Effect of Few‐Layer Graphene ( <scp>FLG</scp> ) and Relative Photodegradation Depth on the Ductile to Fragile Transition of High‐Density Polyethylene
Bibliographic record
Abstract
ABSTRACT Photo‐stabilization is essential for thermoplastics in outdoor applications, as it extends service life by protecting polymer chains from UV‐induced degradation. Graphene has emerged as a multifunctional stabilizer with capabilities including UV screening, barrier effects, and radical scavenging. However, its influence on the depth profile of photodegradation under UV exposure is not fully understood. This work investigates the effect of few‐layer graphene (FLG) on the photodegradation of high‐density polyethylene (HDPE). Neat HDPE and composites with 0.5 wt% FLG were prepared in two thicknesses (3 and 2 mm) and exposed to UV radiation for varying durations. Elongation at break was measured as a function of exposure time and correlated to the degradation depth, determined by chemi‐crystallization using Raman microscopy. In neat HDPE, embrittlement occurred when the degraded layer reached ~10% of the thickness, after 10 and 7 days for 3‐ and 2‐mm samples, respectively, preceding the appearance of surface cracks. In contrast, HDPE with 0.5 wt% FLG retained 50% of its initial elongation at break, characterizing a ductile failure, even after 45 days despite surface cracks. Additionally, embrittlement was only observed when the relative degradation depth reached 12.5%, exceeding the 10% threshold observed for the neat HDPE. The persistence of ductility is attributed to the photo‐stabilizing effect of FLG and the detachment of the degraded surface from the ductile core, leading to a material with enhanced UV resistance for outdoor packaging and coating applications.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".