Circular poly(ethylene terephthalate) with lignin-based toughening additives
Bibliographic record
Abstract
Preparation of benzoate ethyl lignin derivatives and their application for upcycling PET. • An environmentally friendly method to convert technical lignin toward versatile and uniform building blocks. • These benzoate ethyl groups (BELignin) were thoroughly characterized, showing enhanced uniformity and thermal stability. • Blending BEignin with PET can attain toughness (+91 %) and strength (+ 56 %), simultaneously. • Renewable lignin toughening additives can reduce PET greenhouse gas emissions by 17% and prompt its circularity. 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 wall, 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 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 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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".