Analysis of ultraviolet and thermal degradations of four common microplastics and evidence of nanoparticle release
Bibliographic record
Abstract
Many environmental factors affect the breakdown of plastics in aquatic environments, including exposure to ultraviolet (UV) irradiation and elevated environmental temperatures.More studies are needed to understand how these stressors contribute to plastic degradation, resulting in the release of smaller plastic particles.We studied the impact of environmentally relevant UV and temperature (37 • C) weathering of four high-production volume plastics (polystyrene, polypropylene, low-density polyethylene, and high-density polyethylene) suspended in water.Particle release was detected, characterized by scanning electron microscopy (SEM), and nanoparticles were quantified by nanoparticle tracking analysis (NTA).Weathering resulted in the release of micro-and nanoparticles that exhibited a plastic signature corresponding to the parent microplastic.Nanoparticle release is broadly correlated with an increase in the carbonyl index of the parent microplastic.Aged microplastics were characterized for physical and chemical changes.The impact of weathering on microplastic surface hardness and polymer oxidation depended on material type and environmental factors.Few to no particles were observed in controls, including controls that contained microplastics at 4 • C in dark conditions, highlighting the importance of weathering stimuli in particle release.These results show that plastic degradation needs to consider both the parent microplastic and the smaller particles that are formed.
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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 | 0.001 | 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".