Enhanced performance and reprocessability in polypropylene-lignin blends through plasma treatment
Bibliographic record
Abstract
In this work, a new compatibilization strategy for polypropylene-lignin blends was presented, which did not rely on the use of solvents or other chemicals. Soda lignin was subjected to plasma treatment in an argon atmosphere employing a gliding-arc-tornado reactor configuration. The effect of this process was evaluated using electron paramagnetic resonance spectroscopy, nuclear magnetic resonance spectroscopy, differential scanning calorimetry, and thermogravimetric analysis, evidencing significant chemical-structural modifications in lignin, including an increased concentration of phenoxy radicals (60%) and depletion of hydroxyl functionalities (35%). Polypropylene-lignin blends incorporating 5% ( w / w ), 10% ( w / w ), and 20% ( w / w ) of either pristine or plasma-treated lignin were then prepared by melt-blending in a twin-screw extruder, and their thermo-mechanical and rheological properties were investigated in detail. As a result of the plasma-induced modifications occurred in lignin, blends incorporating the plasma-treated material exhibited greater thermo-oxidative stability, more favorable viscoelastic response, significantly improved mechanical performance (137% and 294% strain at break for polypropylene (PP) containing 5% ( w / w ) and 10% ( w / w ) of treated lignin, respectively), and enhanced thermo-mechanical reprocessability (> 95% retention of yield strength and strain at break after re-extrusion). This work provided the first demonstration of the effectiveness of plasma treatment as a viable and sustainable strategy to improve filler-matrix interactions in polypropylene-lignin blends without the use of solvents, chemical compatibilizers or additional wet-chemistry steps, paving the way for the development of lignin-based thermoplastic polyolefin materials with enhanced thermo-mechanical characteristics and improved reprocessability.
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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 | 0.000 |
| 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".