Bibliographic record
Abstract
Abstract In this paper, the crystallization of PLA in PLA/thermoplastic starch (TPS) blends was investigated. The TPS was used as the minor component at a concentration varying between 0 and 42 %. The effect of interfacial area was studied by compatibilizing the blend using maleic anhydride grafted PLA. To further enhance the crystallization rate in PLA/TPS blends, the addition of talc and acetyl triethyl citrate, as nucleating agent and plasticizer respectively, was also studied. The crystallization rate was monitored using DSC analysis under both isothermal and non- isothermal conditions. The isothermal data was used to determine the crystallization half-life and to determine the optimum crystallization temperature. The crystallization half-time of PLA was decreased significantly in presence of the TPS phase and this effect was strengthened by the blends interfacial modification. By applying proper plasticization and nucleation strategy, crystallinity in excess of 50 % was achieved even at cooling rates as high as 80°C/min. Fully crystallized PLA/TPS samples were obtained by injection molding in a hot mold (80°C). Crystallized parts showed improved thermal resistance at temperatures above PLA's glass transition.
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".