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Record W4392758509 · doi:10.5194/egusphere-egu24-13967

Heterogeneous Ice Nucleation of Microplastics before and after Oxidation

2024· preprint· en· W4392758509 on OpenAlexaff
Teresa M. Seifried, Sepehr Nikkho, Aurelio Morales Murillo, Lucas J. Andrew, Edward R. Grant, Allan K. Bertram

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMicroplasticsNucleationIce nucleusEnvironmental scienceEnvironmental chemistryOceanographyChemistryGeology

Abstract

fetched live from OpenAlex

Many recent studies point to the environmental threat posed by microplastic pollution, both in waterways and as transmitted globally in the atmosphere.1,2 Airborne microplastics impact the climate by the direct absorption and scattering of radiation3 and may act indirectly to influence cloud formation and precipitation by means of heterogeneous ice nucleation.4 But, the true efficiency of microplastics as ice-nucleating particles and its implications for cloud formation remain largely unknown.Here, we present evidence for ice nucleation in immersion freezing mode induced by various microplastics suspended in water. This study focuses on seven distinct microplastic morphologies in substances composed of polypropylene (PP), polyethylene (PE) and polyethylene terephthalate (PET). For each polymer type, we analyzed at least one commercially-available microplastic sample and one generated from the breakdown of a commonly used commercial product. PP needles, PP fibers and PET fibers nucleated ice at temperatures relevant for mixed-phase cloud formation, with T50 values of -20.88 °C ± 0.52, -23.24°C ± 0.21 and -21.93°C ± 0.51, respectively. The number of ice nucleation sites per surface area (ns(T)) ranged from 10-1 to 104 cm-2 in a temperature interval of -15 to -25°C. In addition, we conducted oxidation experiments, exposing the samples to ozone and UV light, resulting in a decrease of nucleation temperatures among the ice-active microplastics. The presented data holds significant potential for integration into climate models, facilitating estimations of their impact on cloud formation. (1) Dris, R.; Gasperi, J.; Rocher, V.; Saad, M.; Renault, N.; Tassin, B. Microplastic Contamination in an Urban Area: A Case Study in Greater Paris. Environ. Chem. 2015, 12 (5), 592–599. https://doi.org/10.1071/EN14167.(2) Allen, S.; Allen, D.; Baladima, F.; Phoenix, V. R.; Thomas, J. L.; Le Roux, G.; Sonke, J. E. Evidence of Free Tropospheric and Long-Range Transport of Microplastic at Pic Du Midi Observatory. Nat Commun 2021, 12 (1), 7242. https://doi.org/10.1038/s41467-021-27454-7.(3) Revell, L. E.; Kuma, P.; Le Ru, E. C.; Somerville, W. R. C.; Gaw, S. Direct Radiative Effects of Airborne Microplastics. Nature 2021, 598 (7881), 462–467. https://doi.org/10.1038/s41586-021-03864-x.(4) Ganguly, M.; Ariya, P. A. Ice Nucleation of Model Nanoplastics and Microplastics: A Novel Synthetic Protocol and the Influence of Particle Capping at Diverse Atmospheric Environments. ACS Earth Space Chem. 2019, 3 (9), 1729–1739. https://doi.org/10.1021/acsearthspacechem.9b00132.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.223
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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