Circular poly(ethylene terephthalate) with lignin-based toughening additives
Bibliographic record
Abstract
Creating sustainable plastics demands an efficient strategy to reduce the carbon footprint and enhance the circularity of widely used materials. Inspired by the structure of plant cell walls, renewable lignin macromolecules are modified with benzoate ethyl functional groups and combined with semi-crystalline poly(ethylene terephthalate) (PET) at a 10 % weight ratio. This process significantly improves the toughness (+97 %) and strength (+ 56 %) of PET while also reducing greenhouse gas emissions (−17 %) and promoting circularity, outperforming traditional toughening agents. Our in-depth analysis indicates that benzoate ethyl lignin derivatives exhibit improved thermal stability and controllable physical structure. The newly added benzoate ethyl groups are similar to the fundamental units in PET, facilitating the formation of micro-scale particles within the PET matrix and improving their crystallinity and mechanical performance. The resulting composite can be reprocessed at least three times, representing a significant breakthrough in mechanical processing of thermoplastics. Therefore, this study presents a promising approach to utilizing lignin biopolymer and waste PET for advanced materials with positive environmental footprints.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".