Graphene Oxide as an Auxiliary <scp>UV</scp> Photoprotector for Polypropylene
Bibliographic record
Abstract
ABSTRACT This work presents the preparation and characterization of polypropylene (PP)/graphene oxide (GO) nanocomposites by adding photostabilizing additives to obtain materials with enhanced resistance to photodegradation. The synthesized GO (modified Hummers' method) was characterized using state‐of‐the‐art techniques. The results indicated that a high oxidation index and a relatively low stacking of GO layers were achieved. The PP/GO nanocomposites and reference PP composition (without GO) with different additives (antioxidants, UV absorber, and hindered amine light stabilizer—HALS) were prepared by compression molding. The films were subjected to UV light in an artificial aging chamber for up to 4 weeks and were characterized through visual aspect analysis, FTIR, and Differential Scanning Calorimetry (DSC). The results showed that HALS provided the best protective effect against photodegradation among all analyzed compositions, while the addition of GO also retarded the photodegradation process of PP. Conversely, the UV absorber alone was ineffective in protecting against photodegradation. However, a synergistic effect among HALS, UV absorber, and GO was observed as the best alternative to hinder the PP photodegradation reactions.
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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".