Hybrid biocomposites from polypropylene, sustainable biocarbon and graphene nanoplatelets
Bibliographic record
Abstract
Polypropylene (PP) is an attractive polymer for use in automotive parts due to its ease of processing, hydrophobic nature, chemical resistance and low density. The global shift towards eliminating non-renewable resource consumption has promoted research of sustainable biocarbon (BioC) filler in a PP matrix, but this material often leads to reduction in composite strength and requires additional fillers. Graphene nano-platelets (GnPs) have been the subject of considerable research as a nanofiller due to their strength, while maleic anhydride grafted polypropylene (MA-g-PP) is a commonly used compatibilizer for improvement of interfacial adhesion in composites. This study compared the thermo-mechanical properties of PP/BioC/MA-g-PP/GnP composites with varying wt.% of GnP. Morphological analysis revealed uniform dispersion of BioC, while significant agglomeration of GnPs limited their even dispersion throughout the PP matrix. In the optimal blend of 3 wt.% GnP and 17 wt.% BioC biocontent, tensile strength and modulus increased by ~19% and ~22% respectively, as compared to 20 wt.% BioC biocomposites. Thermal stability and performance enhancement occurred through incorporation of the fillers. Thus, hybridization of fillers in the compatibilized matrix presents a promising route to the enhancement of material properties, while reducing petroleum-based products through use of sustainable BioC filler in composite structures.
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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".