Toughening of polylactide nanocomposites with an ethylene alkyl acrylate copolymer: Effects of the addition of nanoparticles on phase morphology and fracture mechanisms
Bibliographic record
Abstract
Melt compounding of polylactide with ductile polymers is widely proposed as an efficient alternative to overcome its inherent brittleness. An ethylene alkyl acrylate is used as toughening modifier of polylactide. A two‐phase morphology is detected by melt blending of Polylactide and Biomax Strong. Furthermore, the effect of the addition of organically modified nanoparticles, Cloisite 15A, on phase morphology is investigated. A transesterification reaction occurs between the matrix and the toughening modifier, leading to the formation of a terpolymer at the interface in the polymer blends, influencing the interfacial properties. This transesterification reaction is effectively catalyzed by the incorporation of the nanoparticles. The in situ formed terpolymer reveals a distinct glass transition, as the polylactide blocks along the terpolymer chain architecture restricted the segmental mobility of the ethylene alkyl acrylate blocks. The development of the terpolymer suppressed the decrease of the complex viscosity within the timeframe of the dynamic measurements. Multiple crazing is the major energy dissipation mechanism in the deformation of hybrids. The formation of an interconnected morphology in the hybrids triggers a remarkable toughening of polylactide, through the enhanced contribution of matrix plastic deformation. POLYM. ENG. SCI., 56:1415–1424, 2016. © 2016 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".