Lignin as a sustainable filler of polyvinyl chloride composites: Effects of ash content and loading levels on thermomechanical and combustion properties
Bibliographic record
Abstract
Lignin is the second most abundant biopolymer and has generally been discarded as waste, but its availability, renewability, and unique properties have garnered popularity for its use in material applications. Its hydrophobic nature makes it a natural contender as a filler for polyvinyl chloride (PVC), a versatile polymer that is typically modified with a variety of toxic additives that can leach during thermal decomposition, whether through recycling or combustion. In this work, the applicability of Kraft lignin as a filler to PVC is explored, utilizing different grades of Kraft lignin from various purification methods and varying the loading of filler within PVC. The grade of lignin was a major aspect of the resulting mechanical properties due to the ash content, which decreased the elongation by up to 73 % in certain cases, but increased Young's modulus overall. Increasing concentrations of lignin amplified certain properties seen, with an optimal value at a loading of 18 wt%. Here, we see the most retained heat capacity (-5.14 %) and an increase to Young's modulus (+8.76 %). This loading slightly reduced the limiting oxygen index (LOI) (-9.60 %), but improved combustion indices by 50 to 80 %, through its charring behaviour. The industrially purified lignin maintained the largest elongation (-38.7 %) with similar combustion improvements, but lowered the heat capacity more (-21.3 %) compared to the laboratory purified lignin. Kinetic analysis showed a slight increase to the activation energy of dehydrochlorination and a change to the primary mechanism, shifting from nucleating to diffusion controlled.
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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) | 0.001 | 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".