Injection Molded Novel Biocomposites from Polypropylene and Sustainable Biocarbon
Bibliographic record
Abstract
Achieving sustainability in composite materials for high-performance applications is a key issue in modern processing technologies. In this work, the structure-property relationships of injection molded polypropylene (PP)/biocarbon composites were investigated with a focus on the thermal properties and specific emphasis on the coefficient of linear thermal expansion (CLTE). Biocomposites were produced using 30 wt.% biocarbon in a PP matrix, and two different sources of biocarbon produced at ~650 and 900 °C were used. The overall results were compared with 30 wt.% glass- and talc-filled PP composites. Due to the lamellar morphology of the talc developed during the extrusion-injection molding processing, talc-filled composites showed an increase in the CLTE in the normal direction (ND), and a reduction in the flow direction (FD) with respect to the neat polymer. Glass fiber composites also showed an improvement in the CLTE with respect to the neat polymer. However, the biocarbon-based composites showed the best properties in the ND, with improved values in biocarbon produced at higher temperature. The FD values for both biocarbon composites were improved with respect to the matrix, while biocarbon created at lower temperature showed slightly lower expansion values. A comprehensive explanation of these overall phenomena is supported by a series of morphological, thermal, mechanical and rheological tests.
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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".