, the elucidation of their roles in cell nucleation will be beneficial to the foaming industry, which can use the information to optimize its foaming technologies and develop effective nucleating agents. In this study, a series of sensitivity analyses were performed; the results indicated that the contact angle ( θ c ) at the gas—liquid—solid junction and the semi-conical angle ( β) at the nucleating site are critical parameters that govern the free energy barrier to initiate heterogeneous nucleation ( W het ). Moreover, regardless of the surface geometries of the nucleating agents, W het decreases as θ c increases. For moderate values of θ c (e.g., θ c = 90°), heterogeneous nucleation is promoted in conical cavities with a smaller β. However, a smaller β is unfavorable for cell nucleation when θ c is too small.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".