Carbonized Lignin as Sustainable Filler in Biobased Poly(trimethylene terephthalate) Polymer for Injection Molding Applications
Bibliographic record
Abstract
Abstract Light weight and sustainability are the key drivers in the development of novel biobased thermoplastic compounds for automotive applications. This paper reports the engineering properties of thermoplastic compound consisting of a novel bioresourced carbon filler in combination with partially biobased poly(trimethylene terephthalate). The bioresourced carbon filler, which was derived from lignin residue of cellulosic ethanol production, has a clear advantage in terms of density compared to glass fiber and other minerals, and shows potential for weight reduction with 7% lower density at 20% filler content. Polymer processing conditions were optimized in terms of thermomechanical properties, and use of a reactive chain extender additive was studied for improving the performance of the compound. At the optimized conditions, good dimensional stability, 89% increase in heat deflection temperature, 60% increase in flexural modulus, and 14% increase in flexural strength was attained in comparison to neat PTT polymer. Theoretical modeling based on a rule-of-mixture approach showed good agreement of the predicted and experimental modulus of the studied composites. When compared to existing mineral filled engineering polyester resin, many properties of the prepared compounds were on a comparable or favorable level, indicating good potential of the bioresourced carbon filler for light weighting and highly sustainable engineering applications.
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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".