Numerical Investigation of Nucleating-Agent-Enhanced Heterogeneous Nucleation
Bibliographic record
Abstract
Nucleating agents have long been employed in polymeric foaming processes to promote cell nucleation, increase cell density, and improve cell uniformity. This improvement in foam morphology is usually considered to result from the enhanced heterogeneous nucleation caused by the lower free energy barrier for cell nucleation. However, less is known about the underlying mechanisms of nucleating-agent-enhanced nucleation. In the polymer foaming process, pressure is a critical parameter that affects the degree of supersaturation of gas within a polymer−gas solution. In most previous theoretical studies on cell nucleation, a uniform pressure was assumed throughout the solution. Although this assumption may be acceptable when no particles have been added, its validity is questionable when nucleating agents are present. It has been speculated that growing cells that have already been nucleated generate local flow fields that induce tensile stresses around nearby particles, resulting in local pressure fluctuations. The discontinuity at the interface between a nucleating agent particle and the surrounding polymer melt yields local pressure and stress fields around the particle that are different from those in the bulk, which may enhance it as a potential heterogeneous nucleation site. This paper presents a numerical analysis to investigate the pressure profile in the vicinity of nucleating agents and provides new information about the underlying mechanism that promotes cell nucleation in the presence of nucleating agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".