Green Toughness Modifier from Downstream Corn Oil in Improving Poly(lactic acid) Performance
Bibliographic record
Abstract
A green multifunctional toughness modifier for poly(lactic acid) (PLA) was successfully synthesized from the derivatives of downstream corn oil (a coproduct of bioethanol industry) and itaconic acid. The efficiency of the synthesized toughness modifier, monomethyl itaconated epoxidized downstream corn oil (MIECO), was evaluated with a different loading percentage of it (5–15 wt %) in the PLA matrix. A dynamic cross-linking strategy was taken to achieve toughened PLA by using multifunctional MIECO in the presence of a radical initiator. During melt blending, the multifunctional MIECO self-polymerized and reacted with functional groups of PLA to produce a rubbery part within the PLA matrix and compatibilized the blend. The fabricated PLA–MIECO blends demonstrated a remarkably enhanced elongation at break (18 times), tensile toughness (11 times), and notched Izod impact (131%) compared to those of pristine PLA. The scanning electron microscopic (SEM) images confirmed that cavities were generated by debonding of polymerized MIECO from PLA matrix, which provides a plastic deformation and excellent toughness in the PLA–MIECO blends. The synthesized multifunctional toughness modifier and the strategy will pave a way to other biopolymers for expanding their performances and applicability.
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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 | 0.000 |
| 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".