Hybrid biocomposites from polypropylene, sustainable biocarbon and graphene nanoplatelets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it