Polyethylene crosslinking using the epoxy‐anhydride reaction II: Development of a chemorheological model
Bibliographic record
Abstract
Abstract We have developed a kinetic model attempting to describe the rheological changes observed during crosslinking of blends of epoxide‐ and anhydride‐functionalized polyethylene using the activatable imidazolium catalyst EMIC. The model incorporated first‐order formation of an active initiator from a latent state followed by initiation of the crosslinking reaction and continued propagation by an active crosslinking end group. Because the formation of a crosslink does not consume an active end group, the post‐initiation crosslinking reaction assumes a first‐order dependence in crosslinkable functionality. Application of the model to data generated from rheological studies exploring the effects of initiator and anhydride loading was met with moderate success. While curvature in the rheological plots modeled very well, capturing the active initiator formation and early cure, some data sets did not model well at higher degrees of cure. Extraction of modeled parameters , and from fits showed that the initiation rate constant was linearly dependent on the anhydride loading. We believe that this indicates the presence of an independent initiation pathway that destroys latency in the system. Additionally, it was found that the modeled value for the plateau modulus, showed a remarkably linear dependence on initiator loading. This relationship indicates that the ultimate degree of crosslinking in a system is determined by the sequence length of the crosslinking reaction and how many crosslinking cascades initiate based on the initiator loading.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".