Photo‐stabilization mechanisms of High‐Density Polyethylene (<scp>HDPE)</scp> by a commercial few‐layer graphene
Bibliographic record
Abstract
Abstract This work studied few‐layer graphene (FLG) dominant action mechanisms as a photo‐stabilizer. High‐density polyethylene (HDPE) containing 0–0.5 wt% FLG were exposed to UVA radiation in a QUV chamber, according to ASTM G154 for different exposure times, ranging from 0 to 672 h. The chemical, rheological, and mechanical properties were tracked using attenuated total reflection‐Fourier transform infrared (ATR‐FTIR), rheological measurements, and tensile tests. The experimental results showed that the addition of only 0.25 wt% FLG fully stabilized 1‐ to 3‐mm thick HDPE for an exposure time of 672 h. Electron paramagnetic resonance (EPR) test was performed on the UV‐exposed mixture of hydrogen peroxide (H 2 O 2 ) and the FLG aqueous suspensions (0, 0.2, 1, and 5 mg/mL), to study the FLG performance and mechanisms as a photo‐stabilizer. The results showed that FLG effectively decreased the characteristic EPR signal intensity due to both UV absorption/reflection and free radical scavenging. Fifty‐seven percent of the reduction was found to be due to UV absorption/reflection and 43% due to free radical scavenging. It is demonstrated that UV absorption/reflection and free radical scavenging are the dominant ones among the three FLG photo‐stabilizing mechanisms (UV absorption/reflection, free radical scavenging, and physical barrier to oxygen). Highlights FLG showed potential as a photo‐stabilizer for HDPE. Addition of 0.25 wt% FLG fully stabilized HDPE for 672 h of UVA exposure. FLG demonstrated UV absorption/reflection and free radical scavenging effects.
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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".