The effect of pressure and clay on the crystallization behavior and kinetics of polyamide‐6 in nanocomposites
Bibliographic record
Abstract
Abstract The crystallization kinetics of polyamide‐6 (PA‐6) and its nanocomposite (PNC) with 2% clay were studied, using a pressure dilatometer (50 MPa to 200 MPa) to follow the volume changes associated with the crystallization process. Isobaric experiments were carried out to evaluate the effect of pressure and clay on melting temperature (Tm) and crystallization temperature (Ta) of PA‐6. The melting temperatures of PA‐6 in the PNC were very close to those of PA‐6 alone at comparable pressures, but the crystallization temperatures in the PNC were lower than those of PA‐6 alone. The materials exhibited two crystallization zones in isothermal/isobaric experiments. The initial zone involved both the γ‐form and the α‐form of PA‐6, while in the latter zone the γ‐form was dominant. The Avrami equation was used to fit the isothermal/isobaric crystallization data. The Avrami exponent n was between 1.0 and 3.2 for the γ‐form of unfilled PA‐6, between 0.9 and 2.6 for the γ‐form in PNC and for the γ‐form of PA‐6 alone, n was between 1.0 and 2.1 and in PNC between 1.2 and 2.6. The Avrami rate constants (K) for PA‐6 and PNC depend on the experimental crystallization temperature as well as pressure. The rate of crystallization under similar conditions was higher for PNC. Infrared studies on compression molded PA‐6 and PNC samples, cooled from melt at different rates, confirm the formation of the γ‐form in the initial stages of crystallization, as well as its transformation into the α‐form at later stages. In the case of PNC, the γ‐form stabilized when the sample was quenched from melt.
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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".