Blends of polylactic acid with thermoplastic copolyester elastomer: Effect of functionalized terpolymer type on reactive toughening
Bibliographic record
Abstract
This study is an attempt to explore the effectiveness of thermoplastic copolyester elastomer (TPCE) as a toughening agent for improving the impact strength of PLA. Biobased Hytrel® thermoplastic copolyester of polyether glycol and polybutylene terephthalate was selected as the TPCE of choice for this study. Blends of PLA/Hytrel at varying weight ratios were prepared using extrusion followed by injection molding technique. Optimal synergies of two polymers were found in the PLA/Hytrel (70/30) blend, showing impact strength of 234 J/m, a sixfold increase compared to neat PLA. In order to obtain further enhancement in toughness, different functionalized terpolymers were added to accomplish reactive compatibilization. A series of functionalized terpolymers, ethylene methyle acrylate‐glycidyl methacrylate (EMA‐GMA), ethylene butyl acrylate‐glycidyl methacrylate (EBA‐GMA), ethylene methyl acrylate‐maleic anhydride (EMA‐MaH), and ethylene butyl acrylate‐maleic anhydride (EBA‐MaH) were selected. Comparing PLA ternary blends with different terpolymers, GMA containing terpolymers showed better impact toughness compared to MaH terpolymer blends. Unique fracture surface morphology showing debonding cavitation and massive shear yielding in the ternary blends containing EMA‐GMA resulted in super toughened blends. Highest zero shear viscosity and storage modulus was also observed for ternary blends with EMA‐GMA. Under the processing conditions and blend ratio investigated, EMA‐GMA showed better efficiency in improving the toughness of the PLA blends. POLYM. ENG. SCI., 58:280–290, 2018. © 2017 Society of Plastics Engineers
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) | 0.000 | 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".