A Review on Graphene’s Light Stabilizing Effects for Reduced Photodegradation of Polymers
Bibliographic record
Abstract
Graphene, the newest member of the carbon’s family, has proven its efficiency in improving polymers’ resistance against photodegradation, even at low loadings equal to 1 wt% or lower. This protective role involves a multitude of complementary mechanisms associated with graphene’s unique geometry and chemistry. In this review, these mechanisms, taking place during both the initiation and propagation steps of photodegradation, are discussed concerning graphene and graphene derivatives, i.e., graphene oxide (GO) and reduced graphene oxide (rGO). In particular, graphene displays important UV absorption, free radical scavenging, and quenching capabilities thanks to the abundant π-bonds and sp2 carbon sites in its hexagonal lattice structure. The free radical scavenging effect is also partially linked with functional hydroxyl groups on the surface. However, the sp2 sites remain the predominant player, which makes graphene’s antioxidant effect potentially stronger than rGO and GO. Besides, UV screening and oxygen barriers are active protective mechanisms attributed to graphene’s high surface area and 2D geometry. Moreover, the way that graphene, as a nucleating agent, can improve the photostability of polymers, have been explored as well. These include the potential effect of graphene on increasing polymer’s glass transition temperature and crystallinity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".