Microplastic particles contain ice nucleation sites that can be deactivated by atmospheric aging
Bibliographic record
Abstract
Recent research has shown that microplastics are widespread in the atmosphere. However, we know little about their ability to nucleate ice and impact cloud glaciation. Ice nucleation by microplastics could also limit their long-range transport and global distributions. The present study explores the heterogeneous ice nucleation activity of seven microplastic samples in the immersion-freezing mode. Two polypropylene and one polyethylene terephthalate sample froze heterogeneously with median freezing temperatures of -20.9°C, -23.2°C and -21.9°C, respectively. The number of ice nucleation sites per surface area, n_s (T), ranged from 10-1 to 104 cm-2 in a temperature interval of -15 to -25°C, which is comparable to that of volcanic ash and fungal spores. After exposure to ozone or a combination of UV light and ozone, simulating atmospheric aging, the ice nucleation activity decreased in some cases and remained unchanged in others. An initial assessment using microplastic concentrations reported in the literature and our freezing data suggest that microplastics may influence cloud glaciation in some situations, but more studies are needed to confirm this initial assessment. Ice nucleation by microplastics may also impact their long-range transport and global distribution of microplastics based on a comparison of our freezing results and those of fungal spores.
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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".