The Influence of a Commercial Few-Layer Graphene on the Photodegradation Resistance of a Waste Polyolefins Stream and Prime Polyolefin Blends
Bibliographic record
Abstract
This work investigated the photostabilizing role of a commercially available few-layer graphene (FLG) in mixed polyolefins waste stream (MPWS), ensuring extended lifespan for outdoor applications. The investigation was conducted by analyzing carbonyl content increase, surface appearance, and the retention of mechanical properties of UV-exposed MPWS/FLG composites. Despite the likely predegraded condition of MPWS, approximately 60%, 70%, 80%, and 90% of the original ductility was retained in composites containing 1, 4, 7, and 10 wt.% FLG, respectively. Conversely, just 20% of the original ductility was retained in unfilled MPWS. Additionally, less crack density and lower carbonyl concentrations of the composites also highlighted the photoprotection effect of FLG. For prime polyolefin blends, only 0.5 wt.% or 1 wt.% FLG was sufficient to preserve the original surface finishing and protect the mechanical properties from photodegradation. Hence, it was observed that MPWS requires more FLG than prime polyolefin blends to get to comparable property retention. This could be attributed to the poor dispersion of FLG in MPWS and inevitable uncertainties such as the presence of impurities, pre-degradation, and polydispersity associated with MPWS. This study outlines a potential approach to revalorize MPWS that possess a minimal intrinsic value and would otherwise be destined for landfill disposal.
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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".