Enhanced properties of polylactide/polyamide 11 blends by reactive compatibilization
Bibliographic record
Abstract
Abstract Polylactide (PLA)/polyamide 11 (PA11) blends with 75/25 wt.% composition were prepared using an internal mixer. An epoxy‐based chain extender, Joncryl® ADR 4468, was used for reactive compatibilization of the blends. The effect of the mixing strategy of the chain extender with the blend components on its compatibilization effect and on the morphological, rheological, thermomechanical, and mechanical properties of the blends was determined. Due to more reactivity of the chain extender towards PLA compared to PA11, preparing the blends using the epoxy‐modified PA11 resulted in enhanced compatibilization and a reduction of the number‐average diameter of the dispersed droplets from 1.89 to 0.64 μm in the compatibilized blends. Larger complex viscosity and storage modulus were observed for blends prepared using the modified PA11 compared to blends from the modified PLA, consistent with the observed refinement of the morphology. Strain at break of the compatibilized blends increased to 150% compared to 6% for the neat PLA while maintaining the tensile strength and modulus at 62 MPa and 2 GPa (70 MPa and 2.4 GPa for PLA), respectively. The impact toughness increased from 24 J/m for neat PLA to around 40 J/m in compatibilized blends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".