Effect of Mechanical Recycling on the Crystallization of PA 11 and PA 11 LDPE Blends
Bibliographic record
Abstract
Abstract This study investigates the effect of mechanical recycling on the thermal crystallization of virgin polyamide (PA 11) and a post‐consumer PA 11 – low‐density polyethylene (LDPE) blend (90/10) over ten reprocessing cycles. Isothermal, non‐isothermal, and successive self‐nucleation and annealing (SSA) methods are used. Isothermal analysis revealed accelerated crystallization kinetics with increasing reprocessing cycles, as shown by an increase in the inverse of the half‐crystallization time ( 1/τ 1/2exp ) and a decrease in the crystallization energy barrier ( K g ), likely due to enhanced chain mobility and molecular weight reduction from thermal degradation. SSA analysis revealed differences in lamellar structures. After three cycles, virgin PA 11 presented a shoulder in the SSA profile, indicating the formation of thinner lamellae. In contrast, post‐consumer PA 11 showed a progressive increase in its main melting peak, suggesting the development of thicker lamellae with a more uniform molecular population. Thermogravimetric analysis showed reduced thermal stability, as indicated by lower activation energy ( E a ). Despite these changes, their effect is not significant to limit reprocessing, confirming their recyclability for at least ten cycles. To further assess their long‐term viability; structural, rheological, and mechanical properties will be presented in a subsequent study.
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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".